Tachyarrhythmia discrimination

Abstract

An ambulatory medical system is provided. The system includes sensing electrodes and a processor coupled with the sensing electrodes. The sensing electrodes are configured to detect electrical activity from a patient's skin. The processor is configured to derive electrocardiogram (ECG) data from the electrical activity, detect an apparent tachyarrhythmia based on the ECG data, and identify an ECG segment indicating the apparent tachyarrhythmia. The ECG segment includes a first ECG interval bounded by two fiducial points within a first heartbeat and second ECG interval bounded by a first fiducial point within the first heartbeat and a second fiducial point within a second heartbeat. The processor is further configured to determine, based on the first and second ECG intervals, a discrimination metric indicative of QRS complex widening, and provide, based on the discrimination metric, an indication of whether the ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event.

Description

TACHYARRHYTHMIA DISCRIMINATIONBACKGROUND

[0001] The present disclosure is directed to medical devices configured to discriminate between tachyarrhythmias (TAs) for which electrotherapeutic treatment is advisable and TAs for which electrotherapeutic treatment is not advisable.

[0002] Heart failure, if left untreated, can lead to certain life-threatening arrhythmias. Both atrial and ventricular arrhythmias are common in patients with heart failure. One of the deadliest cardiac arrhythmias is ventricular fibrillation, which occurs when normal, regular electrical impulses are replaced by irregular and rapid impulses, causing the heart muscle to stop normal contractions. Because the victim has no perceptible warning of the impending fibrillation, death often occurs before the necessary medical assistance can arrive. Other cardiac arrhythmias can include excessively slow heart rates known as bradycardia or excessively fast heart rates known as tachycardia. Cardiac arrest can occur when a patient in which various arrhythmias of the heart, such as ventricular fibrillation (VF), ventricular tachycardia (VT), pulseless electrical activity (PEA), and asystole (heart stops all electrical activity), result in the heart providing insufficient levels of blood flow to the brain and other vital organs for the support of life. It is generally useful to monitor heart failure patients to assess heart failure symptoms early and provide interventional therapies as soon as possible.

[0003] Patients who are at risk, have been hospitalized for, or otherwise are suffering from, adverse heart conditions can be prescribed a wearable cardiac monitoring and / or treatment device. In addition to the wearable device, the patient can also be given a battery charger and a set of rechargeable batteries. As the wearable device is generally prescribed for continuous use (e.g., only to be removed when bathing), the patient wears the device during all daily activities such as walking, sitting, climbing stairs, resting or sleeping, and other similar daily activities. Maintaining continuous use of the device as prescribed can be beneficial for monitoring patient progress as well as providing treatment to the patient if needed.SUMMARY

[0004] At least one example is directed to an ambulatory medical system for discriminating ventricular tachyarrhythmias from other tachyarrhythmias. The system includes a plurality of sensing electrodes and at least one processor coupled with the plurality of sensing electrodes. The plurality of sensing electrodes are configured to contact skin of a patient externally and detect electrical activity from the skin of the patient. The electrical activity indicates cardiac function in the patient. The at least one processor is configured to derive electrocardiogram (ECG) data from the electrical activity indicating the cardiac function in the patient, detect an apparent tachyarrhythmia in the patient based on the ECG data, and identify an arrhythmic ECG segment indicating the apparent tachyarrhythmia in the patient. The arrhythmic ECG segment includes a first ECG interval bounded by two fiducial points within a first heartbeat of the patient and second ECG interval bounded by a first fiducial point within the first heartbeat and a second fiducial point within a second heartbeat of the patient. The at least one processor is further configured to determine, based on the first and second ECG intervals, a tachyarrhythmic discrimination metric indicative of QRS complex widening, and provide, based on the tachyarrhythmic discrimination metric, an indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event.

[0005] Examples of the ambulatory medical system may incorporate one or more of the following features.

[0006] In the ambulatory medical system, the ventricular tachyarrhythmic event may include a treatment-advisable cardiac rhythm event in the patient and the supraventricular tachycardiac event may include a treatment-inadvisable cardiac rhythm event in the patient. The treatment-advisable cardiac rhythm event may include a ventricular tachycardia event or a ventricular fibrillation event. The first ECG interval may span a portion of a first heartbeat of the patient and the second ECG interval may span a second heartbeat of the patient. The first heartbeat and the second heartbeat may be distinct heartbeats. The second ECG interval may overlap with the first ECG interval. The first heartbeat and the second heartbeat may be distinct heartbeats. The second ECG interval may not overlap the first ECG interval. The first heartbeat and the second heartbeat may be a same heartbeat. The first ECG interval may span two fiducial points within a QRS complex of the same heartbeat of the patient. The second ECG interval may span two fiducial points within the sameheartbeat of the patient. The first ECG interval may be an RS interval. The second ECG interval may be an RR interval.

[0007] In the ambulatory medical system, the tachyarrhythmic discrimination metric may be a comparison of the first ECG interval and the second ECG interval. The tachyarrhythmic discrimination metric may be a ratio of the first ECG interval to the second ECG interval. To provide the indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event may include to provide the indication based on a comparison between a configurable parameter and the tachyarrhythmic discrimination metric. The configurable parameter may quantify a target relationship between the first ECG interval and the second ECG interval.

[0008] In the ambulatory medical system, the tachyarrhythmic discrimination metric may be a first tachyarrhythmic discrimination metric; the at least one processor may be further configured to determine additional tachyarrhythmic discrimination metrics; and to provide the indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event may include to provide the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics. The at least one processor may be further configured to derive at least one summary statistic of the first tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics; and to provide the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics includes to provide the indication based on the at least one summary statistic. The at least one summary statistic may include one or more of mean, median, mode, quartile, percentage, variance, standard deviation, and kurtosis. To determine the additional tachyarrhythmic discrimination metrics may include to determine a first subset of the additional tachyarrhythmic discrimination metrics prior to detection of the apparent tachyarrhythmia, to determine a second subset of the additional tachyarrhythmic discrimination metrics after detection of the apparent tachyarrhythmia. To provide the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics may include to determine a first summary statistic of the first subset of the additional tachyarrhythmic discrimination metrics, to determine a second summary statistic of the first tachyarrhythmic discrimination metric and the second subset of the additional tachyarrhythmic discrimination metrics, and to compare the first summary statistic to the second summary statistic.

[0009] To determine the first subset of the additional tachyarrhythmic discrimination metrics may include to determine the first subset during a baseline measurement of the cardiac function of the patient. To determine the first subset of the additional tachyarrhythmic discrimination metrics may include to analyze first ECG data within a first window. The first window may have a length. To determine the second subset of the additional tachyarrhythmic discrimination metrics may include to analyze second ECG data within a second window having the length. The second ECG data may include the arrhythmic ECG segment.

[0010] In the ambulatory medical system, the plurality of sensing electrodes may include a sideside electrode pair and a front-back electrode pair. To identify the arrhythmic ECG segment may include to identify a first arrhythmic ECG segment using the front -back electrode pair. To determine the tachyarrhythmic discrimination metric may include to determine a first tachyarrhythmic discrimination metric. The at least one processor may be further configured to identify a second arrhythmic ECG segment using the side-side electrode pair and to determine a second tachyarrhythmic discrimination metric based third and fourth ECG intervals with the second arrhythmic ECG segment. To provide the indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event may include to provide the indication based on the first tachyarrhythmic discrimination metric and the second tachyarrhythmic discrimination metric.

[0011] In the ambulatory medical system, the at least one processor may be further configured to determine an onset time based on the arrhythmic ECG segment. To provide the indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event may include to provide the indication based on the tachyarrhythmic discrimination metric and the onset time.

[0012] In the ambulatory medical system, to determine the tachyarrhythmic discrimination metric may include to measure a slope of the ECG data. The tachyarrhythmic discrimination metric may include a metric indicative of asymmetry between a slope of a QR interval and a slope of a RS interval. The metric indicative of asymmetry may include a ratio of a first statistic based on the slope of the QR interval to a second statistic based on the slope of the RS interval. The first statistic and the second statistic may be means or medians.

[0013] At least one example is directed to a wearable cardioverter-defibrillator (WCD) for discriminating ventricular tachyarrhythmias from other tachyarrhythmias. The WCD includes aplurality of sensing electrodes, a plurality of therapy electrodes, and at least one processor coupled with the plurality of sensing electrodes. The plurality of sensing electrodes are configured to contact skin of a patient externally and detect electrical activity from the skin of the patient, the electrical activity indicating cardiac function in the patient. The plurality of therapy electrodes are configured to contact the skin of the patient externally and deliver electrotherapy to a heart of the patient through the skin of the patient. The at least one processor is coupled with the plurality of sensing electrodes and is configured to derive electrocardiogram (ECG) data from the electrical activity indicating the cardiac function in the patient, detect an apparent tachyarrhythmia in the patient based on the ECG data, and identify an arrhythmic ECG segment indicating the apparent tachyarrhythmia in the patient. The arrhythmic ECG segment includes a first ECG interval bounded by two fiducial points within a first heartbeat of the patient and second ECG interval bounded by a first fiducial point within the first heartbeat and a second fiducial point within a second heartbeat of the patient. The at least one processor is further configured to determine, based on the first and second ECG intervals, a tachyarrhythmic discrimination metric indicative of QRS complex widening and deliver or delay, based on the tachyarrhythmic discrimination metric, the electrotherapy based on whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event.

[0014] Examples of the WCD may incorporate one or more of the following features.

[0015] In the WCD, the ventricular tachyarrhythmic event may include a treatment-advisable cardiac rhythm event in the patient. The supraventricular tachycardiac event may include a treatment -inadvisable cardiac rhythm event in the patient. The treatment-advisable cardiac rhythm event may include a ventricular tachycardia event or a ventricular fibrillation event. The first ECG interval may span a portion of a first heartbeat of the patient. The second ECG interval may span a second heartbeat of the patient. The first heartbeat and the second heartbeat may be distinct heartbeats. The second ECG interval may overlap the first ECG interval. The second ECG interval may not overlap the first ECG interval. The first heartbeat and the second heartbeat may be a same heartbeat. The first ECG interval may span two fiducial points within a QRS complex of the same heartbeat of the patient. The second ECG interval may span two fiducial points within the same heartbeat of the patient. The first ECG interval may be an RS interval. The second ECG interval may be an RR interval.

[0016] In the WCD, the tachyarrhythmic discrimination metric may be a comparison of the first ECG interval and the second ECG interval. The tachyarrhythmic discrimination metric may be a ratio of the first ECG interval to the second ECG interval.

[0017] In the WCD, to deliver or delay the electrotherapy may include to deliver or delay the electrotherapy based on a comparison between a configurable parameter and the tachyarrhythmic discrimination metric. The configurable parameter may quantify a target relationship between the first ECG interval and the second ECG interval.

[0018] In the WCD, the tachyarrhythmic discrimination metric may be a first tachyarrhythmic discrimination metric. The at least one processor may be further configured to determine additional tachyarrhythmic discrimination metrics. To deliver or delay the electrotherapy may include to deliver or delay the electrotherapy based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics. The at least one processor may be further configured to derive at least one summary statistic of the first tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics. To deliver or delay the electrotherapy may include to deliver or delay the electrotherapy based on the at least one summary statistic. The at least one summary statistic may include one or more of mean, median, mode, quartile, percentage, variance, standard deviation, and kurtosis. To determine the additional tachyarrhythmic discrimination metrics may include to determine a first subset of the additional tachyarrhythmic discrimination metrics prior to detection of the apparent tachyarrhythmia, to determine a second subset of the additional tachyarrhythmic discrimination metrics after detection of the apparent tachyarrhythmia. To deliver or delay the electrotherapy may include to determine a first summary statistic of the first subset of the additional tachyarrhythmic discrimination metrics, to determine a second summary statistic of the first tachyarrhythmic discrimination metric and the second subset of the additional tachyarrhythmic discrimination metrics, and to compare the first summary statistic to the second summary statistic. To determine the first subset of the additional tachyarrhythmic discrimination metrics may include to determine the first subset during a baseline measurement of the cardiac function of the patient. To determine the first subset of the additional tachyarrhythmic discrimination metrics may include to analyze first ECG data within a first window. The first window may have a length. To determine the second subset of the additional tachyarrhythmic discrimination metrics may include to analyze second ECG data within a second window having the length. The second ECG data may include the arrhythmic ECG segment.

[0019] In the WCD, the plurality of sensing electrodes may include a side-side electrode pair and a front-back electrode pair. To identify the arrhythmic ECG segment may include to identify a first arrhythmic ECG segment using the front-back electrode pair. To determine the tachyarrhythmic discrimination metric may include to determine a first tachyarrhythmic discrimination metric. The at least one processor may be further configured to identify a second arrhythmic ECG segment using the side-side electrode pair and to determine a second tachyarrhythmic discrimination metric based third and fourth ECG intervals with the second arrhythmic ECG segment. To deliver or delay the electrotherapy may include to deliver or delay the electrotherapy based on the first tachyarrhythmic discrimination metric and the second tachyarrhythmic discrimination metric.

[0020] In the WCD, the at least one processor may be further configured to determine an onset time based on the arrhythmic ECG segment. To deliver or delay the electrotherapy may include to deliver or delay the electrotherapy based on the tachyarrhythmic discrimination metric and the onset time.

[0021] In the WCD, to determine the tachyarrhythmic discrimination metric may include to measure a slope of the ECG data. The tachyarrhythmic discrimination metric may include a metric indicative of asymmetry between a slope of a QR interval and a slope of a RS interval. The metric indicative of asymmetry may include a ratio of a first statistic based on the slope of the QR interval to a second statistic based on the slope of the RS interval. The first statistic and the second statistic may be means or medians.

[0022] In the WCD, to deliver or delay the electrotherapy may include to deliver electrotherapy if the tachyarrhythmic discrimination metric is inconclusive. The tachyarrhythmic discrimination metric may be inconclusive, for example, if the tachyarrhythmic discrimination metric is not indicative of QRS complex widening being above a predetermined threshold.

[0023] At least one example is directed to a wearable cardiac monitor for discriminating ventricular tachyarrhythmias from other tachyarrhythmias. The wearable cardiac monitor includes a plurality of sensing electrodes and at least one processor coupled with the plurality of sensing electrodes. The plurality of sensing electrodes is configured to contact skin of a patient externally, and detect electrical activity from the skin of the patient, the electrical activity indicating cardiac function in the patient. The at least one processor is configured to derive electrocardiogram (ECG) data from the electrical activity indicating the cardiac function in the patient, detect an apparent tachyarrhythmia in the patient based on the ECG data, and identify an arrhythmic ECG segmentindi eating the apparent tachyarrhythmia in the patient. The arrhythmic ECG segment includes first and second ECG intervals. The at least one processor is further configured to determine, based on the first and second ECG intervals, a tachyarrhythmic discrimination metric indicative of QRS complex widening, and record, based on the tachyarrhythmic discrimination metric, an indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event.

[0024] Examples of the wearable cardiac monitor may incorporate one or more of the following features. The ventricular tachyarrhythmic event may include a treatment-advisable cardiac rhythm event in the patient. The supraventricular tachycardiac event may include a treatment-inadvisable cardiac rhythm event in the patient. The treatment-advisable cardiac rhythm event may include a ventricular tachycardia event or a ventricular fibrillation event. The first ECG interval may span a portion of a first heartbeat of the patient. The second ECG interval may span a second heartbeat of the patient. The first heartbeat and the second heartbeat may be distinct heartbeats. The second ECG interval may overlap the first ECG interval. The second ECG interval may not overlap the first ECG interval. The first heartbeat and the second heartbeat may be a same heartbeat. The first ECG interval may span two fiducial points within a QRS complex of the same heartbeat of the patient. The second ECG interval may span two fiducial points within the same heartbeat of the patient. The first ECG interval may be an RS interval. The second ECG interval may be an RR interval.

[0025] In the wearable cardiac monitor, the tachyarrhythmic discrimination metric may be a comparison of the first ECG interval and the second ECG interval. The tachyarrhythmic discrimination metric may be a ratio of the first ECG interval to the second ECG interval.

[0026] In the wearable cardiac monitor, to record the indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event may include to record the indication based on a comparison between a configurable parameter and the tachyarrhythmic discrimination metric. The configurable parameter may quantify a target relationship between the first ECG interval and the second ECG interval.

[0027] In the wearable cardiac monitor, the tachyarrhythmic discrimination metric may be a first tachyarrhythmic discrimination metric. The at least one processor may be further configured to determine additional tachyarrhythmic discrimination metrics. To record the indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventriculartachycardiac event may include to record the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics. The at least one processor maybe further configured to derive at least one summary statistic of the first tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics. To record the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics may include to record the indication based on the at least one summary statistic. The at least one summary statistic may include one or more of mean, median, mode, quartile, percentage, variance, standard deviation, and kurtosis. To determine the additional tachyarrhythmic discrimination metrics may include to determine a first subset of the additional tachyarrhythmic discrimination metrics prior to detection of the apparent tachyarrhythmia and to determine a second subset of the additional tachyarrhythmic discrimination metrics after detection of the apparent tachyarrhythmia. To record the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics may include to determine a first summary statistic of the first subset of the additional tachyarrhythmic discrimination metrics, to determine a second summary statistic of the first tachyarrhythmic discrimination metric and the second subset of the additional tachyarrhythmic discrimination metrics, and to compare the first summary statistic to the second summary statistic. To determine the first subset of the additional tachyarrhythmic discrimination metrics may include to determine the first subset during a baseline measurement of the cardiac function of the patient. To determine the first subset of the additional tachyarrhythmic discrimination metrics may include to analyze first ECG data within a first window. The first window may have a length. To determine the second subset of the additional tachyarrhythmic discrimination metrics may include to analyze second ECG data within a second window having the length, the second ECG data including the arrhythmic ECG segment.

[0028] In the wearable cardiac monitor, the plurality of sensing electrodes may include a side-side electrode pair and a front-back electrode pair. To identify the arrhythmic ECG segment may include to identify a first arrhythmic ECG segment using the front-back electrode pair. To determine the tachyarrhythmic discrimination metric may include to determine a first tachyarrhythmic discrimination metric. The at least one processor may be further configured to identify a second arrhythmic ECG segment using the side-side electrode pair and to determine a second tachyarrhythmic discrimination metric based third and fourth ECG intervals with thesecond arrhythmic ECG segment. To record the indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event may include to record the indication based on the first tachyarrhythmic discrimination metric and the second tachyarrhythmic discrimination metric.

[0029] In the wearable cardiac monitor, the at least one processor may be further configured to determine an onset time based on the arrhythmic ECG segment. To record the indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event may include to record the indication based on the tachyarrhythmic discrimination metric and the onset time.

[0030] In the wearable cardiac monitor, to determine the tachyarrhythmic discrimination metric may include to measure a slope of the ECG data. The tachyarrhythmic discrimination metric may include a metric indicative of asymmetry between a slope of a QR interval and a slope of a RS interval. The metric indicative of asymmetry may include a ratio of a first statistic based on the slope of the QR interval to a second statistic based on the slope of the RS interval. The first statistic and the second statistic may be means or medians.

[0031] At least one example is directed to one or more non-transitory computer readable media storing sequences of instructions executable by at least one processor of a medical device to discriminate ventricular tachyarrhythmias from other tachyarrhythmias. The sequences of instructions include instructions to derive electrocardiogram (ECG) data from electrical activity from a skin of a patient, the electrical activity indicating cardiac function in the patient; detect an apparent tachyarrhythmia in the patient based on the ECG data; and identify an arrhythmic ECG segment indicating the apparent tachyarrhythmia in the patient. The arrhythmic ECG segment includes first and second ECG intervals. The sequences of instructions further includes instruction to determine, based on the first and second ECG intervals, a tachyarrhythmic discrimination metric indicative of QRS complex widening; and provide, based on the tachyarrhythmic discrimination metric, an indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event.

[0032] Examples of the one or more non-transitory computer readable media may incorporate one or more of the following features.

[0033] In the one or more non-transitory computer readable media, the ventricular tachyarrhythmic event may include a treatment-advisable cardiac rhythm event in the patient. Thesupraventricular tachycardiac event may include a treatment-inadvisable cardiac rhythm event in the patient. The treatment-advisable cardiac rhythm event may include a ventricular tachycardia event or a ventricular fibrillation event.

[0034] In the one or more non-transitory computer readable media, the first ECG interval may span a portion of a first heartbeat of the patient. The second ECG interval may span a second heartbeat of the patient. The first heartbeat and the second heartbeat may be distinct heartbeats. The second ECG interval may overlaps the first ECG interval. The second ECG interval may not overlap the first ECG interval. The first heartbeat and the second heartbeat may be a same heartbeat. The first ECG interval may span two fiducial points within a QRS complex of the same heartbeat of the patient. The second ECG interval may span two fiducial points within the same heartbeat of the patient. The first ECG interval may be an RS interval. The second ECG interval may be an RR interval.

[0035] In the one or more non-transitory computer readable media, the tachyarrhythmic discrimination metric may be a comparison of the first ECG interval and the second ECG interval. The tachyarrhythmic discrimination metric may be a ratio of the first ECG interval to the second ECG interval. To provide the indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event may include to provide the indication based on a comparison between a configurable parameter and the tachyarrhythmic discrimination metric. The configurable parameter may quantify a target relationship between the first ECG interval and the second ECG interval.

[0036] In the one or more non-transitory computer readable media, the tachyarrhythmic discrimination metric may be a first tachyarrhythmic discrimination metric. The instructions may further include instructions to determine additional tachyarrhythmic discrimination metrics. To provide the indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event may include to provide the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics. The instructions may further include instructions to derive at least one summary statistic of the first tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics. To provide the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics may include to provide the indication based on the at least one summary statistic. The at least one summarystatistic may include one or more of mean, median, mode, quartile, percentage, variance, standard deviation, and kurtosis. To determine the additional tachyarrhythmic discrimination metrics may include to determine a first subset of the additional tachyarrhythmic discrimination metrics prior to detection of the apparent tachyarrhythmia and to determine a second subset of the additional tachyarrhythmic discrimination metrics after detection of the apparent tachyarrhythmia. To provide the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics may include to determine a first summary statistic of the first subset of the additional tachyarrhythmic discrimination metrics, to determine a second summary statistic of the first tachyarrhythmic discrimination metric and the second subset of the additional tachyarrhythmic discrimination metrics, and to compare the first summary statistic to the second summary statistic. To determine the first subset of the additional tachyarrhythmic discrimination metrics may include to determine the first subset during a baseline measurement of the cardiac function of the patient. To determine the first subset of the additional tachyarrhythmic discrimination metrics may include to analyze first ECG data within a first window. The first window may have a length. To determine the second subset of the additional tachyarrhythmic discrimination metrics may include to analyze second ECG data within a second window having the length. The second ECG data may include the arrhythmic ECG segment.

[0037] In the one or more non-transitory computer readable media, to identify the arrhythmic ECG segment may include to identify a first arrhythmic ECG segment using a front-back electrode pair. To determine the tachyarrhythmic discrimination metric may include to determine a first tachyarrhythmic discrimination metric. The instructions may further include instructions to identify a second arrhythmic ECG segment using a side-side electrode pair and to determine a second tachyarrhythmic discrimination metric based third and fourth ECG intervals with the second arrhythmic ECG segment. To provide the indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmic event or a supraventricular tachycardiac event may include to provide the indication based on the first tachyarrhythmic discrimination metric and the second tachyarrhythmic discrimination metric.

[0038] In the one or more non-transitory computer readable media, the instructions may further include instructions to determine an onset time based on the arrhythmic ECG segment. To provide the indication of whether the arrhythmic ECG segment includes a ventricular tachyarrhythmicevent or a supraventricular tachycardiac event may include to provide the indication based on the tachyarrhythmic discrimination metric and the onset time.

[0039] In the one or more non-transitory computer readable media, to determine the tachyarrhythmic discrimination metric may include to measure a slope of the ECG data. The tachyarrhythmic discrimination metric may include a metric indicative of asymmetry between a slope of a QR interval and a slope of a RS interval. The metric indicative of asymmetry may include a ratio of a first statistic based on the slope of the QR interval to a second statistic based on the slope of the RS interval. The first statistic and the second statistic may be means or medians.

[0040] At least one example is directed to a medical device, which may be part of an ambulatory medical system or a wearable cardioverter-defibrillator or a wearable cardiac monitor. The medical device may be configured for discriminating ventricular tachyarrhythmias from other tachyarrhythmias. The medical device may comprise a first interface configured to receive signals from a plurality of sensing electrodes wherein the signals are indicative of cardiac function. In the present example, the sensing electrodes are configured to contact the skin of the patient. The medical device may comprise a second interface configured to couple to therapy electrodes for delivering electrotherapy through the skin of the patient, depending on the type of medical device. The medical device may include at least one processor configured to derive electrocardiogram (ECG) data from the signals. The processor may detect an apparent tachyarrhythmia in the patient based on the ECG data and first predetermined detection criteria, and identify an arrhythmic ECG segment indicating the apparent tachyarrhythmia in the patient. The processor may be further configured to determine whether the arrhythmic ECG segment includes one or either of a ventricular tachyarrhythmic event or a supraventricular tachycardiac event based on the arrhythmic ECG segment and second predetermined detection criteria. The second predetermined detection criteria may be based on changes to one or more QRS complexes detected in the arrhythmic ECG segment, such as changes that exceed a predetermined threshold. The first predetermined detection criteria may be different to the second predetermined detection criteria. In some examples, the processor may be configured to determine whether the ECG data includes one or either of a ventricular tachyarrhythmic event or a supraventricular tachycardiac event based on the ECG data and the second predetermined detection criteria, such as without first identifying an arrhythmic ECG segment indicative of the apparent tachyarrhythmia in the patient. The second predetermined criteria may be based on a ratio of one or more RS intervals to one or more RR intervals in theECG data or the arrhythmic ECG segment to discriminate between VT and other TAs, such as SVT.BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Various aspects of at least one example are discussed below with reference to the accompanying figures, which are not intended to be drawn to scale. The figures are included to provide an illustration and a further understanding of the various aspects and examples and are incorporated in and constitute a part of this specification but are not intended to limit the scope of the disclosure. The drawings, together with the remainder of the specification, serve to explain principles and operations of the described and claimed aspects and examples. In the figures, each identical or nearly identical component that is illustrated in various figures is represented by a like numeral. For purposes of clarity, not every component may be labeled in every figure.

[0042] FIGS. 1 A and IB are illustrations of cross-sections of a human heart.

[0043] FIG. 1C includes a line diagram illustrating a signal produced by normal conduction within a human heart and a line diagram illustrating a signal produced by preexcitation conduction within a human heart.

[0044] FIG. 2 includes two line graphs illustrating signals produced by a human heart presenting with supraventricular tachycardia and two line graphs illustrating signals produced by a human heart presenting with ventricular tachycardia.

[0045] FIG. 3 is a schematic diagram of a controller of a wearable medical device in accordance with examples disclosed herein.

[0046] FIG. 4 is a block diagram of one example of a cardiac event detector with TA discrimination in accordance with examples disclosed herein.

[0047] FIG. 5 is a unified modeling language diagram illustrating an object model used to implement an arrhythmia detector that identifies R-points, S-points, and intervals based thereon in accordance with examples disclosed herein.

[0048] FIG. 6 is a flow diagram illustrating a process of generating fiducial parameters in accordance with examples disclosed herein.

[0049] FIGS. 7A and 7B are a flow diagram illustrating a process of detecting R-points and S- point in accordance with examples disclosed herein.

[0050] FIG. 8 is a timeline illustrating analysis of ECG data by a cardiac event detector with TA discrimination technology.

[0051] FIG. 9 is a flow diagram illustrating a use of TA discrimination technology to validate or refute a declaration indicating that treatment of a detected arrhythmia is advisable.

[0052] FIG. 10 is another timeline illustrating analysis of ECG data by a cardiac event detector with TA discrimination technology.

[0053] FIG. 11 is another flow diagram illustrating a use of TA discrimination technology to validate or refute a declaration indicating that treatment of a detected arrhythmia is advisable.

[0054] FIG. 12 is another timeline illustrating analysis of ECG data by a cardiac event detector with TA discrimination technology.

[0055] FIG. 13 is another flow diagram illustrating a use of TA discrimination technology to validate or refute a declaration indicating that treatment of a detected arrhythmia is advisable.

[0056] FIGS. 14A-14D illustrate example ambulatory cardiac devices in accordance with examples disclosed herein.DETAILED DESCRIPTION

[0057] Electrical activation of a human heart (e g., a heartbeat) is normally a well-organized physiological process during which a wave of action potentials spreads throughout the heart to efficiently and effectively pump blood. FIG. 1A illustrates anatomical features of a human heart 100 involved in its electrical activation. As shown in FIG. 1A, these anatomical features include a sinoatrial (SA) node 102, atrial pathways 104, an atrioventricular (AV) node 106, and ventricular pathways 108. A normal human heartbeat begins at the SA node 102. The SA node 102 generates a wave of action potentials that travel, via intercellular conduction, throughout the atria along the atrial pathways 104. The wave of action potentials depolarizes the cells of the atria, initiating contractions therein. The wave of action potentials arrives at and passes through the AV node 106 to travel throughout the ventricles along the ventricular pathways 108. The tissues involved in the electrical activation of the heart regulate timing of the wave such that each chamber contributes to the pumping of blood through the heart. During a normal heartbeat, atrial activation is complete approximately 90 milliseconds after the SA node 102 initiates the heartbeat. Atrial activation is followed by ventricular activation, which is complete approximately 230 milliseconds after the SA node 102 initiation of the heartbeat.

[0058] Conduction blocks can disrupt the clockwork timing of the heart and cause TAs to occur. More specifically, in some situations, conduction blocks can cause the wave of action potentials to stray from established pathways and reenter cells that have already been depolarized by the wave. By way of an example, a phenomenon such as “reentry” may be implicated. FIG. IB illustrates local reentry 112 and global reentry 114, both of which can be caused by conduction blocks. In local reentry 112, a portion of the atria becomes a self-perpetuating circuit of excitation, which can lead to atrial tachycardia. Similarly, in global reentry 114 the ventricles are repeatedly activated via an alternative pathway that includes both ventricular and atrial cells, which can lead to VT. With regard to VT, the wave of action potentials may enter the ventricles early, for example, via a bypass track 116.

[0059] Both local and global reentry can cause individual heartbeats to become protracted. FIG. 1C illustrates both a signal 120 produced by a normal heartbeat and a signal 122 produced by a heartbeat in which the wave initiated by an AV node travels a bypass track, resulting in a prolonged heartbeat that includes a delta wave 124 and a widened QRS 126. For example, such delta waves can be associated with Wolff-Parkinson-White (WPW) syndrome, as an example of a type of SVT. In examples, such delta waves are characterized as a slurring slow rise of the initial portion of the QRS complex, along with a widened QRS complex. WPW is believed to manifest in around 0.1 to 3 per 1000 people and associated with risk of sudden cardiac death. WPW is attributed to a congenital accessory pathway with episodes of tachyarrhythmias. In such patients, a PR interval duration can be less than around 120 milliseconds, e.g., in a range of 70-120 milliseconds. There may be prolongation of QRS as noted above, e.g., greater than 110 milliseconds, along with manifest delta waves. In examples, presentation of discordant SR-segment and T-wave changes (e.g., in opposition direction to a major component of the QRS complex) can occur.

[0060] Given its physiological basis, some patients who experience VT, which can be caused by reentry among other conditions, present with an RS interval that is greater than 100 milliseconds (ms). However, a substantial number (e.g., 30% or more) of VTs do not exhibit this characteristic. Moreover, while VT can present with heart rates over 150 beats per minute (bpm), this attribute is shared with other TAs, such as SVT. As such, heart rates and / or widened RS intervals alone cannot be relied upon to discriminate completely between VT and SVT.

[0061] In view of the clinical issues described above, at least some examples described herein are directed to medical devices with enhanced ability to discriminate between arrhythmias (e.g., TAs)for which electrotherapy is advisable (e.g., treatment-advisable arrhythmias) and arrhythmias for which electrotherapy is not advisable (e.g., treatment-inadvisable arrhythmias). As explained above, discerning between treatment-advisable and treatment-inadvisable arrhythmias is a benefit of the examples disclosed herein, depending on the particular arrhythmia present in a given patient. For instance, some examples described herein autonomously distinguish between SVT, which is a treatment-inadvisable arrhythmia, and VT, which is a treatment-advisable arrhythmia. Accurately distinguishing between these two types of TAs is useful for positive patient outcomes. Inappropriately treating SVT, which is not a life-threatening arrhythmia, with electrotherapy can, in some instances, convert the SVT to VT, which is a life-threatening arrhythmia.

[0062] As such, systems for and methods of discriminating between types of TAs are provided. In some examples, these systems and methods manifest an appreciation for the principle that intervals between certain fiducial points in ECG data exhibit relationships that are consistent within TAs of the same type but inconsistent between TAs of different types. For instance, in certain examples, the TA discrimination technology described herein utilizes ratios of the RS intervals to RR intervals to discriminate between VT and other TAs, such as SVT. Research conducted by the Applicant has shown that this RS / RR ratio can be used to discriminate VT from SVT with a sensitivity of 90% or more.

[0063] In some examples, a medical device with the TA discrimination technology described herein is capable of detecting arrhythmias, such as VTs, via a plurality of electrodes attached to a patient’s skin. The medical device can derive electrocardiogram (ECG) data segments indicative of a patient’s cardiac function from electrical activity acquired from the patient’s skin via the electrodes. This electrical activity includes noise and electrical signals (ECG signals) generated by the patient’s heart. The medical device can filter the noise and detect an apparent tachyarrhythmia in the patient based on a segment of derived ECG data. This arrhythmic ECG segment can include two intervals bounded by fiducial points in the ECG data.

[0064] In some examples, the two intervals included in the ECG data segment can include an intraheartbeat interval and an inter-heartbeat interval. The bounding fiducial points of the intraheartbeat interval can reside within a single heartbeat. The bounding fiducial points of the interheartbeat interval can span two or more heartbeats. In some examples, the intra-heartbeat interval is an RS interval and the inter-heartbeat interval is an RR interval that includes the RS interval. Inother examples, the intra-heartbeat interval and the inter-heartbeat interval do not overlap. The two intervals need not involve the same heartbeat.

[0065] The medical device can detect a widening of the patient’s QRS complex by analyzing two or more intervals, and thereby discriminate between TAs, such as VT and SVT. This analysis can involve calculation of one or more discrimination metrics, such as one or more a ratios of RS intervals to RR intervals and / or statistics that summarize the discrimination metrics. These statistics may include means, medians, modes, quartiles, interquartile means, standard deviations, variances, ranges, interquartile ranges, absolute deviations, mean absolute deviations, distance standard deviations, coefficients of variation, skewnesses, kurtoses, and distance skewnesses, among others. The analysis can further include comparing the one or more discrimination metrics and / or summary statistics to one or more configurable parameters that specify one or more threshold values. Any combination of comparison and threshold value can quantify a target relationship between the discrimination metrics and / or summary statistics. In some examples, the medical device can analyze ECG data from distinct leads (e.g., electrode pairs) separately and can analyze ECG data collected both before and after occurrence of a TA.

[0066] The medical device can take any of a variety of actions based on a result of these comparisons, such as recording the type of TA, notifying other devices of the type of TA the patient is presenting, and / or initiating a protocol to treat the patient with electrotherapy.

[0067] FIG. 2 includes line graphs 202 and 204 that plot ECG data segments in millivolts over time. As shown in FIG. 2, the line graph 202 plots ECG data acquired from a patient presenting with SVT. The line graph 204 plots ECG data acquired from a patient presenting with VT. The line graph 202 further plots an RS interval 202A and an RR interval 202B. In this example, the RS interval 202A is a period of time between occurrence of an R-point within the ECG data and occurrence of an S-point within the ECG data. As illustrated in FIG. 2, the RS interval begins and ends within a single QRS complex, although this is not a requirement. Further, in this example, the RR interval 202B is a period of time between occurrence of two R-points within the ECG data. As illustrated in FIG. 2, the RR interval begins and ends within adjacent QRS complexes, although this is not a requirement. The line graph 204 plots analogous RS and RR intervals 204A and 204B. As can be seen from FIG. 2, the duration of the RS interval 202A as compared to the duration of the RR interval 202B is substantially less than the duration of the RS interval 204A as comparedto the duration of the RR interval 204B. This principle informs and is applied by several of the examples disclosed herein.

[0068] Example systems and methods that implement TA discrimination based on the foregoing principles will now be described in detail with reference to FIGS. 3-14D. FIG. 3 schematically illustrates one example of a medical device controller 300 that implements TA discrimination techniques that apply the principles described above with reference to FIGS. 2A and 2B.

[0069] As shown in FIG. 3, the medical device controller 300 can include a housing 301 which can be physically integrated with, or distinct from, other parts of a medical device controlled by the controller 300. The housing 301 physically accommodates and arranges other parts of the controller 300, such as at least one data storage 304, at least one network interface 306, at least one user interface 308, at least one rechargeable battery 310, at least one processor 318, at least one physiological sensor interface 312, at least one accelerometer interface 330, and at least one accelerometer 332. It will be appreciated that in one or more embodiments not all of the above- mentioned parts of the controller 300 may be present. In particular, for a controller 300 providing particular functionality, only the components required to provide that particular functionality may necessary, although, of course, other parts may be provided in addition. Further, in the present example, the at least one processor 318 is shown in the housing 301. However, it will be appreciated that in some examples, some or all of the processing described as performed by the at least one processor 318 may be performed by a processor external to the controller 300 and communicably coupled thereto, such as a processor of a computing device local to the controller 300, such as a tablet, or a computing device remote from the controller 300, such as a server. As shown in FIG. 3, the processor 318 implements or otherwise initiates execution of at least one cardiac event detector 316 with TA discrimination, which is illustrated with reference to FIG. 4 and described in more detail below.

[0070] In some examples, the housing 301 further accommodates and arranges therapy delivery circuitry 302 which is configured to provide one or more therapeutic shocks to a patient via at least two therapy electrodes 320. In certain examples, the physiological sensor interface 312 can be configured to interface with both ECG sensing electrodes 322 and non-ECG physiological sensors 323, such as vibrational sensors, lung fluid sensors, infrared and near-infrared-based pulse oximetry sensors, and blood pressure sensors, among other types of sensors.

[0071] In some examples, one or more ambulatory cardiac devices includes a version of the controller 300 that includes like components as those described above but that does not include the therapy delivery circuitry 302 and the therapy electrodes 320 (shown in dotted lines). That is, in certain implementations, a medical device can include only ECG monitoring components and not be configured to provide therapy to the patient. In such implementations, such as a heart failure management system (HFMS), a cardiac event monitor (CEM), or a mobile cardiac telemetry (MCT) device, the construction of the controller of the medical device is similar in many respects to the medical device controller 300 but need not include the therapy delivery circuitry 302 and associated therapy electrodes 320.

[0072] As further shown in FIG. 3, the therapy delivery circuitry 302 can include, or be operably connected to, circuitry that is configured to generate and provide an electrical therapeutic shock. The circuitry can include, for example, resistors, capacitors, relays and / or switches, electrical bridges such as an h-bridge (e.g., including a plurality of insulated gate bipolar transistors or IGBTs), voltage and / or current measuring components, and other similar circuitry components arranged and connected such that the circuitry components work in concert with the therapy delivery circuitry and under control of one or more processors (e.g., the processor 318) to provide, for example, at least one therapeutic shock to the patient including one or more pacing, cardioversion, or defibrillation therapeutic pulses.

[0073] Pacing pulses can be used to treat cardiac arrhythmia conditions such as bradycardia (e.g., less than 30 bpm) and tachycardia (e.g., more than 150 bpm) using, for example, fixed rate pacing, demand pacing, anti-tachycardia pacing, and the like. Defibrillation pulses can be used to treat ventricular tachycardia and / or ventricular fibrillation.

[0074] The capacitors can include a parallel-connected capacitor bank consisting of a plurality of capacitors (e.g., two, three, four or more capacitors). In some examples, the capacitors can include a single film or electrolytic capacitor as a series connected device including a bank of the same capacitors. These capacitors can be switched into a series connection during discharge for a defibrillation pulse. For example, a single capacitor of approximately 140 uF or larger, or four capacitors of approximately 650 uF can be used. The capacitors can have a 1600 VDC or higher rating for a single capacitor, or a surge rating between approximately 350 to 500 VDC for paralleled capacitors and can be charged in approximately 15 to 30 seconds from a battery pack.

[0075] For example, each defibrillation pulse can deliver between 60 to 180 joules of energy. In some implementations, the defibrillating pulse can be a biphasic truncated exponential waveform, whereby the signal can switch between a positive and a negative portion (e.g., charge directions). This type of waveform can be effective at defibrillating patients at lower energy levels when compared to other types of defibrillation pulses (e.g., such as monophasic pulses). For example, an amplitude and a width of the two phases of the energy waveform can be automatically adjusted to deliver a precise energy amount (e.g., 150 joules) regardless of the patient’s body impedance. The therapy delivery circuitry 302 can be configured to perform the switching and pulse delivery operations, e.g., under control of the processor 318. As the energy is delivered to the patient, the amount of energy being delivered can be tracked. For example, the amount of energy can be kept to a predetermined constant value even as the pulse waveform is dynamically controlled based on factors such as the patient’s body impedance when the pulse is being delivered.

[0076] In certain examples, the therapy delivery circuitry 302 can be configured to deliver a set of cardioversion pulses to correct, for example, an improperly beating heart. When compared to defibrillation as described above, cardioversion typically includes a less powerful shock that is delivered at a certain frequency to mimic a heart’s normal rhythm.

[0077] In some examples, the network interface 306 can facilitate communication of information between the medical device controller 300 and one or more other devices or entities over a communications network. For example, where the medical device controller 300 is included in an ambulatory medical device, the network interface 306 can be configured to communicate with a remote computing device such as a remote server or other similar computing device. The network interface 306 can include, for example, communications circuitry for transmitting data in accordance with a BLUETOOTH wireless standard for exchanging such data over short distances to an intermediary device. For example, such an intermediary device can be configured as a base station, a “hotspot” device, a smartphone, a tablet, a portable computing device, and / or other devices in proximity of the wearable medical device including the medical device controller 300. In some examples the base station or “hotspot” functionality may be provided by a smartphone, a tablet, a portable computing device, and / or other devices in proximity of the wearable medical device including the medical device controller 300. The intermediary device(s) may in turn communicate the data to a remote server over a broadband cellular network communications link. The communications link may implement broadband cellular technology (e.g., 2.5G, 2.75G, 3G,4G, 5G cellular standards) and / or Long-Term Evolution (LTE) technology or GSM / EDGE and UMTS / HSPA technologies for high-speed wireless communication. In some implementations, the intermediary device(s) may communicate with a remote server over a WI-FI communications link based on an IEEE 802.11 standard.

[0078] In certain examples, the user interface 308 can include one or more physical interface devices such as input devices, output devices, and combination input / output devices and a software stack configured to drive operation of the devices. These user interface elements can render visual, audio, and / or tactile content. Thus, the user interface 308 can receive input or provide output, thereby enabling a user to interact with the medical device controller 300.

[0079] The medical device controller 300 can also include at least one rechargeable battery 310 configured to provide power to one or more integral parts of the medical device controller 300. The rechargeable battery 310 can include a rechargeable multi-cell battery pack. In one example implementation, the rechargeable battery 310 can include three or more 2200 mAh lithium ion cells that provide electrical power to the other parts of the medical device controller 300. For example, the rechargeable battery 310 can provide its power output in a range of between 20 mA to 1000 mA (e.g., 40 mA) output and can support 24 hours, 48 hours, 72 hours, or more, of runtime between charges. In certain implementations, the battery capacity, runtime, and type (e.g., lithium ion, nickel-cadmium, or nickel-metal hydride) can be changed to best fit the specific application of the medical device controller 300.

[0080] The physiological sensor interface 312 can include physiological signal circuitry that is coupled to one or more sensors configured to monitor one or more physiological parameters of the patient. As shown, the sensors can be coupled to the medical device controller 300 via a wired or wireless connection. The sensors can include one or more ECG sensing electrodes 322, and / or non-ECG physiological sensors 323 such as one or more of a vibration sensor(s) 324, tissue fluid monitor(s) 326 (e.g., based on ultra-wide band RF devices), and motion sensor(s) (e.g., accelerometers, gyroscopes, and / or magnetometers). In some implementations, the sensors can include a plurality of conventional ECG sensing electrodes in addition to digital sensing electrodes.

[0081] The sensing electrodes 322 can be configured to monitor a patient’s ECG information. For example, by design, the digital sensing electrodes 322 can include skin-contacting electrode surfaces that may be deemed polarizable or non-polarizable depending on a variety of factors including the metals and / or coatings used in constructing the electrode surface. All such electrodescan be used with the principles, techniques, devices and systems described herein. For example, the electrode surfaces can be based on stainless steel, noble metals such as platinum, or Ag-AgCl.

[0082] In some examples, the electrodes 322 can be used with an electrolytic gel dispersed between the electrode surface and the patient’s skin. In certain implementations, the electrodes 322 can be dry electrodes that do not need an electrolytic material. As an example, such a dry electrode can be based on tantalum metal and having a tantalum pentoxide coating as is described above. Such dry electrodes can be more comfortable for long term monitoring applications.

[0083] Referring back to FIG. 3, the vibration sensors 324 can be configured to detect cardiac and / or pulmonary vibration information. For example, the vibration sensors 324 can detect a patient’s heart valve vibration information. For example, the vibration sensors 324 can be configured to detect cardio-vibrational signal values including any one or all of SI, S2, S3, and S4. From these cardio-vibrational signal values or heart vibration values, certain heart vibration metrics may be calculated, including any one or more of electromechanical activation time (EMAT), average EMAT, percentage of EMAT (% EMAT), systolic dysfunction index (SDI), and left ventricular systolic time (LVST). The vibration sensors 324 can also be configured to detect heart wall motion, for instance, by placement of the sensor in the region of the apical beat. The vibration sensors 324 can include a vibrational sensor configured to detect vibrations from a patient’s cardiac and pulmonary system and provide an output signal responsive to the detected vibrations of a targeted organ, for example, being able to detect vibrations generated in the trachea or lungs due to the flow of air during breathing. In certain implementations, additional physiological information can be determined from pulmonary-vibrational signals such as, for example, lung vibration characteristics based on sounds produced within the lungs (e.g., stridor, crackle, etc.). The vibration sensors 324 can also include a multi-channel accelerometer, for example, a three-channel accelerometer configured to sense movement in each of three orthogonal axes such that patient movement / body position can be detected and correlated to detected cardio- vibrational information. The vibration sensors 324 can transmit information descriptive of the cardio-vibrational information to the sensor interface 312 for subsequent analysis.

[0084] The tissue fluid monitors 326 can use RF based techniques to assess fluid levels and accumulation in a patient’s body tissue. For example, the tissue fluid monitors 326 can be configured to measure fluid content in the lungs, typically for diagnosis and follow-up of pulmonary edema or lung congestion in heart failure patients. The tissue fluid monitors 326 caninclude one or more antennas configured to direct RF waves through a patient’s tissue and measure output RF signals in response to the waves that have passed through the tissue. In certain implementations, the output RF signals include parameters indicative of a fluid level in the patient’s tissue. The tissue fluid monitors 326 can transmit information descriptive of the tissue fluid levels to the sensor interface 312 for subsequent analysis.

[0085] As further shown in FIG. 3, the controller 300 can further include an accelerometer interface 330 for coupling to one or more accelerometers. In the present example, the controller 300 comprises the accelerometer interface 330 and a set of accelerometers 332. The accelerometer interface 330 can be operably coupled to one or more of the accelerometers 332 and configured to receive one or more outputs from the accelerometers 332. The accelerometer interface 330 can be further configured to condition the output signals by, for example, converting analog accelerometer signals to digital signals (if using an analog accelerometer), filtering the output signals, combining the output signals into a combined directional signal (e g., combining each x- axis signal into a composite x-axis signal, combining each y-axis signal into a composite y-axis signal, and combining each z-axis signal into a composite z-axis signal). In some examples, the accelerometer interface 330 can be configured to filter the signals using a high-pass or band-pass filter to isolate the acceleration of the patient due to movement from the component of the acceleration due to gravity.

[0086] Additionally or alternatively, the accelerometer interface 330 can configure the output for further processing. For example, the accelerometer interface 330 can be configured to arrange the output of an individual accelerometer 332 as a vector expressing the acceleration components of the x-axis, the y-axis, and the z-axis as received from each accelerometer. The accelerometer interface 330 can be operably coupled to the processor 318 and configured to transfer the output signals from the accelerometers 332 to the processor 318 for further processing and analysis.

[0087] As described above, one or more of the accelerometers 332 can be integrated into one or more components of a medical device. For example, as shown in FIG. 3, an accelerometer 332 can be integrated into the controller 300. In some examples, an accelerometer 332 can be integrated into one or more of a therapy electrode 320, a sensing electrode 322, a physiological sensor 323, and into other components of a medical device. When controller 300 is included in a hospital wearable defibrillator (HWD), an accelerometer can be integrated into an adhesive ECG sensing and / or therapy electrode patch.

[0088] In certain implementations, the cardiac event detector 316 can be configured to monitor a patient’s ECG signal for an occurrence of a cardiac event such as an arrhythmia or other similar cardiac event. The cardiac event detector can be configured to operate under control of the processor 318 to execute one or more methods that process received ECG signals from, for example, the sensing electrodes 322 and determine the likelihood that a patient is experiencing a cardiac event. As illustrated in FIG. 3, the cardiac event detector 316 implements TA discrimination techniques as described further below with reference to FIGS. 4-13. In some examples, to implement TA discrimination the cardiac event detector 316 analyzes the received ECG signals to detect particular fiducial points therein. For instance, in certain examples, these fiducial points include R-points and S-points. An R-point is the crest of an R-wave. An S-point is the trough of an S-wave. Some examples of the cardiac event detector 316 identify these fiducial points at least in part by executing processes that find local maxima and / or local minima within the ECG data (e.g., via changes in slope). In some examples, the cardiac event detector 316 further executes processes that calculate RR intervals and RS intervals using the identified R-points and S-points. Examples of processes that the cardiac event detector 316 is configured to execute in some implementations are described further below with reference to FIGS. 6-13. FIG. 5, which is described further below, illustrates an example of a class model implemented by some instances of the cardiac event detector 316 to execute the processes outlined above and described further below.

[0089] In some examples, the cardiac event detector 316 utilizes the parameters described above (e.g., R-points, S-points, RS intervals, and RR intervals) to discern certain types of TAs from others. For instance, in certain examples, the cardiac event detector 316 discriminates VT from other TAs by comparing an RS interval to an RR interval. In at least one example, the cardiac event detector 316 calculates a ratio of the RS interval to the RR interval as part of this comparison. Specific examples of processes that the cardiac event detector 316 is configured to execute to discriminate VT from other TAs (e.g., SVT) are described further below with reference to FIGS. 8-13.

[0090] The cardiac event detector 316 can be implemented using hardware or a combination of hardware and software. For instance, in some examples, cardiac event detector 316 can be implemented as code that is stored within the data storage 304 and executed by the processor 318. In this example, the instructions included in the cardiac event detector 316 can cause the processor318 to perform one or more methods for analyzing a received ECG signal to determine, using TA discrimination if appropriate, whether an adverse cardiac event is occurring. In other examples, the cardiac event detector 316 can be an application-specific integrated circuit (ASIC) that is coupled to the processor 318 and configured to monitor ECG signals, using TA discrimination if appropriate, for adverse cardiac event occurrences. Thus, examples of the cardiac event detector 316 are not limited to a particular hardware or software implementation.

[0091] In some implementations, the processor 318 includes one or more processors (or one or more processor cores) that each are configured to perform a series of instructions that result in manipulated data and / or control the operation of the other components of the medical device controller 300. In some implementations, when executing a specific process (e.g., cardiac monitoring), the processor 318 can be configured to make specific logic-based determinations based on input data received and be further configured to provide one or more outputs that can be used to control or otherwise inform subsequent processing to be carried out by the processor 318 and / or other processors or circuitry with which the processor 318 is communicatively coupled. Thus, the processor 318 reacts to specific input stimulus in a specific way and generates a corresponding output based on that input stimulus. In some example cases, the processor 318 can proceed through a sequence of logical transitions in which various internal register states and / or other bit cell states internal or external to the processor 318 can be set to logic high or logic low. As referred to herein, the processor 318 can be configured to execute a function where software is stored in a data store coupled to the processor 318, the software being configured to cause the processor 318 to proceed through a sequence of various logic decisions that result in the function being executed. The various components that are described herein as being executable by the processor 318 can be implemented in various forms of specialized hardware, software, or a combination thereof. For example, the processor 318 can be a digital signal processor (DSP) such as a 24-bit DSP. The processor 318 can be a multi-core processor, e.g., having two or more processing cores. The processor 318 can be an Advanced RISC Machine (ARM) processor such as a 32-bit ARM processor or a 64-bit ARM processor. The processor 318 can execute an embedded operating system, and include services provided by the operating system that can be used for file system manipulation, display & audio generation, basic networking, firewalling, data encryption and communications.

[0092] In some examples, ambulatory cardiac devices including the controller 300 can further include front-end configurations that use circuitry to accommodate a signal from a high source impedance from the sensing electrode (e.g., having an internal impedance range from approximately 100 Kiloohms to one or more Megaohms). This high source impedance signal is processed and transmitted to a monitoring device such as the processor 318 of the controller 300 as described above for further processing. In some examples, the medical device, such as an ambulatory cardiac device, may include circuitry configured to cancel out common mode signals. In certain implementations, the ambulatory cardiac devices include a microprocessor or another dedicated processor operably coupled to the sensing electrodes that is configured to receive a common noise signal from each of the sensing electrodes, sum the common noise signals, invert the summed common noise signals and feed the inverted signal back into the patient as a driven ground using, for example, a driven right leg circuit to cancel out common mode signals.

[0093] Turning now to FIG. 4, one example of the cardiac event detector 316 with TA discrimination is illustrated as a block diagram. As shown in FIG. 4, the detector 316 is configured to detect cardiac events experienced by the patient using a variety of analysis techniques. In some examples, the detector 316 receives electrode signals 402 from various electrodes of a medical device, such as any of the medical devices described further below with reference to FIGS. 14A- 14D. For instance, the electrode signals 402 may include signals from a front-back electrode pair and a side-side electrode pair, as described further below with reference to FIG. 14A. In these and other examples, the detector 316 generates an arrhythmia status signal 422 that indicates whether the patient is experiencing an arrhythmia. The arrhythmia status signal 422 may also include an indication of the particular type of arrhythmia the patient is experiencing including, for example, an indication of whether the patient is experiencing SVT, VT, or VF, among others. It is appreciated that the various blocks included within the detector 316 may be implemented in hardware, software, or a combination of hardware and software. For example, the electrode signals 402 may include a digitized ECG signal of the patient and each of the blocks may be function blocks executed by a Digital-Signal-Processor (DSP).

[0094] In some examples, the high frequency (HF) noise analyzer block 410, automatic gain control block 412, and electrode falloff sensing block 414 each determine, generate, and communicate indicators of qualities of the electrode signals 402 being received. For instance, in certain examples, the HF noise analyzer block 410 detects high frequency noise in the electrodesignals 402, generates an HF noise indicator representative of a level of high frequency noise detected, and communicates the HF noise indicator to a signal quality monitor block 418. In some examples, the automatic gain control block 412 controls the gain of one or more gain stages in an amplifier cascade to increase the magnitude of the electrode signals 402. The automatic gain control block 412 may also determine whether the electrode signals 402 are experiencing distortion (e.g., soft clipping or hard clipping) and communicate an indicator of signal distortion to the signal quality monitor block 418. In certain examples, the electrode falloff sensing block 414 determines whether the electrodes have fallen off the patient and communicates an electrode contact indicator to the signal quality monitor block 418. The signal quality monitor 418 receives and analyzes the various indicators communicated by the HF noise analyzer 410, the automatic gain control 412, and the electrode falloff sensing block 414 to provide a final signal quality indicator to a decision logic block 420, which is described further below. It should be noted that the signal quality monitor block 418 may also receive electrode quality information from the spectrum analyzer block 408 in the form of a low frequency (LF) noise indicator. It will be appreciated that in this and other examples, the axis analyzer 406, spectrum analyzer 408, HF noise analyzer 410, the automatic gain control 412, the electrode falloff sensing block 414, the signal quality monitor 418, and the rate analyzer 416 may be provided independently of one another. For example, the rate analyzer 416 may receiver the axis rate and spectral rate information from a different source or may determine the information itself. Likewise, the signal quality monitor 418 may receive one or more of the LF noise indicator, HF noise indicator, signal distortion indicator and electrode contact indicator. Further, the signal quality monitor 418 may receive the one or more indicators from different components or may determine the indicators itself. The decision logic 420, in some examples, may receive none or only one or more of the indicators / information shown in Figure 4 in addition to the points and intervals from the QRS detector block 404. In one or more examples, the decision logic 420 is configured to determine one or more of the indicators / information. In general, the detector 316 receives electrode signals 402 and may determine fiducial points and intervals, and one or more of a measure of heart rate, an electrical axis of the heart, a measure of noise, and a measure of distortion.

[0095] In some examples, the QRS detector block 404, axis analyzer 406, and spectrum analyzer 408 each analyze the incoming electrode signals 402, generate heart rate information based thereon, and communicate the heart rate information to a rate analyzer block 416 and the decisionlogic 420. For instance, in certain examples, the QRS detector block 404 determines the heart rate of the patient based on the electrode signals 402. The QRS detector block 404 may also determine heart rate stability of the patient based on heart rate changes. The QRS detector block 404 may also communicate the determined heart rate and heart rate stability to the rate analyzer block 416. The QRS block may further generate fiducial parameters used to discriminate between types of TAs using the processes described further below with reference to FIGS. 5-7B. In some examples, the axis analyzer block 406 determines an electrical axis of the heart of the patient using various morphology techniques. The axis analyzer block 406 may also determine and communicate an axis rate to the rate analyzer block 416. In addition, the axis analyzer block 406 may determine a vector of the electrical axis of the heart and communicate, to the decision logic block 420, heart axis information including an axis validity signal. This heart axis information may be used by the decision logic block 420 to determine a type of arrhythmia presented by the patient, as described further below. In some examples, the spectrum analyzer 408 measures and evaluates frequency components of the electrode signals 402. The spectrum analyzer 408 may transform the electrode information into the frequency domain by, for example, a Fast Fourier Transform (FFT). The spectrum analyzer 408 may also determine a spectral heart rate that is communicated to the rate analyzer block 416.

[0096] In some examples, the rate analyzer block 416 receives heart rate information from the QRS detector block 404, the axis analyzer block 406, and the spectrum analyzer block 408. In these examples, the rate analyzer block 416 determines the patient’s heart rate based on the heart rate information and communicates an indicator of the heart rate to the decision logic block 420. For instance, in examples where signals from multiple pairs of electrodes (e.g., side-side electrode pair and front -back electrode pair) are available, the rate analyzer block 416 determines a heart rate of the patient by determining whether heart rates from the QRS detector 404 are equal across the electrode pairs. If the rates are equal, the rate analyzer block 416 determines that the heart rate from the QRS detector block 404 is the proper rate. However, if the rate stability signal is unsteady or if the rates across the electrode pairs differ, the rate analyzer block 416 may refer to the axis rate from the axis analyzer 406 or spectral rate from the spectrum analyzer 408 to determine the proper heart rate. The rate analyzer block 416 can track the stability of the axis rate and the spectral rate to determine the reliability of the respective rates. In addition, the rate analyzer 416 may reevaluate the rate inputs individually and independently or in comparison to one another.

[0097] In some examples, the decision logic 420 receives the indicator of a heart rate from the rate analyzer 416, the indicator of axis validity from the axis analyzer 406, and the signal quality indicator from the signal quality monitor 418. The decision logic 420 employs one or more of the heart rate, heart axis, and signal quality information to determine whether the patient is presenting with an arrhythmia and / or identify the type of arrhythmia. For example, the decision logic block 420 may use the signal quality indicator to determine the reliability of the heart axis information and / or heart rate information received from the axis analyzer 406 and rate analyzer 416, respectively. For example, the decision logic 420 may ignore information from the axis analyzer 406 and / or the rate analyzer 416 while the ECG signal quality is poor.

[0098] In cases where the electrode signals 402 are of sufficient quality, the decision logic block 420 may determine whether the patient is presenting with an arrhythmia based on the heart axis information and heart rate information. For example, the decision logic 420 may employ the heart rate information to determine whether the patient is experiencing an arrhythmia and employ the heart axis information to discriminate between one or more types of arrhythmias. For instance, a patient having peaks in correlation between an ECG template and the ECG signal of the patient at times different from times of phase zero crossings in the correlation signal is likely experiencing an arrhythmia originating from a heart ventricle. Accordingly, the decision logic block 420 may analyze the axis information to discriminate between arrhythmias originating in a heart ventricle (e.g., ventricular tachycardia and ventricular fibrillation) and arrhythmias originating from other areas of the heart (e.g., supraventricular tachycardia).

[0099] Alternatively or additionally, the decision logic 420 may utilize fiducial parameters received from the QRS detector 404 to discriminate VT from other TAs, such as SVT. FIG. 5 is a class diagram illustrating an object-oriented (OO) implementation 500 of selected portions of a cardiac event detector, such as the detector 316, described above. As shown in FIG. 5, the OO implementation 500 includes an ArrhythmiaDetector class 502, a QrsDetectOb server class 504, a QrsDetector class 506, a QrsChannel class 508, a QrsPeak class 510, and a QrsDetectSubject class 512. In this example, the ArrhythmiaDetector class 502 is a composition of the QrsDetector class 506, the QrsDetector class 506 is a composition of the QrsChannel class 508, and the QrsChannel class 508 class is a composition of the QrsPeak class 510. The QrsPeak class 510 is a subclass of the QrsDetectSubject class 512, and the QrsDetectOb server class 504 is a subclass of the ArrhythmiaDetector class 502. As will be appreciated in light of this disclosure, in a softwareimplementation of the detector 316 of FIGS. 3 and 4 aspects of the QRS detector 404 may be implemented by objects instantiated from the classes 506-510 and aspects of the decision logic 420 may be implemented by objects instantiated from the class 502. In these implementations, code configured to implement the classes 502 and 506-510 may be executed by at least one processor, such as the processor 318 of FIG. 3, to control a medical device to store attributes and execute operations according to the configuration of the classes 502 and 506-510, as described herein.

[0100] In some examples, the QrsDetectSubject class 512 and the QrsDetectOb server class 504 are configured to implement a subject-observer framework that enables transmission of subject fiducial parameters to observers that may further manipulate the fiducial parameters to advantageous effect. As shown in FIG. 5, the QrsDetectServer class 512 defines an observers attribute as a list of references to QrsDetectObservers. Operations defined by the QrsDetectServer class 512 include Registerobserver, and notification operations: NotifyRPeak, NotifySPeak, NotifyRRInterval, and NotifyRSInterval, in some examples. In these examples, the Registerobserver operation is configured to store a reference to a QrsDetectOb server object, passed to the operation as an argument of the Registerobserver operation, within the observers attribute. Each of the notification operations is configured to communicate, upon execution of the notification operation, the fiducial parameter indicated in its name to the list of QrsDetectObservers referred to in the observers attribute. The NotifyRPeak operation is configured to communicate data regarding a detected R-point. The NotifySPeak operation is configured to communicate data regarding a detected S-point. The NotifyRRInterval operation is configured to communicate data regarding a calculated RR interval. The NotifyRSInterval operation is configured to communicate data regarding a calculated RS interval. Each of the notification operations communicates both a time value (e.g., point or interval) of its named fiducial parameter and a lead through which the fiducial parameter was detected. The time value and the lead may be passed to a notification operation via arguments, as shown in FIG. 5.

[0101] In some examples, operations defined by the QrsDetectOb server class 504 include one or more trigger operations, such as OnRPeakChange, OnSPeakChange, OnRRChange, and OnRSChange. Each of the trigger operations is configured to execute processing in response to a change in the fiducial parameter indicated in its name. The OnRPeakChange operation is configured to process an R-point change. The OnSPeakChange operation is configured to processan S-point change. The OnRRChange operation is configured to process an RR interval change. The OnRSChange operation is configured to process an RS interval change. The processing executed may be based on a time value at which the change occurred in the fiducial parameter and a lead through which the change was detected. The time value and the lead may be passed to a trigger operation via arguments, as shown in FIG. 5. The processing executed by the trigger operations varies between examples and may be overwritten within subclasses, such as the ArrhythmiaDetector class 502, that inherit the trigger operations from the QrsDetectOb server class 504.

[0102] In some examples, the ArrhythmiaDetector class 502 defines an m qrsDetector attribute of the QrsDetector class type that is configured to house a member QrsDetector object at runtime. This member QrsDetector object supplies its composite ArrhythmiaDetector object with QRS complex information used by the ArrhythmiaDetector object to identify arrhythmias, such as TAs. The ArrhythmiaDetector class 502 may be further configured to receive, via the subject-observer framework described above, information (e.g., fiducial parameter values) from the QrsDetector object, either of two QrsChannel objects that are members of the QrsDetector object, and either of two QrsPeak objects that are each a member of one of the two QrsChannel objects. In certain examples, operations defined by the ArrhythmiaDetector class 502 include trigger operations: OnRPeakChange, OnSPeakChange, OnRRChange, and OnRSChange, which are inherited from the QrsDetectOb server class 504 in some examples. In these examples, the inherited trigger functions may be overwritten to record the fiducial parameters used to discriminate VT from other TAs. For instance, in some examples, the OnRPeakChange operation is configured to record an R-point and an RR interval, and the OnSPeakChange operation is configured to record an S-point and RS interval. In certain examples, the ArrhythmiaDetector class 502 defines an m dataWriter attribute of the DataWriter class type (not shown) that is configured to house a member DataWriter object at runtime. In these examples, the ArrhythmiaDetector class 502 is configured to interoperate with the DataWriter object to record, in non-transitory storage, data processed via the ArrhythmiaDetector class 502. This data can include ECG data, data derived therefrom, arrhythmia data, data descriptive of fiducial parameters, etc. The DataWriter object can be configured to record data in a variety of formats, such as comma delimited (CSV) format, fixed-length text format, binary format, etc.

[0103] In some examples, the QrsDetector class 506 defines an m_pQrsChannel attribute that is an array of elements of the QrsPeak class type. The m pQrsChanncl attribute is configured to house a member QrsPeak object at runtime for each lead being monitored. These member QrsPeak objects supply a composite QrsDetector object with fiducial parameters detected in ECG data acquired via a lead associated with the QrsPeak object. In certain examples, operations defined by the QrsDetector class 506 include RegisterQrsDetectOb server, which may be inherited from the QrsDetectSubject class 512 in some examples.

[0104] In some examples, the QrsChannel class 508 defines an m lead attribute and a m qrsPeak attribute and of the QrsPeak class type that is configured to house a member QrsPeak object at runtime. This member QrsPeak object supplies its composite QrsChannel object with fiducial parameters detected in ECG data acquired via lead associated with the QrsChannel object and specified by the m lead attribute of the QrsChannel object. In certain examples, operations defined by the QrsChannel class 508 include RegisterQrsDetectOb server, which may be inherited from the QrsDetectSubject class 512 in some examples.

[0105] In some examples, the QrsPeak class 510 is configured to monitor ECG data acquired via individual leads for fiducial points, to calculate fiducial parameters based thereon, and to communicate the calculated fiducial parameters to registered QrsDetectOb server objects. As shown in FIG. 5, QrsPeak class 510 defines the following attributes: m rPeak, m sPeak, m rrlnterval, m rslnterval, and m lead. The m rPeak is configured to store data specifying an R- point. The m sPeak is configured to store data specifying an S-point. The m rrlnterval is configured to store data specifying an RR interval. The m rslnterval is configured to store data specifying an RS interval. The m lead is configured to store data identifying the individual lead being monitored. In certain examples, operations defined by the QrsPeak class 510 include QrsPeak, RegisterQrsDetectObserver, Run, and notification operations: NotifyRPeak, NotifySPeak, NotifyRRInterval, and NotifyRSInterval, which are inherited from the QrsDetectSubject class 512 in some examples. In these examples, the QrsPeak operation is configured to set a threshold value to a value, passed to the operation as an argument of the QrsPeak operation, for a lead specified in an argument of the QrsPeak operation. The threshold value may be used by a QrsPeak object to identify fiducial points in ECG data. The Run operation is configured to set a QrsPeak object to use or not use, depending on a Boolean value specified asan argument to the Run operation, a QrsFilter referenced by another argument to the Run operation, when processing ECG data.

[0106] Turning now to FIG. 6, a process 600 to generate fiducial parameters is illustrated as a flow diagram. The process 600 may be executed by at least one processor (e.g., the processor 318 of FIG. 3) under control of code (e.g., if any part of the detector 316 of FIG. 3 is implemented in software) and / or by specialized circuitry (e.g., if any part of the detector 316 is implemented in special -purpose hardware). The fiducial parameters generated by the process 600 can be used, for example, by decision logic (e.g., the decision logic 420 of FIG. 4) to discriminate VT from other TAs. For instance, the decision logic may calculate an RS / RR ratio and compare the ratio to a threshold value to discriminate VT from other treatment-inadvisable TAs, such as SVT. In certain examples, the processor executes the process 600 by implementing a QrsDetector object based on the QrsDetector class 506 of FIG. 5. In some examples, the process 600 continuously receives and analyzes samples of ECG data derived from ECG signals acquired from a patient.

[0107] As shown in FIG. 6, the process 600 starts with the processor receiving 602 an ECG data sample derived from ECG signals acquired from a patient. The ECG data sample may include a set of ECG data values digitized from the ECG signals. The ECG data sample may include filtered (e.g., by scaling filters, but not a derivative filter) and / or unfiltered ECG data. It should be noted that, in some cases, even filtered ECG data samples may include artifacts due to noise or other interference.

[0108] Continuing with the process 600, the processor attempts to detect 604 R-points and / or S- points in the ECG data sample. For instance, in some examples, the processor executes a fiducial point detection process. FIGS. 7A-7B illustrate one example of a fiducial point detection process 700 that is executed by the processor in some examples. In certain examples, the processor executes the process 700 by implementing a QrsPeak object based on the QrsPeak class 510 of FIG. 5.

[0109] As shown in FIG. 7A, the process 700 starts with the processor determining 702 whether the sample received in operation 602 of FIG. 6 includes a local maximum. For instance, in some examples, the processor calculates a slope of the ECG data and detects a local maximum where the slope transitions from positive or zero value to a negative value (e.g., executes a first derivative and second derivative test). If the processor determines that the sample does not include a localmaximum, the processor proceeds to an operation 706. If the processor determines that the sample includes a local maximum, the processor proceeds to an operation 704.

[0110] Continuing with the process 700, the processor determines 704 whether an amplitude of the local maximum exceeds a configurable threshold value associated with R-points in unfiltered samples. For instance, within the operation 704 the processor may compare the amplitude to an R- point threshold value within a range of 0.5 millivolts to 4 millivolts. Alternatively or additionally, in some examples, within the operation 704 the processor calculates a moving average and uses the moving average as the R-point threshold value. For instance, in some of these examples, the processor identifies one or more maximum amplitudes within one or more previously acquired ECG segments and sets the R-point threshold value to be a percentage (e.g., 25%) of the average of the maximum amplitudes. The use of a moving average to set a threshold value for identifying points, such as the R-point, may be applied in other example R-point and S-point identification processes. The number of previously acquired segments referenced by the processor when determining the moving average can vary, with some examples being 2 segments, 3 segments, 4 segments, 5 segments, 6 segments, or more. Similarly, durations of previously acquired segments can vary, with some examples being 1 second, 2 seconds, 3 seconds, 4 seconds, or more. In addition, in some examples, if a maximum amplitude detected within a previously acquired segment falls below a minimum threshold value (e.g., 0.001 millivolts), the processor assigns a value of 0.001 millivolts to be the maximum amplitude for the segment. Regardless of the origin of the R-point threshold value, if the processor determines that the amplitude of the local maximum does not exceed the R-point threshold value, the processor proceeds to an operation 722 of FIG. 7B. If the processor determines that the local maximum exceeds the R-point threshold value, the processor determines that the local maximum is a unfiltered-sample R-point and proceeds to an operation 708.

[0111] Continuing with the process 700, the processor determines 708 whether an R-point identification flag (e.g., stored in a memory accessible to the processor) is set. For instance, in some examples, the processor accesses a memory location storing a value for the flag and determines whether the flag is set to a 0 or a 1. If the processor determines that the value is 1, the processor determines that the flag is set and proceeds to operation 712. If the processor determines that the value is 0, the processor determines that the flag is not set and proceeds to operation 710.

[0112] Continuing with the process 700, the processor stores 710 the local maximum as a candidate R-point and proceeds to the operation 722 of FIG. 7B. For instance, in some examples of the operation 710, the processor stores the local maximum within a data structure allocated in the memory and configured to house fiducial points.

[0113] Continuing with the process 700 and returning to the operation 706, the processor determines 706 whether the sample received in operation 602 of FIG. 6 includes a local minimum. For instance, in some examples, the processor calculates a slope of the ECG data and detects a local minimum where the slope transitions from negative or zero value to a positive value (e.g., executes a first derivative and second derivative test). If the processor determines that the sample does not include a local minimum, the processor proceeds to the operation 722 of FIG. 7B. If the processor determines that the sample includes a local minimum, the processor proceeds to an operation 720.

[0114] Continuing with the process 700, the processor stores 720 the local minimum as a candidate S-point and proceeds to the operation 722 of FIG. 7B. For instance, in some examples of the operation 720, the processor stores the local minimum within a data structure allocated in the memory and configured to house fiducial points.

[0115] Continuing with the process 700, the processor initiates a subprocess in which identification of an S-point to associate with an actual R-point is attempted. This subprocess starts with the processor retrieving 712 two candidate S-points from a sequence of candidate S-points accumulated via the operation 720. These two S-points include a target S-point and a candidate S- point immediately preceding (in time) the target S-point in the sequence. For instance, in some examples, the processor reads the target S-point and the preceding S-point from a data structure allocated in the memory and configured to house a sequence of fiducial points. In some examples, if no S-point precedes the target S-point in the sequence, the processor fabricates a fictitious S- point with a timing set to zero and uses the fictitious S-point as the preceding S-point in subsequent processing. In other examples, if no S-point precedes the target S-point in the sequence, the processor reads only the target S-point and, nevertheless, proceeds to the operation 714.

[0116] Continuing with the process 700, the processor determines 714 whether a most recently detected actual R-point falls temporally between the preceding S-point (if such exists) and the target S-point. Detection of actual R-points is described further below with reference to operation 734 of FIG. 7B. For instance, in some examples, the processor calculates a first difference bysubtracting the timing of the actual R-point from the timing of the target S-point and a second difference by subtracting the timing of the actual R-point from the timing of the preceding S-point (or a value of zero, if no preceding S-point exists). In these examples, if the first difference is positive and the second difference is negative, the processor determines that the actual R-point falls between the preceding S-point and the target S-point and proceeds to operation 718. If the first difference is not positive or the second difference is not negative, the processor determines that the actual R-point does not fall between the preceding S-point and the target S-point and proceeds to operation 716.

[0117] Continuing with the process 700, the processor unsets 718 the R-point flag and stores the target S-point as an actual S-point. For instance, in some examples, the processor accesses a memory location storing a value for the flag and stores a value of 0 in the memory location. Further, in some examples, within the operation 718 the processor accesses a data structure allocated in the memory and configured to store S-points and stores, within the data structure, the target S-point as an actual S-point. Subsequent to execution of the operation 718, the processor proceeds to the operation 722 of FIG. 7B.

[0118] Continuing with the process 700, the processor determines 716 whether the target S-point is the last candidate S-point. For instance, in some examples, the processor attempts to access a next target S-point adjacent to the target S-point in the sequence of S-points. If the processor succeeds in accessing the next target S-point, the processor determines that the target S-point is not the last candidate S-point and returns to the operation 712 to retrieve the next target S-point and an S-point preceding the next target S-point for subsequent processing. If the processor fails to access the next target S-point, the processor determines that the target S-point is the last candidate S-point and proceeds to the operation 722 of FIG. 7B.

[0119] Continuing with the process 700 with reference to FIG. 7B, the processor applies 722 a derivative filter to the sample of ECG data. The derivative filter may be useful to identify R-points (i.e., filtered-sample R-points) within the filtered sample.

[0120] Continuing with the process 700, the processor determining 724 whether the filtered sample includes a local maximum. For instance, in some examples, the processor calculates a slope of the ECG data and detects a local maximum where the slope transitions from positive or zero value to a negative value (e.g., executes a first derivative and second derivative test). If the processor determines that the filtered sample does not include a local maximum, the process 700may end. Tf the processor determines that the sample includes a local maximum, the processor proceeds to an operation 726.

[0121] Continuing with the process 700, the processor determines 726 whether an amplitude of the local maximum exceeds a configurable threshold value associated with filtered-sample R- points. For instance, within the operation 708 the processor may compare the amplitude to an R- point threshold value within a range of 0.5 millivolts to 4 millivolts. Alternatively or additionally, in some examples, within the operation 704 the processor calculates a moving average and uses the moving average as the R-point threshold value. For instance, in some of these examples, the processor identifies one or more maximum amplitudes within one or more previously acquired ECG segments and sets the R-point threshold value to be a percentage (e.g., 25%) of the average of the maximum amplitudes. The number of previously acquired segments referenced by the processor when determining the moving average can vary, with some examples being 2 segments, 3 segments, 4 segments, 5 segments, 6 segments, or more. Similarly, durations of previously acquired segments can vary, with some examples being 1 second, 2 seconds, 3 seconds, 4 seconds, or more. In addition, in some examples, if a maximum amplitude detected within a previously acquired segment falls below a minimum threshold value (e.g., 0.001 millivolts), the processor assigns a value of 0.001 millivolts to be the maximum amplitude for the segment. Regardless of the origin of the R-point threshold value, if the processor determines that the amplitude of the local maximum does not exceed the R-point threshold value, the process 700 may end. If the processor determines that the local maximum exceeds the R-point threshold value, the processor determines that the local maximum is a filtered-sample R-point and proceeds to an operation 728.

[0122] Continuing with the process 700, the processor initiates a subprocess in which a mapping of the filtered-sample R-point to a target R-point in the unfiltered sample is attempted. This subprocess starts with the processor retrieving 728 a target R-point from a sequence of candidate R-points accumulated via the operation 712. For instance, in some examples, the processor reads the target R-point from a data structure allocated in the memory and configured to house a sequence of fiducial points.

[0123] Continuing with the process 700, the processor determines 730 whether a difference in timing between the target R-point and the filtered-sample R-point is less than a threshold value. For instance, in some examples, the processor calculates the difference by subtracting the timing of the filtered-sample R-point from the timing of the target R-point. In these examples, if theabsolute value of the difference is less than a time falling within a range of 0.1 seconds to 0.15 seconds, the processor determines that the target R-point corresponds to the f tered- sample R- point and proceeds to operation 734. If the absolute value of the difference is not less than the a time falling within a range of 0.1 seconds to 0.15 seconds, the processor determines that the target R-point does not correspond to the filtered-sample R-point and proceeds to operation 732. It should be noted the ranges of threshold values listed above will vary depending on the rate at which ECG signals are sampled and any delay caused in fdtering the ECG signals.

[0124] Continuing with the process 700, the processor sets 734 the R-point flag and stores the target R-point as an actual R-point. For instance, in some examples, the processor accesses a memory location storing a value for the flag and stores a value of 1 in the memory location. Further, in some examples, within the operation 734 the processor accesses a data structure allocated in the memory and configured to store R-points and stores, within the data structure, the target R-point as an actual R-point. Subsequent to execution of the operation 734, the process 700 may end.

[0125] Continuing with the process 700, the processor determines 732 whether the target R-point is the last candidate R-point. For instance, in some examples, the processor attempts to access a next target R-point adjacent to the target R-point in the sequence of R-points. If the processor succeeds in accessing the next target R-point, the processor determines that the target R-point is not the last candidate R-point and returns to the operation 728 to retrieve the next target R-point for subsequent processing. If the processor fails to access the next target R-point, the processor determines that the target R-point is the last candidate R-point and that the process 700 may end.

[0126] Returning to the process 600, if the processor detected 606 an actual R-point, the processor calculates 610 an RR interval based on the detected R-point and another, previously detected actual R-point. In some instances, the previously detected R-point is adjacent to the detected R-point within a sequence of R-points detected in ECG data samples from the patient. In this case, the RR interval counts as a single heartbeat for rate determination purposes but spans two distinct heartbeats (which center on the QSR complex) of the patient. It should be noted, however, that the RR interval calculated by the operation 610 may span more heartbeats of the patient, in some instances. For example, one or more intermediate R-points may reside between the previously detected R-point and the detected R-point within the sequence of R-points detected in ECG data samples from the patient. Additionally or alternatively, in some examples, the processor willdiscard any RR interval calculated to span more than a configurable threshold value (e.g., 3.5 seconds, 4 seconds, 4.5 seconds, 5 seconds, etc.).

[0127] Continuing with the process 600, if the processor detected 608 an actual S-point, the processor calculates 612 an RS interval based on the detected S-point and a previously detected actual R-point. In some instances, the previously detected R-point and the actual S-point are associated with a single QRS complex detected in ECG data samples from the patient. In this case, the RS interval resides within one heartbeat of the patient. It should be noted, however, that the RS interval calculated by the operation 612 may span more than a single heartbeat of the patient, in some instances. For example, one or more intermediate S-points may reside between the previously detected R-point and the detected S-point within the sequence of fiducial points detected in ECG data samples from the patient. Additionally or alternatively, in some examples, the processor will discard any RS interval calculated to span more than a configurable threshold value (e.g., 3.5 seconds, 4 seconds, 4.5 seconds, 5 seconds, etc.).

[0128] Subsequent to the operations 610 and / or 612, the process 600 may end.

[0129] It should be noted that the process described with reference to FIGS. 6-7B can be adapted to identify other fiducial points (e.g., P-points, Q-points, T-points) within filtered and unfiltered ECG data samples and to calculate other fiducial parameters (e.g., the fiducial points and / or ECG intervals therebetween, such as PR intervals, PR segments, QRS complexes, QT intervals, ST segments, slopes of segments and / or intervals, etc.).

[0130] Turning now to FIG. 8, an ECG analysis timeline 800 is illustrated. The timeline 800 shows an example scenario in which the TA discrimination technology disclosed herein impacts analysis and / or treatment of an arrhythmia presented by a patient. As shown in FIG. 8, the timeline 800 includes timepoints 802A-8020. In this example, a medical device, such as any of the medical devices illustrated below with reference to FIGS. 14A-14D, initially detects an arrhythmia and declares treatment of the arrhythmia to be advisable using a rate-based arrhythmia detector but eventually withdraws the declaration of treatment advisability based on analysis performed using the TA discrimination technology disclosed herein.

[0131] As shown in FIG. 8, the timeline 800 includes a sequence of validation periods 1 through N. During individual validation periods, the processor attempts the validate that treatment is advisable for the detected arrhythmia by collecting fiducial parameters based on fiducial points and analyzing the same using a VT discrimination process, such as explained further below withreference to FIG. 9. As shown in FIG. 8, the VT discrimination process analyzes a sample of fiducial parameters associated with each validation period separately to generate a statistic for the validation period and uses the statistic to determine whether treatment is advisable for the detected arrhythmia.

[0132] At timepoint 802A the medical device, which incorporates a medical device controller with TA discrimination technology, such as the medical device controller 300 described above with reference to FIG. 3, detects and declares a treatment-advisable arrhythmia condition in a patient being monitored by the medical device. This declaration may be issued, for example, by a processor (e.g., the processor 318 of FIG. 3). In declaring the treatment-advisable arrhythmia, the processor may operate under control of code (e.g., if any part of the detector 316 of FIG. 3 is implemented in software) and / or may include specialized circuitry (e.g., if any part of the detector 316 is implemented in special-purpose hardware). Regardless of its physical implementation, the processor may detect and declare the treatment-advisable arrhythmia based on heart rate indicators communicated by a heart rate analyzer (e.g., the rate analyzer 416 of FIG. 4) to the processor. Upon declaration of the arrhythmia condition, the processor may initiate collection of fiducial parameters, such as R-points, S-points, RR intervals, and RS intervals as described above. The processor may continue to collect the fiducial parameters until a first validation period (e.g., validation period 1) ends at timepoint 802B. The first validation period may span a duration of 8 seconds, 10 seconds, 12 seconds, 15 seconds, or longer in some examples. As such, the validation periods described herein are not limited to particular durations.

[0133] Continuing with the timeline 800, at timepoint 802B the processor may calculate, based on the collected fiducial parameters, one or more statistics that summarize some or all of the collected fiducial parameters. For instance, in some examples, the processor determines a median value of RS / RR ratios measured during the first validation period and compares the median value to a threshold value (e.g., 0.2) that demarcates RS / RR ratios indicative of a treatment-advisable TA, such as VT, from RS / RR ratios indicative of a treatment-inadvisable TA, such as SVT. In this example, the processor determines that the median value of RS / RR ratios measured during the first validation period is less than the threshold value and, as such, declares the TA to be treatment- inadvisable (e.g., SVT or some TA other than VT). After making this determination, the processor initiates collection of fiducial parameters for a next validation period (e.g., validation period 2)and continues to collect the fiducial parameters until the next validation period ends at timepoint 802C.

[0134] Continuing with the timeline 800, at timepoint 802C the processor may again calculate, based on the fiducial parameters collected during the next validation period, one or more statistics that summarize some or all of the collected fiducial parameters. For instance, in some examples, the processor determines a median value of RS / RR ratios measured during the next validation period and compares the median value to a threshold value (e.g., 0.2) that demarcates RS / RR ratios indicative of a treatment-advisable TA, such as VT, from RS / RR ratios indicative of a treatment- inadvisable TA, such as SVT. In this example, the processor determines that the median value of RS / RR ratios measured during the next validation period is less than the threshold value and, as such, declares treatment to be inadvisable for the detected TA. After making this determination, the processor initiates collection of fiducial parameters for the next validation period (not shown) and continues to collect the fiducial parameters until the next validation period ends at the next timepoint. In this example, the processor continues to execute the processing described above with reference to 802C for a configurable number of validation periods (e.g., N periods in FIG. 8). The number of validation periods varies between examples, with some examples including 3 periods, 4 periods, or 5 or more periods. At the end of the validation periods, if no treatment-advisable TA’s were declared, the processor withdraws the treatment-advisable arrhythmia declaration and continues to monitor the patient.

[0135] Turning now to FIG. 9, an arrhythmia analysis process 900 is illustrated that employs TA discrimination to advantageous effect. As will be appreciated in view of this disclosure, some examples of the process 900 may produce a timeline like the timeline 800 of FIG. 8. The process 900 may be executed by a processor (e.g., the processor 318 of FIG. 3). The processor may operate under control of code (e.g., if any part of the detector 316 of FIG. 3 is implemented in software) and / or may include specialized circuitry (e.g., if any part of the detector 316 is implemented in special -purpose hardware). As shown in FIG. 9, the process 900 starts with the processor declaring 902 that the patient is presenting with an apparent arrhythmia (e.g., a TA) for which treatment is advisable. For instance, in some examples, the processor declares an arrhythmia based on a heart rate indicator generated by a rate analyzer (e.g. the rate analyzer 416 of FIG. 4) as described above.

[0136] Continuing with the process 900, the processor starts 904 a validation timer configured to expire at the end of a validation time period during which the processor attempts to validate thatthe arrhythmia is treatment-advisable (e.g., via electrotherapy) or to determine that the arrhythmia is treatment-inadvisable. For instance, in various examples, the validation timer is configured to expire in 10 seconds, 20 seconds, 30 seconds, 45 seconds, or 1 minute.

[0137] Continuing with the process 900, the processor collects 906 a sample of RS / RR ratios during a portion of the validation period. For instance, in some examples, the processor acquires ECG signals during the portion of the validation period and generates fiducial parameters based on the acquired ECG signals using any of the various technologies described herein. The processor then calculates the sample of RS / RR ratios based on the generated fiducial parameters. The duration of the portion of the validation period, during which the RS / RR ratios are collected, may vary between examples. Examples of the duration of the portion include 1 second, 5 seconds, 10 seconds, 15 seconds, and 30 seconds. It should be noted that, in some examples, the operation 906 consumes the entire validation period, rather than a portion thereof.

[0138] Continuing with the process 900, the processor calculates 908 one or more statistics based on the sample of RS / RR ratios. These statistics may include any of the statistics described herein. For instance, in some examples, the processor determines a median value of the RS / RR ratios within the operation 908.

[0139] Continuing with the process 900, the processor determines 910 whether the arrhythmia declared in the operation 902 is treatment-advisable based on a tachyarrhythmic discrimination metric. The process used to make this determination varies between examples and the statistics calculated in the operation 908. For instance, in some examples, the processor determines whether the arrhythmia is treatment-advisable (e g., discriminates VT from other TAs) via threshold analysis. In at least one example, the processor determines that treatment is inadvisable for the arrhythmia if the median value of the RS / RR ratios determined in the operation 908 is less than 0.2 or some other threshold. If the processor determines that treatment is advisable for the detected arrhythmia, the processor stores 912 a record of the same in the memory and, optionally, initiates a treatment protocol (e.g., where the processor is a part of a medical treatment device, such as the medical devices described below with reference to FIGS. 14A and 14B), and the process 900 may end. If the processor determines that the arrhythmia is treatment-inadvisable, the processor proceeds to operation 914.

[0140] It should be noted that in some examples of the process 900, the processor executes threshold analysis for multiple leads within the operation 910, where the medical device providesthe same. In these situations, the processor declares an arrhythmia as treatment-advisable if the median value associated with either lead is 0.2 or greater or if no median values are available via either lead (e.g., due to noise).

[0141] Continuing with the process 900, the processor determines 914 whether the validation timer has expired. For instance, in some examples, the processor retrieves the current time from a system clock accessible to the processor and compares the current time to the validation period expiration time set in the operation 904. In these examples, if the current time is equal to or exceeds the validation period expiration time, the processor determines that the validation timer has expired and proceeds to an operation 916. Further, in these examples, if the current time is less than the validation period expiration time, the processor determines that the validation timer has not expired and returns to the operation 906 to collect another sample of RS / RR ratios.

[0142] Continuing with the process 900, the processor records 916 withdrawal of the declaration of the treatment-advisable arrhythmia declared in the operation 902. For instance, in some examples, the processor stores a record in the memory that indicates advisability of the declared arrhythmia was unable to be validated, thus indicating that the arrhythmia was not, for example, VT, and the process 900 may end. In other examples, the process 900 may not use a validation timer. For example, the process may analyze a predetermined or variable number of samples or ECG segments. The process illustrated in Figure 9 may be provided by the processor independent of the other processes or functionality described herein.

[0143] Turning now to FIG. 10, an ECG analysis timeline 1000 is illustrated. The timeline 1000 shows an example scenario in which the TA discrimination technology disclosed herein impacts analysis and / or treatment of an arrhythmia presented by a patient. As shown in FIG. 10, the timeline 1000 includes timepoints 1002A-1002Z. In this example, a medical device, such as any of the medical devices illustrated below with reference to FIGS. 14A-14D, initially detects an arrhythmia and declares treatment of the arrhythmia to be advisable using a rate-based arrhythmia detector but eventually withdraws the declaration of treatment advisability based on analysis performed using the TA discrimination technology disclosed herein.

[0144] As shown in FIG. 10, the timeline 1000 includes a sequence of collection windows 1 through M and a sequence of validation periods 1 through N. During individual collection windows, the processor collects pre-arrythmia (e.g., normal) fiducial parameters of a patient. As illustrated in the timeline 1000, the collection windows stop and validation periods begin when anarrhythmia is detected. In some examples, such as those illustrated by FIG. 10, treatment of the arrhythmia is presumed to be advisable, in which case a treatment-advisable arrhythmia is declared. During individual validation periods, the processor attempts the validate the declared arrhythmia as being treatment-advisable by collecting post-arrythmia fiducial parameters and analyzing the same using a VT discrimination process explained further below with reference to FIG. 11. As shown in FIG. 10, the VT discrimination process analyzes a sample of fiducial parameters associated with each validation period separately to generate a statistic for the validation period and uses the statistic to determine whether to validate the declared arrhythmia as treatment-advi sable.

[0145] At timepoint 1002A the medical device, which incorporates a medical device controller with TA discrimination technology, such as the medical device controller 300 described above with reference to FIG. 3, begins collecting fiducial parameters of a patient being monitored by the medical device. The fiducial parameters may be collected, for example, by a processor (e.g., the processor 318 of FIG. 3). In collecting the parameters, the processor may operate under control of code (e.g., if any part of the detector 316 of FIG. 3 is implemented in software) and / or may include specialized circuitry (e.g., if any part of the detector 316 is implemented in special-purpose hardware). Regardless of its physical implementation, the processor may collect fiducial parameters, such as R-points, S-points, RR intervals, and RS intervals as described above. The processor may continue to collect the fiducial parameters until a first collection window (e.g., collection window 1) ends at timepoint 1002B. The first collection window may span a duration of 5 seconds, 8 seconds, 10 seconds, or longer, in some examples. As such, the examples disclosed herein are not limited to collection windows having a particular duration.

[0146] Continuing with the timeline 1000, at timepoint 1002B the processor may calculate, based on the collected fiducial parameters, one or more statistics that summarize some or all of the collected fiducial parameters. For instance, in some examples, the processor determines a median value of RS / RR ratios measured during the first collection window and stores the median value in memory for subsequent processing. In this example, after storing the median value, the processor initiates collection of fiducial parameters for a next collection window (e.g., collection window 2) and continues to collect the fiducial parameters until the next collection window ends at timepoint 1002C.

[0147] Continuing with the timeline 1000, at timepoint 1002C the processor may again calculate, based on the fiducial parameters collected during the next collection window, one or more statistics that summarize some or all of the collected fiducial parameters. For instance, in some examples, the processor determines a median value of RS / RR ratios measured during the next collection window and stores the median value in memory for subsequent processing. In this example, after storing the median value, the processor initiates collection of fiducial parameters for the next collection window (not shown) and continues to collect the fiducial parameters until the next collection window ends at the next timepoint. In this example, the processor continues to execute the processing described above with reference to 1002C for a configurable number of collection windows. In some examples, the median values are stored in a circular data structure (e.g., a queue) that limits the number of stored median values to 7.

[0148] Continuing with the timeline 1000, at timepoint 1002P the processor detects and declares an arrhythmia condition in a patient being monitored by the medical device. For instance, in some examples, the processor detects and declares the treatment-advisable arrhythmia based on heart rate indicators communicated by a heart rate analyzer (e.g., the rate analyzer 416 of FIG. 4) to the processor. Upon declaration of the treatment-advisable arrhythmia condition, the processor initiates collection of fiducial parameters, such as R-points, S-points, RR intervals, and RS intervals as described above. The processor may continue to collect the fiducial parameters until a first validation period (e.g., validation period 1) ends at timepoint 1002Q. The first validation period may span a duration of 10 seconds, although the examples described herein are not limited to validation periods that span a particular duration, as described above. At timepoint 1002P the processor may also determine a median value of the stored median values accumulated via the collection windows and store this determined median value as a pre-arrhythmia median value. It should be noted that, in some examples, if the amount of time between the start of the timeline 1000 (e.g., the timepoint 1002A) and arrhythmia declaration (e.g., the timepoint 1002P) is insufficient to collect the configurable number of collection windows, the median value of the stored median values will be based on a number of collection windows that is fewer than the configurable number of collection windows described above.

[0149] Continuing with the timeline 1000, at timepoint 1002Q the processor may calculate, based on the collected fiducial parameters, one or more statistics that summarize some or all of the collected fiducial parameters. For instance, in some examples, the processor determines a medianvalue of RS / RR ratios measured during the first validation period and compares this median value to the pre-arrhythmia median value. In at least one example, the processor makes this comparison by calculating a ratio of the median value to the pre-arrhythmia median value and comparing the ratio to a threshold value (e.g., 1.5). In this example, the processor determines that the ratio is less than the threshold value and, as such, declares the TA to be treatment-inadvisable. After making this determination, the processor initiates collection of fiducial parameters for a next validation period (e.g., validation period 2) and continues to collect the fiducial parameters until the next validation period ends at timepoint 1002R.

[0150] Continuing with the timeline 1000, at timepoint 1002R the processor may again calculate, based on the fiducial parameters collected during the next validation period, one or more statistics that summarize some or all of the collected fiducial parameters. For instance, in some examples, the processor determines a median value of RS / RR ratios measured during the next validation period and compares the median to the pre-arrhythmia median value via the ratio method described above. In this example, the processor determines that the ratio is again less than the threshold value and, as such, declares the TA to be treatment-inadvisable. After making this determination, the processor initiates collection of fiducial parameters for the next validation period (not shown) and continues to collect the fiducial parameters until the next validation period ends at the next timepoint. In this example, the processor continues to execute the processing described above with reference to 1002R for a configurable number of validation periods. At the end of the validation periods, if no treatment-advisable TA’s were declared, the processor withdraws the arrhythmia declaration and continues to monitor the patient. The process illustrated in Figure 10 may be provided by the processor independent of the other processes or functionality described herein.

[0151] Turning now to FIG. 11, an arrhythmia analysis process 1100 is illustrated that employs TA discrimination to advantageous effect. As will be appreciated in view of this disclosure, some examples of the process 1100 may produce a timeline like the timeline 1000 of FIG. 10 described above. The process 1100 may be executed by a processor (e.g., the processor 318 of FIG. 3). The processor may operate under control of code (e.g., if any part of the detector 316 of FIG. 3 is implemented in software) and / or may include specialized circuitry (e.g., if any part of the detector 316 is implemented in special-purpose hardware).

[0152] As shown in FIG. 11, the process 1100 starts with the processor collecting 1102 a sample of RS / RR ratios during a portion of a pre-arrhythmia period (e.g., a collection window). Forinstance, in some examples, the processor acquires ECG signals during the portion of the prearrhythmia period and generates fiducial parameters based on the acquired ECG signals using any of the various technologies described herein. In these examples, the processor next derives the sample of RS / RR ratios from the generated fiducial parameters. The duration of the portion of the pre-arrhythmia period, during which the RS / RR ratios are collected, may vary between examples. Examples of the duration of the portion include 1 second, 5 seconds, 10 seconds, 15 seconds, and 30 seconds. As such, the examples described herein do not limit portion duration to a particular duration.

[0153] Continuing with the process 1100, the processor calculates 1104 one or more statistics based on the sample of RS / RR ratios. These statistics may include any of the statistics described herein. For instance, in some examples, the processor determines a median value of the RS / RR ratios within the operation 1104.

[0154] Continuing with the process 1100, the processor adds 1106 the one or more statistics to a set of pre-arrhythmia statistics. In some examples, the processor stores the one or more statistics within a data structure allocated in memory and configured to store the set of pre-arrhythmia statistics. For instance, in at least one example, median values generated by the operation 1108 are stored in a circular data structure (e.g., a queue) that limits the number of stored median values to 7. The number of median values stored thusly is not limited to a particular number.

[0155] Continuing with the process 1100, the processor determines whether to declare 1108 an apparent, treatment-advisable arrhythmia based on physiological parameters presented by the patient. For instance, in some examples, the processor declares an arrhythmia based on a heart rate indicator generated by a rate analyzer (e.g. the rate analyzer 416 of FIG. 4) as described above. This arrhythmia may be presumed to be treatment-advisable. If the processor does not declare an arrhythmia, the processor returns to the operation 1102 and collects additional samples of RS / RR ratios. If the processor declares an arrhythmia, the processor proceeds to an operation 1110.

[0156] Continuing with the process 1100, the processor calculates 1110 a statistic that summarizes the set of pre-arrhythmia statistics accumulated via the operations 1102-1106. For instance, in some examples, the processor determines a median value of the median values of RS / RR ratios stored within the set of pre-arrhythmia statistics and stores this pre-arrhythmia statistic in memory for subsequent processing.

[0157] Continuing with the process 1100, the processor starts 1 112 a validation timer configured to expire at the end of a validation period during which the processor attempts to validate that the arrhythmia is treatment-advisable (e.g., via electrotherapy) or to determine that the arrhythmia is treatment-inadvisable. For instance, in some examples, the validation period is configured to expire in 10 seconds, 20 seconds, 30 seconds, 45 seconds, or 1 minute.

[0158] Continuing with the process 1100, the processor collects 1114 a sample of RS / RR ratios during a portion of the validation period. For instance, in some examples, the processor acquires ECG signals during the portion of the validation period and generates fiducial parameters based on the acquired ECG signals using any of the various technologies described herein. The processor then calculates the sample of RS / RR ratios based on the generated fiducial parameters. The duration of the portion of the validation period, during which the RS / RR ratios are collected, may vary between examples. Examples of the duration of the portion include 1 second, 5 seconds, 10 seconds, 15 seconds, and 30 seconds. It should be noted that, in some examples, the operation 1106 may consume the entire validation period, rather than a portion thereof.

[0159] Continuing with the process 1100, the processor calculates 1116 one or more postarrhythmia statistics based on the sample of RS / RR ratios. These statistics may include any of the statistics described herein. For instance, in some examples, the processor determines a median value of the RS / RR ratios within the operation 1116 and stores the median value as a postarrhythmia statistic. In these examples, if the processor is unable to calculate a median value (e.g., due to noise), the processor stores a -1 as the post-arrhythmia statistic.

[0160] Continuing with the process 1100, the processor determines 1118 whether the arrhythmia declared in the operation 1102 is treatment-advisable based on a tachyarrhythmic discrimination metric. The process used to make this determination varies between examples and the statistics calculated in the operation 1108. For instance, in some examples, the processor determines whether the arrhythmia is treatment-advisable (e.g., discriminates VT from other TAs) via threshold analysis. In some examples, the processor determines whether the arrhythmia is treatment-advisable by comparing the pre-arrhythmia statistic to the post-arrythmia statistic. In at least one example, the processor makes this comparison by calculating a ratio of the post arrhythmia statistic to the pre-arrhythmia statistic and comparing the ratio to a threshold value (e.g., 1.5). In this example, if the processor determines that the ratio is less than the threshold value, the processor determines that treatment of the detected arrhythmia is inadvisable. Further, in thisexample, if the processor determines that the ratio is equal to or greater than the threshold value, the processor determines that the declared arrhythmia is treatment-advisable. If the processor determines that the arrhythmia is treatment-advisable, the processor stores 1120 a record of the same in the memory and, optionally, initiates a treatment protocol (e.g., where the processor is a part of a medical treatment device, such as the medical devices described below with reference to FIGS. 14A and 14B), and the process 1100 may end. If the processor determines that the arrhythmia is treatment-inadvisable, the processor proceeds to operation 1122.

[0161] It should be noted that in some examples of the process 1100, the processor analyzes ratios of the post arrhythmia statistic and the pre-arrhythmia statistic associated with each of multiple leads within the operation 1118, where the medical device provides the same. In these situations, the processor declares an arrhythmia as treatment-advisable if the ratio associated with either lead is 1.5 or greater or if the ratio associated with either lead is negative (e g., due to noise).

[0162] Continuing with the process 1100, the processor determines 1122 whether the validation timer has expired. For instance, in some examples, the processor retrieves the current time from a system clock accessible to the processor and compares the current time to the validation period expiration time set in the operation 1112. In these examples, if the current time is equal to or exceeds the validation period expiration time, the processor determines that the validation timer has expired and proceeds to an operation 1124. Further, in these examples, if the current time is less than the validation period expiration time, the processor determines that the validation timer has not expired and returns to the operation 1114 to collect another sample of RS / RR ratios.

[0163] Continuing with the process 1100, the processor records 1124 withdrawal of the declaration of the treatment-advisable arrhythmia. For instance, in some examples, the processor stores a record in the memory that indicates treatment advisability for the declared arrhythmia was unable to be validated, thus indicating that the arrhythmia was not, for example, VT, and the process 1100 may end. The process illustrated in Figure 11 may be provided by the processor independent of the other processes or functionality described herein.

[0164] Turning now to FIG. 12, an ECG analysis timeline 1200 is illustrated. The timeline 1200 shows an example scenario in which the TA discrimination technology disclosed herein impacts analysis and / or treatment of an arrhythmia presented by a patient. As shown in FIG. 12, the timeline 1200 includes timepoints 1202A-1202Z. In this example, a medical device, such as any of the medical devices illustrated below with reference to FIGS. 14A-14D, initially detects anarrhythmia and declares treatment of the arrhythmia to be advisable using a rate-based arrhythmia detector but eventually withdraws the declaration of treatment advisability based on analysis performed using the TA discrimination technology disclosed herein.

[0165] As shown in FIG. 12, the timeline 1200 includes a sequence of validation periods 1 through N. During each of the validation periods, the processor attempts the validate the declared arrhythmia as being treatment-advisable by collecting fiducial parameters and analyzing the same using a VT discrimination process, such as explained further below with reference to FIG. 13. As shown in FIG. 12, individual validation periods include ratio detection windows 1 through M. In this example, the processor analyzes the fiducial parameters within each window separately to generate a record for the window, accumulates the records into a group, summarizes the group into a statistic, and uses the statistic to determine whether to validate the declared arrhythmia as treatment-advi sable.

[0166] At timepoint 1202A the medical device, which incorporates a medical device controller with TA discrimination technology, such as the medical device controller 300 described above with reference to FIG. 3, detects and declares a treatment-advisable arrhythmia condition in a patient being monitored by the medical device. The arrhythmia declaration may be issued, for example, by a processor (e.g., the processor 318 of FIG. 3). In declaring the arrhythmia, the processor may operate under control of code (e.g., if any part of the detector 316 of FIG. 3 is implemented in software) and / or may include specialized circuitry (e.g., if any part of the detector 316 is implemented in special-purpose hardware). Regardless of its physical implementation, the processor may detect and declare the treatment-advisable arrhythmia based on heart rate indicators communicated by a heart rate analyzer (e.g., the rate analyzer 416 of FIG. 4) to the processor. Upon declaration of the arrhythmia condition, the processor may initiate collection of fiducial parameters, such as R-points, S-points, RR intervals, and RS intervals as described above. The processor may continue to collect the fiducial parameters until a first detection window (e.g., detection window 1) ends at timepoint 1202B. The first detection window may span a duration of 1 second, 2 seconds, or 3 or more seconds in various examples. As such, the durations of detection windows utilized in any given example is not limited a particular duration.

[0167] Continuing with the timeline 1200, at timepoint 1202B the processor may calculate, based on the collected fiducial parameters, one or more statistics that summarize some or all of the collected fiducial parameters. These statistics may be any of the statistics described herein. Forinstance, in some examples, the processor attempts to calculate at least one RS / RR ratio measured during the first detection window and stores a value indicative of success (e.g., 1) or failure (e.g., 0) in memory for subsequent processing. In this example, the processor stores a 1 indicating that at least one RS / RR ratio was successfully calculated for the first detection window. After attempting the calculation and recording the result of the attempt, the processor initiates collection of fiducial parameters for a next detection window (e.g., detection window 2) and continues to collect the fiducial parameters until the next detection window ends at timepoint 1202C.

[0168] Continuing with the timeline 1200, at timepoint 1202C the processor may again calculate, based on the fiducial parameters collected during the next detection window, one or more statistics that summarize some or all of the collected fiducial parameters. For instance, in some examples, the processor attempts to calculate at least one RS / RR ratio measured during the next detection window. In this example, the processor stores a 1 indicating that at least one RS / RR ratio was successfully calculated for the next detection window. After making this determination, the processor initiates collection of fiducial parameters for the next detection window (not shown) and continues to collect the fiducial parameters until the next detection window ends at the next timepoint. In this example, the processor continues to execute the processing described above with reference to 1202C for a configurable number of detection windows (e.g., 10, in some examples).

[0169] Continuing with the timeline 1200, at timepoint 1202N the processor may calculate, based on the records accumulated in the detection windows, one or more statistics that characterize a rate of success of calculating the fiducial parameters. For instance, in some examples, the processor calculates a mean value of the records accumulated in the detection windows and compares the mean value to a threshold value (e.g., 0.8) that represents a demarcation value below which the initial arrhythmia declaration is validated. This demarcation value reflects the understanding that the RS / RR ratio will be difficult to calculate regularly when the patient is presenting with VT or VF. In this example, the processor determines that the mean value of the records is greater than the threshold value and, as such, declares treatment of the TA to be inadvisable. After making this determination, the processor initiates collection of fiducial parameters and accumulation of records for detection windows within a next validation period (not shown) and continues to collect the fiducial parameters and accumulate records for detection windows until the next validation period ends. The validation period processing described above for the next validation period repeats until validation period N ends at timepoint 1202Z. At the end of validation period N, if no treatment-advisable TA’s were declared, the processor withdraws the treatment-advisable arrhythmia declaration and continues to monitor the patient.

[0170] Turning now to FIG. 13, an arrhythmia analysis process 1300 is illustrated that employs TA discrimination to advantageous effect. As will be appreciated in view of this disclosure, in execution some examples of the process 1300 may produce a timeline like the timeline 1200 of FIG. 12. The process 1300 may be executed by a processor (e.g., the processor 318 of FIG. 3). The processor may operate under control of code (e.g., if any part of the detector 316 of FIG. 3 is implemented in software) and / or may include specialized circuitry (e.g., if any part of the detector 316 is implemented in special-purpose hardware). As shown in FIG. 13, the process 1300 starts with the processor declaring 1302 that the patient is presenting with an apparent arrhythmia (e.g., a TA) for which treatment is advisable. For instance, in some examples, the processor declares an arrhythmia based on a heart rate indicator generated by a rate analyzer (e.g. the rate analyzer 416 of FIG. 4) as described above.

[0171] Continuing with the process 1300, the processor starts 1304 a validation timer configured to expire at the end of a time period during which the processor attempts to validate that the arrhythmia is treatment-advisable (e.g., via electrotherapy) or to determine that the arrhythmia is treatment-inadvisable. For instance, in some examples, the processor stores a validation period expiration time in memory that is equal to the current time plus a validation duration. This validation duration may be 10 seconds, 20 seconds, 30 seconds, 45 seconds, or 1 minute, depending on the implementation.

[0172] Continuing with the process 1300, the processor starts 1306 a detection timer configured to expire at the end of a time period during which the processor attempts to detect and / or calculate one or more statistics based on fiducial parameters. For instance, in some examples, the processor stores a detection period expiration time in memory that is equal to the current time plus a detection duration. This detection duration may be 1 second, 10 seconds, 20 seconds, 30 seconds, 45 seconds, or 1 minute, depending on the implementation.

[0173] Continuing with the process 1300, the processor collects 1308 a sample of fiducial parameters during a portion of the detection period. For instance, in some examples, the processor acquires ECG signals during the portion of the period and generates fiducial parameters based on the acquired ECG signals using any of the various technologies described herein. The duration of the portion of the detection period may vary between examples. For instance, the duration of theportion may be 1 second, 5 seconds, 10 seconds, 1 seconds, or 30 seconds, depending on the implementation. It should be noted that, in some examples, the operation 1308 may consume the entire detection period, rather than a portion thereof.

[0174] Continuing with the process 1300, the processor attempts to calculate 1310 an RS / RR ratio based on the fiducial parameters generated in the operation 1308. For instance, in some examples, the processor divides an RS interval generated in the operation 1308 by an RR interval generated in the operation 1308.

[0175] Continuing with the process 1300, the processor stores 1312 a record in memory of whether an RS / RR ratio was successfully calculated in the operation 1310. For instance, in some examples, the processor stores a value of 1 in the record if the RS / RR ratio was successfully calculated and a value of 0 in the record if the RS / RR ratio was not successfully calculated.

[0176] Continuing with the process 1300, the processor determines 1314 whether the detection timer has expired. For instance, in some examples, the processor retrieves the current time from a system clock accessible to the processor and compares the current time to the detection expiration time set in the operation 1306. In these examples, if the current time is equal to or exceeds the detection expiration time, the processor determines that the detection timer has expired and proceeds to an operation 1316. Further, in these examples, if the current time is less than the detection expiration time, the processor determines that the detection timer has not expired and returns to the operation 1308 to collect another sample of fiducial parameters.

[0177] Continuing with the process 1300, the processor calculates 1316 a statistic based on the records accumulated via execution of the operations 1308-1312. For instance, in some examples, the processor calculates a mean of the values stored in the records.

[0178] Continuing with the process 1300, the processor determines 1318 whether the arrhythmia declared in the operation 1302 is treatment-advisable based on a tachyarrhythmic discrimination metric. The process used to make this determination varies between examples and the statistics calculated in the operation 1310. For instance, in some examples, the processor determines whether the arrhythmia is treatment-advisable (e.g., discriminates VT from other TAs) via threshold analysis. In at least one example, the processor determines that the arrhythmia is treatment -inadvisable if the mean of the recorded values calculated in the operation 1316 is greater than 0.8. If the processor determines that the arrhythmia is treatment-advisable, the processor stores 1320 a record of the same in the memory and, optionally, initiates a treatment protocol (e.g.,where the processor is a part of a medical treatment device, such as the medical devices described below with reference to FIGS. 14A and 14B), and the process 1300 may end. If the processor determines that the arrhythmia is treatment-inadvisable, the processor proceeds to operation 1322.

[0179] Continuing with the process 1300, the processor determines 1322 whether the validation timer has expired. For instance, in some examples, the processor retrieves the current time from a system clock accessible to the processor and compares the current time to an expiration time set in the operation 1304. In these examples, if the current time is equal to or exceeds the expiration time, the processor determines that the validation timer has expired and proceeds to an operation 1324. Further, in these examples, if the current time is less than the expiration time, the processor determines that the validation timer has not expired and returns to the operation 1306 to start another statistics timer and collect another sample of fiducial parameters.

[0180] Continuing with the process 1300, the processor records 1324 withdrawal of the declaration of the treatment-advisable arrhythmia. For instance, in some examples, the processor stores a record in the memory that indicates advisability of treatment of the the arrhythmia declared in the operation 1302 was unable to be validated, thus indicating that the arrhythmia was not, for example, VT, and the process 1300 may end. The process illustrated in Figure 13 may be provided by the processor independent of the other processes or functionality described herein. Further, in some examples a statistics timer and / or validation timer may not be used and instead the progression of the process may be defined by a number of ratios, samples or ECG segments.

[0181] It should be noted that, the processes 900, 1100, and 1300 described above will result in an initially declared treatable-advisable arrhythmia condition being treated (provide that the medical device executing the process is configured for electrotherapy) unless at least one tachyarrhythmic discrimination metric indicates that treatment is inadvisable and, therefore, should be at least delayed.

[0182] It should be noted that, in some examples of the processes 900, 1100, and 1300, when determining whether treatment is advisable within the initial execution of operations 910, 1118, or 1318, the processor further analyzes rapidity of onset of the arrhythmia condition detected in operations 902, 1108, and 1302. For instance, in some of these examples, the processor calculates, within the initial execution of operations 910, 1118, and 1318, an average heart rate for a period of time ending with the arrhythmia declaration of the operations 902, 1108, and 1302 and compares this average heart rate to a threshold value above which treatment of the arrythmia is inadvisable.If the processor determines that the average heart rate is below the threshold value, the processor further determines that treatment of the arrythmia is advisable and proceeds appropriately. If the processor determines that the average heart rate is not below the threshold value, the processor determines that treatment may be inadvisable and further determines treatment advisability as described above. The threshold value and duration of the period of time used in these examples varies based on the particular implementation. Some examples of the threshold value include 150 bpm or more (e.g., 170 bpm, 180 bpm, 200 bpm, 230 bpm, or 240 bpm). Some examples of the duration of the period of time include 10 seconds or more (e.g., 30 seconds, 45 seconds, 1 minute, 1.5 minutes, or 2 minutes).

[0183] It should also be noted that, in some examples of the processes 900, 1100, and 1300, when determining whether treatment is advisable within execution of the operations 910, 1118, or 1318, the processor further analyzes symmetry of the QR and RS intervals. For instance, in some of these examples, the processor calculates, within the operations 910, 1118, and 1318, a ratio of the slope of the QR interval to the slope of the RS interval and compares this ratio to a range of values associated with sufficient symmetry to advise against treatment. If the processor determines that the ratio falls outside the range, the processor further determines that treatment of the arrythmia is advisable and proceeds appropriately. If the processor determines that the ratio is within the range, the processor determines that treatment may be inadvisable and further determines treatment advisability as described above. The range of values used in these examples varies based on the particular implementation. Some examples of the range of values include -0.7 to -1.2 and -0.9 to - 1.1.

[0184] The teachings of the present disclosure can be generally applied to external medical monitoring and / or treatment devices that include one or more sensors as described herein. Such external medical devices can include, for example, ambulatory medical devices as described herein that are capable of and designed for moving with the patient as the patient goes about his or her daily routine. An example ambulatory medical device can be a wearable medical device such as a WCD, a wearable cardiac monitoring device, an in-hospital device such as an HWD, a short-term wearable cardiac monitoring and / or therapeutic device, mobile cardiac event monitoring devices, and other similar wearable medical devices.

[0185] The wearable medical device can be capable of continuous use by the patient. In some implementations, the continuous use can be substantially or nearly continuous in nature. That is,the wearable medical device can be continuously used, except for sporadic periods during which the use temporarily ceases (e.g., while the patient bathes, while the patient is refit with a new and / or a different garment, while the battery is charged / changed, while the garment is laundered, etc.). Such substantially or nearly continuous use as described herein may nonetheless be considered continuous use. For example, the wearable medical device can be configured to be worn by a patient for as many as 24 hours a day. In some implementations, the patient can remove the wearable medical device for a short portion of the day (e.g., for half an hour to bathe). In such an example, nearly continuous can include 23.5 hours a day of wear with a half hour removal period.

[0186] Further, the wearable medical device can be configured as a long term or extended use medical device. Such devices can be configured to be used by the patient for an extended period of several days, weeks, months, or even years. In some examples, the wearable medical device can be used by a patient for an extended period of at least one week. In some examples, the wearable medical device can be used by a patient for an extended period of at least 30 days. In some examples, the wearable medical device can be used by a patient for an extended period of at least one month. In some examples, the wearable medical device can be used by a patient for an extended period of at least two months. In some examples, the wearable medical device can be used by a patient for an extended period of at least three months. In some examples, the wearable medical device can be used by a patient for an extended period of at least six months. In some examples, the wearable medical device can be used by a patient for an extended period of at least one year. In some implementations, the extended use can be uninterrupted until a physician or other healthcare provider (HCP) provides specific instruction to the patient to stop use of the wearable medical device.

[0187] Regardless of the extended period of wear, the use of the wearable medical device can include continuous wear by the patient as described above. For example, the continuous use can include continuous wear or attachment of the wearable medical device to the patient, e.g., through one or more of the electrodes as described herein, during both periods of monitoring and periods when the device may not be monitoring the patient but is otherwise still worn by or otherwise attached to the patient. The wearable medical device can be configured to continuously monitor the patient for cardiac-related information (e.g., ECG information, including arrhythmia information, cardio-vibrations, etc.) and / or non-cardiac information (e.g., blood oxygen, the patient’s temperature, glucose levels, tissue fluid levels, and / or lung vibrations). The wearablemedical device can carry out its monitoring in periodic or aperiodic time intervals or times. For example, the monitoring during intervals or times can be triggered by a user action or another event.

[0188] As noted above, the wearable medical device can be configured to monitor other non-ECG physiologic parameters of the patient in addition to cardiac related parameters. For example, the wearable medical device can be configured to monitor, for example, pulmonary-vibrations (e.g., using microphones and / or accelerometers), breath vibrations, sleep related parameters (e.g., snoring, sleep apnea), tissue fluids (e.g., using radio-frequency transmitters and sensors), among others.

[0189] Other example wearable medical devices include automated cardiac monitors and / or defibrillators for use in certain specialized conditions and / or environments such as in combat zones or within emergency vehicles. Such devices can be configured so that they can be used immediately (or substantially immediately) in a life-saving emergency. In some examples, the ambulatory medical devices described herein can be pacing-enabled, e.g., capable of providing therapeutic pacing pulses to the patient. In some examples, the ambulatory medical devices can be configured to monitor for and / or measure ECG metrics including, for example, heart rate (such as average, median, mode, or other statistical measure of the heart rate, and / or maximum, minimum, resting, pre-exercise, and post-exercise heart rate values and / or ranges), heart rate variability metrics, premature ventricular contraction (PVC) burden or counts, atrial fibrillation burden metrics, pauses, heart rate turbulence, QRS height, QRS width, changes in a size or shape of morphology of the ECG information, cosine R-T, artificial pacing, QT interval, QT variability, T wave width, T wave alternans, T-wave variability, and ST segment changes.

[0190] FIG. 14A illustrates an example medical device 1400 that is external, ambulatory, and wearable by a patient 1402, and configured to implement one or more configurations described herein. For example, the medical device 1400 can be a non-invasive medical device configured to be located substantially external to the patient. Such a medical device 1400 can be, for example, an ambulatory medical device that is capable of and designed for moving with the patient as the patient goes about his or her daily routine. For example, the medical device 1400 as described herein can be bodily-attached to the patient such as the LifeVest® wearable cardioverter defibrillator available from ZOLL® Medical Corporation. Such wearable defibrillators typically are worn nearly continuously for two to three months at a time. During the period of time in whichthey are worn by the patient, the wearable defibrillator can be configured to continuously monitor the vital signs of the patient and, upon determination that treatment is required, can be configured to deliver one or more therapeutic electrical pulses to the patient. For example, such therapeutic shocks can be pacing, defibrillation, or transcutaneous electrical nerve stimulation (TENS) pulses.

[0191] The medical device 1400 can include one or more of the following: a garment 1410, one or more ECG sensing electrodes 1412, one or more non-ECG physiological sensors 1413, one or more therapy electrodes 1414a and 1414b (collectively referred to herein as therapy electrodes 1414), a medical device controller 1420 (e.g., controller 400 as described above in the discussion of FIG. 4), a connection pod 1430, a patient interface pod 1440, a belt 1450, or any combination of these. In some examples, at least some of the components of the medical device 1400 can be configured to be affixed to the garment 1410 (or in some examples, permanently integrated into the garment 1410), which can be worn about the patient’s torso.

[0192] The medical device controller 1420 can be operatively coupled to the sensing electrodes 1412, which can be affixed to the garment 1410, e.g., assembled into the garment 1410 or removably attached to the garment, e.g., using hook and loop fasteners. In some implementations, the sensing electrodes 1412 can be permanently integrated into the garment 1410. The medical device controller 1420 can be operatively coupled to the therapy electrodes 1414. For example, the therapy electrodes 1414 can also be assembled into the garment 1410, or, in some implementations, the therapy electrodes 1414 can be permanently integrated into the garment 1410. In an example, the medical device controller 1420 includes a patient user interface 1460 to allow a patient interface with the externally-worn device. For example, the patient can use the patient user interface 1460 to respond to activity related questions, prompts, and surveys as described herein.

[0193] Component configurations other than those shown in FIG. 14A are possible. For example, the sensing electrodes 1412 can be configured to be attached at various positions about the body of the patient 1402. The sensing electrodes 1412 can be operatively coupled to the medical device controller 1420 through the connection pod 1430. In some implementations, the sensing electrodes 1412 can be adhesively attached to the patient 1402. In some implementations, the sensing electrodes 1412 and at least one of the therapy electrodes 1414 can be included on a single integrated patch and adhesively applied to the patient’s body.

[0194] The sensing electrodes 1412 can be configured to detect one or more cardiac signals. Examples of such signals include ECG signals and / or other sensed cardiac physiological signals from the patient. In certain examples, as described herein, the non-ECG physiological sensors 1413 such as accelerometers, vibrational sensors, RF-based sensors, and other measuring devices for recording additional non-ECG physiological parameters. For example, as described above, the such non-ECG physiological sensors are configured to detect other types of patient physiological parameters and acoustic signals, such as tissue fluid levels, cardio-vibrations, lung vibrations, respiration vibrations, patient movement, etc.

[0195] In some examples, the therapy electrodes 1414 can also be configured to include sensors configured to detect ECG signals as well as other physiological signals of the patient. The connection pod 1430 can, in some examples, include a signal processor configured to amplify, filter, and digitize these cardiac signals prior to transmitting the cardiac signals to the medical device controller 1420. One or more of the therapy electrodes 1414 can be configured to deliver one or more therapeutic defibrillating shocks to the body of the patient 1402 when the medical device 1400 determines that such treatment is warranted based on the signals detected by the sensing electrodes 1412 and processed by the medical device controller 1420. Example therapy electrodes 1414 can include metal electrodes such as stainless-steel electrodes that include one or more conductive gel deployment devices configured to deliver conductive gel to the metal electrode prior to delivery of a therapeutic shock.

[0196] In some implementations, medical devices as described herein can be configured to switch between a therapeutic medical device and a monitoring medical device that is configured to only monitor a patient (e.g., not provide or perform any therapeutic functions). For example, therapeutic components such as the therapy electrodes 1414 and associated circuitry can be optionally decoupled from (or coupled to) or switched out of (or switched in to) the medical device. For example, a medical device can have optional therapeutic elements (e.g., defibrillation and / or pacing electrodes, components, and associated circuitry) that are configured to operate in a therapeutic mode. The optional therapeutic elements can be physically decoupled from the medical device to convert the therapeutic medical device into a monitoring medical device for a specific use (e.g., for operating in a monitoring-only mode) or a patient. Alternatively, the optional therapeutic elements can be deactivated (e.g., via a physical or a software switch), essentially rendering the therapeutic medical device as a monitoring medical device for a specific physiologicpurpose or a particular patient. As an example of a software switch, an authorized person can access a protected user interface of the medical device and select a preconfigured option or perform some other user action via the user interface to deactivate the therapeutic elements of the medical device.

[0197] FIG. 14B illustrates a hospital wearable defibrillator 1400A that is external, ambulatory, and wearable by a patient 1402. Hospital wearable defibrillator 1400A can be configured in some implementations to provide pacing therapy, e.g., to treat bradycardia, tachycardia, and asystole conditions. The hospital wearable defibrillator 1400A can include one or more ECG sensing electrodes 1412a, one or more therapy electrodes 1414a and 1414b, a medical device controller 1420 and a connection pod 1430. For example, each of these components can be structured and function as like number components of the medical device 1400. For example, the electrodes 1412a, 1414a, 1414b can include disposable adhesive electrodes. For example, the electrodes can include sensing and therapy components disposed on separate sensing and therapy electrode adhesive patches. In some implementations, both sensing and therapy components can be integrated and disposed on a same electrode adhesive patch that is then attached to the patient. For example, the front adhesively attachable therapy electrode 1414a attaches to the front of the patient’s torso to deliver pacing or defibrillating therapy. Similarly, the back adhesively attachable therapy electrode 1414b attaches to the back of the patient’s torso. In an example scenario, at least three ECG adhesively attachable sensing electrodes 1412a can be attached to at least above the patient’s chest near the right arm, above the patient’s chest near the left arm, and towards the bottom of the patient’s chest in a manner prescribed by a trained professional.

[0198] A patient being monitored by a hospital wearable defibrillator and / or pacing device may be confined to a hospital bed or room for a significant amount of time (e.g., 75% or more of the patient’s stay in the hospital). As a result, a user interface 1460a can be configured to interact with a user other than the patient, e.g., a nurse, for device-related functions such as initial device baselining, setting and adjusting patient parameters, and changing the device batteries. Such interactions can also be accomplished using an HCP interface application (e.g., the HCP interface application 122A ofFIG. 1).

[0199] In some examples, the hospital wearable defibrillator 1400A can further includes one or more motion sensors such as accelerometers. For example, an accelerometer can be integrated into one or more of a sensing electrode 1412a (e g., integrated into the same patch as the sensingelectrode), a therapy electrode 1414a (e.g., integrated into the same patch as the therapy electrode), the medical device controller 1420, the connection pod 1430, and various other components of the hospital wearable defibrillator 1400A.

[0200] In some implementations, an example of a therapeutic medical device that includes a digital front-end in accordance with the systems and methods described herein can include a short-term defibrillator and / or pacing device. For example, such a short-term device can be prescribed by a physician for patients presenting with syncope. A wearable defibrillator can be configured to monitor patients presenting with syncope by, e g., analyzing the patient’s physiological and cardiac activity for aberrant patterns that can indicate abnormal physiological function. For example, such aberrant patterns can occur prior to, during, or after the onset of syncope. In such an example implementation of the short-term wearable defibrillator, the electrode assembly can be adhesively attached to the patient’s skin and have a similar configuration as the hospital wearable defibrillator described above in connection with FIG. 14A.

[0201] FIGS. 14C and 14D illustrate example wearable patient monitoring devices with no treatment or therapy functions. For example, such devices are configured to monitor one or more physiological parameters of a patient, e.g., for remotely monitoring and / or diagnosing a condition of the patient. For example, such physiological parameters can include a patient’s ECG information, tissue (e.g., lung) fluid levels, cardio-vibrations (e.g., using accelerometers or microphones), and other related cardiac information. A cardiac monitoring device is a portable device that the patient can carry around as he or she goes about their daily routine.

[0202] Referring to FIG. 14C, an example wearable patient monitoring device 1400C can include tissue fluid monitors 1465 that use RF based techniques to assess fluid levels and accumulation in a patient’s body tissue. Such tissue fluid monitors 1465 can be configured to measure fluid content in the lungs, typically for diagnosis and follow-up of pulmonary edema or lung congestion in heart failure patients. The tissue fluid monitors 1465 can include one or more antennas configured to direct RF waves through a patient’s tissue and measure output RF signals in response to the waves that have passed through the tissue. In certain implementations, the output RF signals include parameters indicative of a fluid level in the patient’s tissue. In examples, device 1400C may be a cardiac monitoring device that also includes digital sensing electrodes 1470 for sensing ECG activity of the patient. Device 1400C can pre-process the ECG signals via one or more ECG processing and / or conditioning circuits such as an ADC, operational amplifiers, digital filters,signal amplifiers under control of a microprocessor. Device 1400C can transmit information descriptive of the ECG activity and / or tissue fluid levels via a network interface to a remote server for analysis. Additionally, in certain implementations, the device 1400C can include one or accelerometers for measuring motion signals as described herein.

[0203] Referring to FIG 14D, another example wearable cardiac monitoring device MOOD can be attached to a patient via at least three adhesive digital cardiac sensing electrodes 1475 disposed about the patient’s torso. Additionally, in certain implementations, the device MOOD can include one or accelerometers integrated into, for example, one or more of the digital sensing electrodes for measuring motion signals as described herein.

[0204] Cardiac devices 1400C and MOOD are used in cardiac monitoring and telemetry and / or continuous cardiac event monitoring applications, e.g., in patient populations reporting irregular cardiac symptoms and / or conditions. These devices can transmit information descriptive of the ECG activity and / or tissue fluid levels via a network interface to a remote server for analysis. Example cardiac conditions that can be monitored include atrial fibrillation (AF), bradycardia, tachycardia, atrio-ventricular block, Lown-Ganong-Levine syndrome, atrial flutter, sino-atrial node dysfunction, cerebral ischemia, pause(s), and / or heart palpitations. For example, such patients may be prescribed a cardiac monitoring for an extended period of time, e.g., 10 to 30 days, or more. In some ambulatory cardiac monitoring and / or telemetry applications, a portable cardiac monitoring device can be configured to continuously monitor the patient for a cardiac anomaly, and when such an anomaly is detected, the monitor can automatically send data relating to the anomaly to a remote server. The remote server may be located within a 24-hour manned monitoring center, where the data is interpreted by qualified, cardiac-trained reviewers and / or HCPs, and feedback provided to the patient and / or a designated HCP via detailed periodic or event- triggered reports. In certain cardiac event monitoring applications, the cardiac monitoring device is configured to allow the patient to manually press a button on the cardiac monitoring device to report a symptom. For example, a patient can report symptoms such as a skipped beat, shortness of breath, light headedness, racing heart rate, fatigue, fainting, chest discomfort, weakness, dizziness, and / or giddiness. The cardiac monitoring device can record predetermined physiologic parameters of the patient (e.g., ECG information) for a predetermined amount of time (e.g., 1-30 minutes before and 1-30 minutes after a reported symptom). As noted above, the cardiac monitoring device can be configured to monitor physiologic parameters of the patient other thancardiac related parameters. For example, the cardiac monitoring device can be configured to monitor, for example, cardio-vibrational signals (e.g., using accelerometers or microphones), pulmonary-vibrational signals, breath vibrations, sleep related parameters (e.g., snoring, sleep apnea), tissue fluids, among others.

[0205] In some examples, the devices described herein (e.g., FIGS. 14A-14D) can communicate with a remote server via an intermediary or gateway device 1480 such as that shown in FIG. 14D. For instance, devices such as shown in FIGS. 14A-D can be configured to include a network interface communications capability as described herein in reference to, for example, FIG. 4.

[0206] Although the subject matter contained herein has been described in detail for the purpose of illustration, it is to be understood that such detail is solely for that purpose and that the present disclosure is not limited to the disclosed embodiments, but, on the contrary, is intended to cover modifications and equivalent arrangements that are within the scope of the appended claims. For example, it is to be understood that the present disclosure contemplates that, to the extent possible, one or more features of any embodiment can be combined with one or more features of any other embodiment.

[0207] Other examples are within the scope of the description and claims. Additionally, certain functions described above can be implemented using software, hardware, firmware, hardwiring, or combinations of any of these. Features implementing functions can also be physically located at various positions, including being distributed such that portions of functions are implemented at different physical locations.

Claims

CLAIMSWhat is claimed is:

1. An ambulatory medical system for discriminating ventricular tachyarrhythmias from other tachyarrhythmias, the system comprising: a plurality of sensing electrodes configured to contact skin of a patient externally, and detect electrical activity from the skin of the patient, the electrical activity indicating cardiac function in the patient; and at least one processor coupled with the plurality of sensing electrodes and configured to derive electrocardiogram (ECG) data from the electrical activity indicating the cardiac function in the patient, detect an apparent tachyarrhythmia in the patient based on the ECG data, identify an arrhythmic ECG segment indicating the apparent tachyarrhythmia in the patient, the arrhythmic ECG segment comprising a first ECG interval bounded by two fiducial points within a first heartbeat of the patient and second ECG interval bounded by a first fiducial point within the first heartbeat and a second fiducial point within a second heartbeat of the patient, determine, based on the first and second ECG intervals, a tachyarrhythmic discrimination metric indicative of QRS complex widening, and provide, based on the tachyarrhythmic discrimination metric, an indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event.

2. The ambulatory medical system of claim 1, wherein: the ventricular tachyarrhythmic event comprises a treatment-advisable cardiac rhythm event in the patient; and the supraventricular tachycardiac event comprises a treatment-inadvisable cardiac rhythm event in the patient.

3. The ambulatory medical system of claim 2, wherein the treatment-advisable cardiac rhythm event comprises a ventricular tachycardia event or a ventricular fibrillation event.

4. The ambulatory medical system of any preceding claim, wherein: the first ECG interval spans a portion of a first heartbeat of the patient; and the second ECG interval spans a second heartbeat of the patient.

5. The ambulatory medical system of claim 4, wherein: the first heartbeat and the second heartbeat are distinct heartbeats; and the second ECG interval overlaps the first ECG interval.

6. The ambulatory medical system of claim 4, wherein: the first heartbeat and the second heartbeat are distinct heartbeats; and the second ECG interval does not overlap the first ECG interval.

7. The ambulatory medical system of claim 4, wherein: the first heartbeat and the second heartbeat are a same heartbeat; and the second ECG interval overlaps the first ECG interval.

8. The ambulatory medical system of claim 7, wherein: the first ECG interval spans two fiducial points within a QRS complex of the same heartbeat of the patient; and the second ECG interval spans two fiducial points within the same heartbeat of the patient.

9. The ambulatory medical system of claim 7 or claim 8, wherein: the first ECG interval is an RS interval; and the second ECG interval is an RR interval.

10. The ambulatory medical system of any preceding claim, wherein the tachyarrhythmic discrimination metric is a comparison of the first ECG interval and the second ECG interval.

11. The ambulatory medical system of claim 10, wherein the tachyarrhythmic discrimination metric is a ratio of the first ECG interval to the second ECG interval.

12. The ambulatory medical system of any preceding claim, wherein to provide the indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event comprises to provide the indication based on a comparison between a configurable parameter and the tachyarrhythmic discrimination metric.

13. The ambulatory medical system of claim 12, wherein the configurable parameter quantifies a target relationship between the first ECG interval and the second ECG interval.

14. The ambulatory medical system of any preceding claim, wherein: the tachyarrhythmic discrimination metric is a first tachyarrhythmic discrimination metric; the at least one processor is further configured to determine additional tachyarrhythmic discrimination metrics; and to provide the indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event comprises to provide the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics.

15. The ambulatory medical system of claim 14, wherein: the at least one processor is further configured to derive at least one summary statistic of the first tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics; and to provide the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics comprises to provide the indication based on the at least one summary statistic.

16. The ambulatory medical system of claim 15, wherein the at least one summary statistic comprises one or more of mean, median, mode, quartile, percentage, variance, standard deviation, and kurtosis.

17. The ambulatory medical system of any one of claims 14 to 16, wherein: to determine the additional tachyarrhythmic discrimination metrics comprises to determine a first subset of the additional tachyarrhythmic discrimination metrics prior to detection of the apparent tachyarrhythmia, and determine a second subset of the additional tachyarrhythmic discrimination metrics after detection of the apparent tachyarrhythmia; and to provide the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics comprises to determine a first summary statistic of the first subset of the additional tachyarrhythmic discrimination metrics; determine a second summary statistic of the first tachyarrhythmic discrimination metric and the second subset of the additional tachyarrhythmic discrimination metrics; and compare the first summary statistic to the second summary statistic.

18. The ambulatory medical system of claim 17, wherein to determine the first subset of the additional tachyarrhythmic discrimination metrics comprises to determine the first subset during a baseline measurement of the cardiac function of the patient.

19. The ambulatory medical system of claim 17, wherein: to determine the first subset of the additional tachyarrhythmic discrimination metrics comprises to analyze first ECG data within a first window, the first window having a length; and to determine the second subset of the additional tachyarrhythmic discrimination metrics comprises to analyze second ECG data within a second window having the length, the second ECG data comprising the arrhythmic ECG segment.

20. The ambulatory medical system of any preceding claim, wherein:the plurality of sensing electrodes comprise a side-side electrode pair and a front-back electrode pair; to identify the arrhythmic ECG segment comprises to identify a first arrhythmic ECG segment using the front-back electrode pair; to determine the tachyarrhythmic discrimination metric comprises to determine a first tachyarrhythmic discrimination metric; the at least one processor is further configured to identify a second arrhythmic ECG segment using the side-side electrode pair, determine a second tachyarrhythmic discrimination metric based third and fourthECG intervals with the second arrhythmic ECG segment; and to provide the indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event comprises to provide the indication based on the first tachyarrhythmic discrimination metric and the second tachyarrhythmic discrimination metric.

21. The ambulatory medical system of any preceding claim, wherein: the at least one processor is further configured to determine an onset time based on the arrhythmic ECG segment; and to provide the indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event comprises to provide the indication based on the tachyarrhythmic discrimination metric and the onset time.

22. The ambulatory medical system of any preceding claim, wherein to determine the tachyarrhythmic discrimination metric comprises to measure a slope of the ECG data.

23. The ambulatory medical system of claim 22, wherein the tachyarrhythmic discrimination metric comprises a metric indicative of asymmetry between a slope of a QR interval and a slope of a RS interval.

24. The ambulatory medical system of claim 23, wherein the metric indicative of asymmetry comprises a ratio of a first statistic based on the slope of the QR interval to a second statistic based on the slope of the RS interval.

25. The ambulatory medical system of claim 24, wherein the first statistic and the second statistic are means or medians.

26. A wearable cardioverter-defibrillator (WCD) for discriminating ventricular tachyarrhythmias from other tachyarrhythmias, the WCD comprising: a plurality of sensing electrodes configured to contact skin of a patient externally, and detect electrical activity from the skin of the patient, the electrical activity indicating cardiac function in the patient; a plurality of therapy electrodes configured to contact the skin of the patient externally, and deliver electrotherapy to a heart of the patient through the skin of the patient; and at least one processor coupled with the plurality of sensing electrodes and the plurality of therapy electrodes and configured to derive electrocardiogram (ECG) data from the electrical activity indicating the cardiac function in the patient, detect an apparent tachyarrhythmia in the patient based on the ECG data, identify an arrhythmic ECG segment indicating the apparent tachyarrhythmia in the patient, the arrhythmic ECG segment comprising a first ECG interval bounded by two fiducial points within a first heartbeat of the patient and second ECG interval bounded by a first fiducial point within the first heartbeat and a second fiducial point within a second heartbeat of the patient, determine, based on the first and second ECG intervals, a tachyarrhythmic discrimination metric indicative of QRS complex widening, and deliver or delay, based on the tachyarrhythmic discrimination metric, the electrotherapy based on whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event.

27. The WCD of claim 26, wherein: the ventricular tachyarrhythmic event comprises a treatment-advisable cardiac rhythm event in the patient; and the supraventricular tachycardiac event comprises a treatment-inadvisable cardiac rhythm event in the patient.

28. The WCD of claim 27, wherein the treatment-advisable cardiac rhythm event comprises a ventricular tachycardia event or a ventricular fibrillation event.

29. The WCD of any of claims 26-28, wherein: the first ECG interval spans a portion of a first heartbeat of the patient; and the second ECG interval spans a second heartbeat of the patient.

30. The WCD of claim 29, wherein: the first heartbeat and the second heartbeat are distinct heartbeats; and the second ECG interval overlaps the first ECG interval.

31. The WCD of claim 29, wherein: the first heartbeat and the second heartbeat are distinct heartbeats; and the second ECG interval does not overlap the first ECG interval.

32. The WCD of claim 29, wherein: the first heartbeat and the second heartbeat are a same heartbeat; and the second ECG interval overlaps the first ECG interval.

33. The WCD of claim 32, wherein: the first ECG interval spans two fiducial points within a QRS complex of the same heartbeat of the patient; and the second ECG interval spans two fiducial points within the same heartbeat of the patient.

34. The WCD of claim 32 or 33, wherein: the first ECG interval is an RS interval; and the second ECG interval is an RR interval.

35. The WCD of any of claims 26 to 34, wherein the tachyarrhythmic discrimination metric is a comparison of the first ECG interval and the second ECG interval.

36. The WCD of claim 35, wherein the tachyarrhythmic discrimination metric is a ratio of the first ECG interval to the second ECG interval.

37. The WCD of any of claims 26 to 36, wherein to deliver or delay the electrotherapy comprises to deliver or delay the electrotherapy based on a comparison between a configurable parameter and the tachyarrhythmic discrimination metric.

38. The WCD of claim 37, wherein the configurable parameter quantifies a target relationship between the first ECG interval and the second ECG interval.

39. The WCD of any of claims 26 to 38, wherein: the tachyarrhythmic discrimination metric is a first tachyarrhythmic discrimination metric; the at least one processor is further configured to determine additional tachyarrhythmic discrimination metrics; and to deliver or delay the electrotherapy comprises to deliver or delay the electrotherapy based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics.

40. The WCD of claim 39, wherein: the at least one processor is further configured to derive at least one summary statistic of the first tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics; andto deliver or delay the electrotherapy comprises to deliver or delay the electrotherapy based on the at least one summary statistic.

41. The WCD of claim 40, wherein the at least one summary statistic comprises one or more of mean, median, mode, quartile, percentage, variance, standard deviation, and kurtosis.

42. The WCD of any one of claims 39 to 42, wherein: to determine the additional tachyarrhythmic discrimination metrics comprises to determine a first subset of the additional tachyarrhythmic discrimination metrics prior to detection of the apparent tachyarrhythmia, and determine a second subset of the additional tachyarrhythmic discrimination metrics after detection of the apparent tachyarrhythmia; and to deliver or delay the electrotherapy comprises to: determine a first summary statistic of the first subset of the additional tachyarrhythmic discrimination metrics; determine a second summary statistic of the first tachyarrhythmic discrimination metric and the second subset of the additional tachyarrhythmic discrimination metrics; and compare the first summary statistic to the second summary statistic.

43. The WCD of claim 42, wherein to determine the first subset of the additional tachyarrhythmic discrimination metrics comprises to determine the first subset during a baseline measurement of the cardiac function of the patient.

44. The WCD of claim 42, wherein: to determine the first subset of the additional tachyarrhythmic discrimination metrics comprises to analyze first ECG data within a first window, the first window having a length; and to determine the second subset of the additional tachyarrhythmic discrimination metrics comprises to analyze second ECG data within a second window having the length, the second ECG data comprising the arrhythmic ECG segment.

45. The WCD of any of claims 26 to 44, wherein: the plurality of sensing electrodes comprise a side-side electrode pair and a front-back electrode pair; to identify the arrhythmic ECG segment comprises to identify a first arrhythmic ECG segment using the front-back electrode pair; to determine the tachyarrhythmic discrimination metric comprises to determine a first tachyarrhythmic discrimination metric; the at least one processor is further configured to identify a second arrhythmic ECG segment using the side-side electrode pair, determine a second tachyarrhythmic discrimination metric based third and fourthECG intervals with the second arrhythmic ECG segment; and to deliver or delay the electrotherapy comprises to deliver or delay the electrotherapy based on the first tachyarrhythmic discrimination metric and the second tachyarrhythmic discrimination metric.

46. The WCD of any of claims 26 to 45, wherein: the at least one processor is further configured to determine an onset time based on the arrhythmic ECG segment; and to deliver or delay the electrotherapy comprises to deliver or delay the electrotherapy based on the tachyarrhythmic discrimination metric and the onset time.

47. The WCD of any of claims 26 to 46, wherein to determine the tachyarrhythmic discrimination metric comprises to measure a slope of the ECG data.

48. The WCD of claim 47, wherein the tachyarrhythmic discrimination metric comprises a metric indicative of asymmetry between a slope of a QR interval and a slope of a RS interval.

49. The WCD of claim 48, wherein the metric indicative of asymmetry comprises a ratio of a first statistic based on the slope of the QR interval to a second statistic based on the slope of the RS interval.-n-50. The WCD of claim 49, wherein the first statistic and the second statistic are means or medians.

51. The WCD of claim 26, wherein to deliver or delay the electrotherapy comprises to deliver electrotherapy if the tachyarrhythmic discrimination metric is inconclusive.

52. A wearable cardiac monitor for discriminating ventricular tachyarrhythmias from other tachyarrhythmias, the wearable cardiac monitor comprising: a plurality of sensing electrodes configured to contact skin of a patient externally, and detect electrical activity from the skin of the patient, the electrical activity indicating cardiac function in the patient; and at least one processor coupled with the plurality of sensing electrodes and configured to derive electrocardiogram (ECG) data from the electrical activity indicating the cardiac function in the patient, detect an apparent tachyarrhythmia in the patient based on the ECG data, identify an arrhythmic ECG segment indicating the apparent tachyarrhythmia in the patient, the arrhythmic ECG segment comprising first and second ECG intervals, determine, based on the first and second ECG intervals, a tachyarrhythmic discrimination metric indicative of QRS complex widening, and record, based on the tachyarrhythmic discrimination metric, an indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event.

53. The wearable cardiac monitor of claim 52, wherein: the ventricular tachyarrhythmic event comprises a treatment-advisable cardiac rhythm event in the patient; and the supraventricular tachycardiac event comprises a treatment-inadvisable cardiac rhythm event in the patient.

54. The wearable cardiac monitor of claim 53, wherein the treatment-advisable cardiac rhythm event comprises a ventricular tachycardia event or a ventricular fibrillation event.

55. The wearable cardiac monitor of any of claims 52 to 54, wherein: the first ECG interval spans a portion of a first heartbeat of the patient; and the second ECG interval spans a second heartbeat of the patient.

56. The wearable cardiac monitor of claim 55, wherein: the first heartbeat and the second heartbeat are distinct heartbeats; and the second ECG interval overlaps the first ECG interval.

57. The wearable cardiac monitor of claim 55, wherein: the first heartbeat and the second heartbeat are distinct heartbeats; and the second ECG interval does not overlap the first ECG interval.

58. The wearable cardiac monitor of claim 55, wherein: the first heartbeat and the second heartbeat are a same heartbeat; and the second ECG interval overlaps the first ECG interval.

59. The wearable cardiac monitor of claim 58, wherein: the first ECG interval spans two fiducial points within a QRS complex of the same heartbeat of the patient; and the second ECG interval spans two fiducial points within the same heartbeat of the patient.

60. The wearable cardiac monitor of claim 58 or claim 59, wherein: the first ECG interval is an RS interval; and the second ECG interval is an RR interval.

61. The wearable cardiac monitor of any one of claims 52 to 60, wherein the tachyarrhythmic discrimination metric is a comparison of the first ECG interval and the second ECG interval.

62. The wearable cardiac monitor of claim 61, wherein the tachyarrhythmic discrimination metric is a ratio of the first ECG interval to the second ECG interval.

63. The wearable cardiac monitor of any one of claims 52 to 62, wherein to record the indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event comprises to record the indication based on a comparison between a configurable parameter and the tachyarrhythmic discrimination metric.

64. The wearable cardiac monitor of claim 63, wherein the configurable parameter quantifies a target relationship between the first ECG interval and the second ECG interval.

65. The wearable cardiac monitor of any one of claims 52 to 64, wherein: the tachyarrhythmic discrimination metric is a first tachyarrhythmic discrimination metric; the at least one processor is further configured to determine additional tachyarrhythmic discrimination metrics; and to record the indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event comprises to record the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics.

66. The wearable cardiac monitor of claim 65, wherein: the at least one processor is further configured to derive at least one summary statistic of the first tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics; and to record the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics comprises to record the indication based on the at least one summary statistic.

67. The wearable cardiac monitor of claim 66, wherein the at least one summary statistic comprises one or more of mean, median, mode, quartile, percentage, variance, standard deviation, and kurtosis.

68. The wearable cardiac monitor of any one of claims 65 to 67, wherein: to determine the additional tachyarrhythmic discrimination metrics comprises to determine a first subset of the additional tachyarrhythmic discrimination metrics prior to detection of the apparent tachyarrhythmia, and determine a second subset of the additional tachyarrhythmic discrimination metrics after detection of the apparent tachyarrhythmia; and to record the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics comprises to: determine a first summary statistic of the first subset of the additional tachyarrhythmic discrimination metrics; determine a second summary statistic of the first tachyarrhythmic discrimination metric and the second subset of the additional tachyarrhythmic discrimination metrics; and compare the first summary statistic to the second summary statistic.

69. The wearable cardiac monitor of claim 68, wherein to determine the first subset of the additional tachyarrhythmic discrimination metrics comprises to determine the first subset during a baseline measurement of the cardiac function of the patient.

70. The wearable cardiac monitor of claim 68, wherein: to determine the first subset of the additional tachyarrhythmic discrimination metrics comprises to analyze first ECG data within a first window, the first window having a length; and to determine the second subset of the additional tachyarrhythmic discrimination metrics comprises to analyze second ECG data within a second window having the length, the second ECG data comprising the arrhythmic ECG segment.

71. The wearable cardiac monitor of any one of claims 52 to 70, wherein:the plurality of sensing electrodes comprise a side-side electrode pair and a front-back electrode pair; to identify the arrhythmic ECG segment comprises to identify a first arrhythmic ECG segment using the front-back electrode pair; to determine the tachyarrhythmic discrimination metric comprises to determine a first tachyarrhythmic discrimination metric; the at least one processor is further configured to identify a second arrhythmic ECG segment using the side-side electrode pair, determine a second tachyarrhythmic discrimination metric based third and fourthECG intervals with the second arrhythmic ECG segment; and to record the indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event comprises to record the indication based on the first tachyarrhythmic discrimination metric and the second tachyarrhythmic discrimination metric.

72. The wearable cardiac monitor of any one of claims 52 to 71, wherein: the at least one processor is further configured to determine an onset time based on the arrhythmic ECG segment; and to record the indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event comprises to record the indication based on the tachyarrhythmic discrimination metric and the onset time.

73. The wearable cardiac monitor of any one of claims 52 to 72, wherein to determine the tachyarrhythmic discrimination metric comprises to measure a slope of the ECG data.

74. The wearable cardiac monitor of claim 73, wherein the tachyarrhythmic discrimination metric comprises a metric indicative of asymmetry between a slope of a QR interval and a slope of a RS interval.

75. The wearable cardiac monitor of claim 74, wherein the metric indicative of asymmetry comprises a ratio of a first statistic based on the slope of the QR interval to a second statistic based on the slope of the RS interval.

76. The wearable cardiac monitor of claim 75, wherein the first statistic and the second statistic are means or medians.

77. One or more non-transitory computer readable media storing sequences of instructions executable by at least one processor of a medical device to discriminate ventricular tachyarrhythmias from other tachyarrhythmias, the sequences of instructions comprising instructions to: derive electrocardiogram (ECG) data from electrical activity from a skin of a patient, the electrical activity indicating cardiac function in the patient; detect an apparent tachyarrhythmia in the patient based on the ECG data; identify an arrhythmic ECG segment indicating the apparent tachyarrhythmia in the patient, the arrhythmic ECG segment comprising first and second ECG intervals; determine, based on the first and second ECG intervals, a tachyarrhythmic discrimination metric indicative of QRS complex widening; and provide, based on the tachyarrhythmic discrimination metric, an indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event.

78. The one or more non-transitory computer readable media of claim 77, wherein: the ventricular tachyarrhythmic event comprises a treatment-advisable cardiac rhythm event in the patient; and the supraventricular tachycardiac event comprises a treatment -inadvisable cardiac rhythm event in the patient.

79. The one or more non-transitory computer readable media of claim 78, wherein the treatment- advisable cardiac rhythm event comprises a ventricular tachycardia event or a ventricular fibrillation event.-SO-80. The one or more non-transitory computer readable media of any one of claims 77 to 79, wherein: the first ECG interval spans a portion of a first heartbeat of the patient; and the second ECG interval spans a second heartbeat of the patient.

81. The one or more non-transitory computer readable media of claim 80, wherein: the first heartbeat and the second heartbeat are distinct heartbeats; and the second ECG interval overlaps the first ECG interval.

82. The one or more non-transitory computer readable media of claim 80, wherein: the first heartbeat and the second heartbeat are distinct heartbeats; and the second ECG interval does not overlap the first ECG interval.

83. The one or more non-transitory computer readable media of claim 80, wherein: the first heartbeat and the second heartbeat are a same heartbeat; and the second ECG interval overlaps the first ECG interval.

84. The one or more non-transitory computer readable media of claim 83, wherein: the first ECG interval spans two fiducial points within a QRS complex of the same heartbeat of the patient; and the second ECG interval spans two fiducial points within the same heartbeat of the patient.

85. The one or more non-transitory computer readable media of claim 83 or 84, wherein: the first ECG interval is an RS interval; and the second ECG interval is an RR interval.

86. The one or more non-transitory computer readable media of any one of claims 77 to 85, wherein the tachyarrhythmic discrimination metric is a comparison of the first ECG interval and the second ECG interval.

87. The one or more non-transitory computer readable media of claim 86, wherein the tachyarrhythmic discrimination metric is a ratio of the first ECG interval to the second ECG interval.

88. The one or more non-transitory computer readable media of any one of claims 77 to 87, wherein to provide the indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event comprises to provide the indication based on a comparison between a configurable parameter and the tachyarrhythmic discrimination metric.

89. The one or more non-transitory computer readable media of claim 88, wherein the configurable parameter quantifies a target relationship between the first ECG interval and the second ECG interval.

90. The one or more non-transitory computer readable media of any one of claims 77 to 89, wherein: the tachyarrhythmic discrimination metric is a first tachyarrhythmic discrimination metric; the instructions further comprise instructions to determine additional tachyarrhythmic discrimination metrics; and to provide the indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event comprises to provide the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics.

91. The one or more non-transitory computer readable media of claim 90, wherein: the instructions further comprise instructions to derive at least one summary statistic of the first tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics; andto provide the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics comprises to provide the indication based on the at least one summary statistic.

92. The one or more non-transitory computer readable media of claim 91, wherein the at least one summary statistic comprises one or more of mean, median, mode, quartile, percentage, variance, standard deviation, and kurtosis.

93. The one or more non-transitory computer readable media of any one of claims 90 to 92, wherein: to determine the additional tachyarrhythmic discrimination metrics comprises to determine a first subset of the additional tachyarrhythmic discrimination metrics prior to detection of the apparent tachyarrhythmia, and determine a second subset of the additional tachyarrhythmic discrimination metrics after detection of the apparent tachyarrhythmia; and to provide the indication based on the tachyarrhythmic discrimination metric and the additional tachyarrhythmic discrimination metrics comprises to determine a first summary statistic of the first subset of the additional tachyarrhythmic discrimination metrics; determine a second summary statistic of the first tachyarrhythmic discrimination metric and the second subset of the additional tachyarrhythmic discrimination metrics; and compare the first summary statistic to the second summary statistic.

94. The one or more non-transitory computer readable media of claim 93, wherein to determine the first subset of the additional tachyarrhythmic discrimination metrics comprises to determine the first subset during a baseline measurement of the cardiac function of the patient.

95. The one or more non-transitory computer readable media of claim 93, wherein: to determine the first subset of the additional tachyarrhythmic discrimination metrics comprises to analyze first ECG data within a first window, the first window having a length; andto determine the second subset of the additional tachyarrhythmic discrimination metrics comprises to analyze second ECG data within a second window having the length, the second ECG data comprising the arrhythmic ECG segment.

96. The one or more non-transitory computer readable media of any of claims 77 to 95, wherein: to identify the arrhythmic ECG segment comprises to identify a first arrhythmic ECG segment using a front-back electrode pair; to determine the tachyarrhythmic discrimination metric comprises to determine a first tachyarrhythmic discrimination metric; the instructions further comprise instructions to identify a second arrhythmic ECG segment using a side-side electrode pair, determine a second tachyarrhythmic discrimination metric based third and fourthECG intervals with the second arrhythmic ECG segment; and to provide the indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event comprises to provide the indication based on the first tachyarrhythmic discrimination metric and the second tachyarrhythmic discrimination metric.

97. The one or more non-transitory computer readable media of any one of claims 77 to 96, wherein: the instructions further comprise instructions to determine an onset time based on the arrhythmic ECG segment; and to provide the indication of whether the arrhythmic ECG segment comprises a ventricular tachyarrhythmic event or a supraventricular tachycardiac event comprises to provide the indication based on the tachyarrhythmic discrimination metric and the onset time.

98. The one or more non-transitory computer readable media of any one of claims 77 to 97, wherein to determine the tachyarrhythmic discrimination metric comprises to measure a slope of the ECG data.

99. The one or more non-transitory computer readable media of claim 98, wherein the tachyarrhythmic discrimination metric comprises a metric indicative of asymmetry between a slope of a QR interval and a slope of a RS interval.

100. The one or more non-transitory computer readable media of claim 99, wherein the metric indicative of asymmetry comprises a ratio of a first statistic based on the slope of the QR interval to a second statistic based on the slope of the RS interval.

101. The one or more non-transitory computer readable media of claim 100, wherein the first statistic and the second statistic are means or medians.

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