Systems and methods for detecting cardiac arrhythmias

Abstract

Systems and methods for ambulatory detection of medical events such as arrhythmias are described herein. Embodiments of an arrhythmia detection system may include a detection criteria circuit that uses baseline cardiac characteristics of the patient when the patient does not have an arrhythmia to determine a patient-specific detection criteria. The detection criteria circuit generates a patient-specific signal metric threshold by adjusting a population-based signal metric threshold, wherein the manner and amount of adjustment is based on information about the patient's baseline cardiac characteristics. The arrhythmia detection system detects an arrhythmia episode using a physiological signal sensed from the patient and a patient-specific arrhythmia detection threshold.

Description

[0001] Priority claim

[0002] This application claims the benefit of priority under 35 U.S.C. §119(e) to U.S. Provisional Patent Application Serial No. 62 / 668,392, filed on May 8, 2018, the entire contents of which are incorporated herein by reference. Technical Field

[0003] This document relates generally to medical devices and, more particularly, to systems, devices, and methods for detecting and managing cardiac arrhythmias. Background Art

[0004] Implantable medical devices (IMDs) have been used to monitor patient health or disease states and to deliver therapy. For example, implantable cardioverter defibrillators (ICDs) can be used to monitor certain abnormal heart rhythms and deliver electrical energy to the heart to correct the abnormal rhythms. Some IMDs can be used to monitor chronic deterioration of cardiac hemodynamic performance, such as caused by congestive heart failure (CHF), and provide cardiac stimulation therapy, including cardiac resynchronization therapy (CRT) for correcting cardiac asynchrony within or between chambers.

[0005] Some IMDs can detect arrhythmias, such as atrial tachyarrhythmias. One type of atrial tachyarrhythmia is atrial fibrillation (AF), considered the most common clinical arrhythmia affecting millions of people. During AF, disordered electrical impulses originating in or near the atria can cause irregular conduction to the ventricles, resulting in an inappropriately fast and irregular heart rate. AF may be paroxysmal, which may last from a few minutes to a few days before stopping on its own. Persistent AF may last for more than a week and often requires medication or other treatment to return to normal sinus rhythm. If normal rhythm cannot be restored with treatment, AF is permanent. AF may be associated with stroke and require anticoagulation.

[0006] Another type of atrial tachyarrhythmia is atrial flutter (AFL). AFL is often associated with some degree of atrioventricular (AV) nodal block and may be associated with a rapid and usually regular heart rate. Typical or type I AFL may involve a single reentrant circuit in the right atrium around the tricuspid annulus with an atrial rate of 240 to 340 beats per minute (bpm). This reentrant circuit most often runs in a counterclockwise direction. Atypical or type II AFL follows a different circuit, may involve the right or left atrium, and typically has a faster atrial rate of around 340-440 bpm. AFL may be associated with a variety of heart conditions, such as coronary artery disease (CAD) or hypertensive heart disease. AFL often degenerates into AF. Chronic, rapid AFL can lead to decompensation and loss of normal heart function. This may manifest as exertion intolerance, nighttime dyspnea, or swelling in the legs or abdomen.

[0007] Regular testing for atrial tachyarrhythmias such as AF or AFL may be clinically important for assessing cardiac function. Some atrial tachyarrhythmias may be characterized by a slow and steady ventricular rate. Such atrial tachyarrhythmic episodes may be mistaken for sinus rhythm by the IMD and go undetected or underdetected in some patients. This may adversely affect patient outcomes.

[0008] Overview

[0009] Atrial tachyarrhythmias such as AF or AFL are characterized by rapid atrial rates. In some patients, such as those not indicated for atrial lead implantation, direct sensing of atrial activation rate using electrodes located in or near the atria is not available or feasible. Medical devices, such as single-chamber IMDs without dedicated atrial sensing / pacing leads, can detect atrial tachyarrhythmias based on ventricular heart rate without directly sensing atrial activity. However, confounding factors such as noise, motion artifacts, or heart rhythms other than atrial tachyarrhythmias may be erroneously detected as atrial tachyarrhythmia events. For example, during AFL, impulses from the atria are conducted to the ventricles through the atrioventricular node (AV node). Largely due to its longer refractory period, the AV node has a protective effect on ventricular heart rates by blocking atrial impulses exceeding 180 beats per minute (bpm). If the AFL rate is 300 bpm, a two-to-one (2:1) heart block may develop, whereby only half of the atrial impulses are conducted to the ventricles, resulting in a ventricular rate of 150 bpm. Because heart rate is a measure of ventricular activity rather than atrial activity, medical devices that detect atrial tachyarrhythmias based on ventricular heart rate rather than atrial activity may be confused by physiological sinus rhythm when the heart rate is elevated, such as during tolerable physical activity (e.g., sinus tachycardia).

[0010] By comparing the signal metric generated from the physiological signal with the detection criteria (e.g., detection threshold), arrhythmias can be detected. Traditionally, the detection threshold is determined based on population data. Users (e.g., healthcare providers) can manually adjust the population-based detection threshold for individual patients. Population-based thresholds are usually tuned to achieve the desired overall algorithm performance (e.g., sensitivity, specificity, or positive and negative predictive values), but may not provide the best arrhythmia detection performance for one or some patients. Although manually programming the detection threshold can improve the detection performance of some patients, it lacks automation and usually consumes extra time and more medical resources for patient monitoring. In addition, manually tuning the detection threshold may not be feasible for mobile patient management, in which case there may not be appropriate medical personnel and resources available at any time, or such availability may be greatly delayed. As a result, arrhythmias such as AF or AFL may not be detected or falsely detected as sinus rhythm or other arrhythmic events. Inappropriate detection of atrial tachyarrhythmia attacks may reduce the specificity of detection and lead to lack of treatment or untimely treatment, or unnecessary or inappropriate treatment. Providing false alarms to clinicians regarding inappropriately detected arrhythmias, or presenting clinicians with a high number of inappropriately detected arrhythmia events for review or judgment, could adversely affect device effectiveness and unnecessarily increase healthcare costs associated with patient management. This could therefore reduce the clinical utility of heart rate-based atrial tachyarrhythmia detection.

[0011] For at least the foregoing reasons, the inventors have recognized an unmet need for improved mobile arrhythmia detection systems and methods that can automatically adjust device settings for individual patients to more accurately detect arrhythmias such as atrial tachyarrhythmias. Systems, devices, and methods for detecting arrhythmias such as atrial tachyarrhythmias, among others, are discussed herein. Embodiments of an arrhythmia detection system may include a detection criteria circuit that uses baseline cardiac characteristics of the patient when the patient does not have an arrhythmia to determine patient-specific detection criteria. In one example, the detection criteria circuit may generate a patient-specific signal metric threshold by adjusting a population-based signal metric threshold, wherein the manner and amount of adjustment is based on the patient's baseline cardiac characteristics. The arrhythmia detection system may detect an arrhythmia attack using physiological signals sensed from the patient and a patient-specific arrhythmia detection threshold.

[0012] Example 1 is a system for detecting arrhythmias in a patient. The system includes a detection criteria circuit and an arrhythmia detector circuit. The detection criteria circuit is configured to determine a baseline cardiac characteristic using a first physiological signal sensed from the patient when the patient does not have an arrhythmia, and to determine a patient-specific arrhythmia detection threshold using the determined baseline cardiac characteristic. The arrhythmia detector circuit is configured to detect an arrhythmia episode using a second physiological signal sensed from the patient and the patient-specific arrhythmia detection threshold.

[0013] In Example 2, the subject matter of Example 1 optionally includes a detection criteria circuit that can be further configured to determine a patient-specific arrhythmia confirmation threshold using the determined baseline cardiac characteristics. The arrhythmia detector circuit is configured to confirm or deny a detected arrhythmia episode using the patient-specific arrhythmia confirmation threshold; wherein the patient-specific arrhythmia confirmation threshold corresponds to a higher specificity for detecting arrhythmias than the arrhythmia detection threshold.

[0014] In Example 3, the subject matter of Example 2 optionally includes an arrhythmia detector circuit that can be configured to: generate a first signal metric and a second signal metric different from the first signal metric from a second physiological signal, respectively; detect an arrhythmia attack by comparing the first signal metric with a patient-specific arrhythmia detection threshold; and confirm or deny the detected arrhythmia attack by comparing the second signal metric with a patient-specific arrhythmia confirmation threshold.

[0015] In Example 4, the subject matter of any one or more of Examples 1-3 optionally includes a detection standard circuit that can be configured to detect the patient's sinus rhythm using a third physiological signal sensed from the patient and determine baseline cardiac characteristics during the detected sinus rhythm.

[0016] In Example 5, the subject matter of Example 4 can optionally include a sensor circuit configured to sense the first, second, and third physiological signals.

[0017] In Example 6, the subject matter of Example 5 can optionally include a sensor circuit coupled to a first sensor for sensing the first physiological signal and a second sensor for sensing the second physiological signal, the first sensor being of a different type than the second sensor.

[0018] In Example 7, the subject matter of any one or more of Examples 4-6 can optionally include a detection criteria circuit that can be configured to detect sinus rhythm using a population-based threshold.

[0019] In Example 8, the subject matter of any one or more of Examples 1-7 can optionally include a detection criteria circuit that can be configured to determine the patient-specific arrhythmia detection threshold by modifying the population-based threshold according to baseline cardiac characteristics.

[0020] In Example 9, the subject matter of any one or more of Examples 1-8 can optionally include a detection standard circuit that can be configured to update the baseline cardiac characteristic periodically or in response to a change in a patient condition.

[0021] In Example 10, the subject matter of Example 9 can optionally include a detection standard circuit that can be configured to update the baseline cardiac characteristic in response to a change in posture.

[0022] In Example 11, the subject matter of any one or more of Examples 1-10 optionally includes a detection standard circuit that can be configured to: determine a baseline cardiac characteristic during a training session having a training duration; evaluate a quality measurement of the determined baseline cardiac characteristic; and adjust the training duration based on the quality measurement of the baseline cardiac characteristic.

[0023] In Example 12, the subject matter of any one or more of Examples 1-11 can optionally include an arrhythmia detector circuit that can be configured to detect an atrial tachyarrhythmia episode using a patient-specific atrial tachyarrhythmia detection threshold.

[0024] In Example 13, the subject matter of Example 12 optionally includes that the baseline cardiac characteristics may include a ventricular rate variability measure during sinus rhythm.

[0025] In Example 14, the subject matter of Example 12 optionally includes that the baseline cardiac characteristics may include a measure of atrioventricular block during sinus rhythm.

[0026] In Example 15, any one or more of Examples 1-14 optionally include that the arrhythmia may include cardiac arrest. The detection criteria circuit may be configured to determine a patient-specific R-wave amplitude threshold using the baseline cardiac characteristic, and the arrhythmia detector circuit may be configured to determine an R-wave amplitude metric from the second physiological signal and detect the cardiac arrest episode by comparing the R-wave amplitude metric to the patient-specific R-wave amplitude threshold.

[0027] Example 16 is a method for detecting arrhythmia in a patient using a medical system. The method includes the following steps: using a sensor circuit to sense a first physiological signal when the patient does not have an arrhythmia and a second physiological signal different from the first physiological signal; using the first physiological signal to determine a baseline cardiac characteristic via a detection standard circuit; using the determined baseline cardiac characteristic to determine a patient-specific arrhythmia detection threshold via the detection standard circuit; and detecting an arrhythmia episode via an arrhythmia detector circuit using the second physiological signal and the patient-specific arrhythmia detection threshold.

[0028] In Example 17, the subject matter of Example 16 optionally includes detecting an atrial tachyarrhythmia episode using a patient-specific atrial tachyarrhythmia detection threshold.

[0029] In Example 18, the subject matter of Example 17 optionally includes determining baseline cardiac characteristics, which may include generating a signal metric from a first physiological signal during sinus rhythm comprising one or more of: a ventricular rate variability metric; an atrioventricular block metric; a signal morphology metric; or a signal quality metric.

[0030] In Example 19, the subject matter of Example 16 optionally includes the steps of determining a patient-specific arrhythmia confirmation threshold using determined baseline cardiac characteristics, and confirming or denying a detected arrhythmia episode using the patient-specific arrhythmia confirmation threshold; wherein the patient-specific arrhythmia confirmation threshold corresponds to a higher specificity for detecting arrhythmias than the arrhythmia detection threshold.

[0031] In Example 20, the subject matter of Example 16 optionally includes detecting sinus rhythm of the patient using the third physiological signal and the population-based threshold, and determining baseline cardiac characteristics during the detected sinus rhythm.

[0032] In Example 21, the subject matter of Example 16 optionally includes determining a patient-specific arrhythmia detection threshold, which may include modifying a population-based threshold based on baseline cardiac characteristics.

[0033] In Example 22, the subject matter of Example 16 optionally includes updating the baseline cardiac characteristics of the patient periodically or in response to changes in the patient's condition.

[0034] In Example 23, the subject matter of any one or more of Examples 16-22 optionally includes the steps of: determining a baseline cardiac characteristic during a training session having a training duration; evaluating a quality measurement of the determined baseline cardiac characteristic; and adjusting the training duration based on the quality measurement of the baseline cardiac characteristic.

[0035] The systems, devices and methods discussed herein can improve the medical technology of automatic cardiac rhythm management (CRM) and avoid deterioration of cardiac function. Patient-specific detection criteria such as patient-specific detection thresholds discussed herein can enhance the performance and functionality of implantable medical devices. In some examples, patient-specific detection criteria take into account the patient's underlying medical condition and improve the sensitivity and specificity of atrial tachyarrhythmia event detection, with little or no additional cost. The systems and methods discussed herein can reduce the costs associated with false atrial tachyarrhythmia detection, or the manual inspection required for such false determinations. In other examples, existing system performance (e.g., high arrhythmia detection sensitivity and specificity, etc.) can be maintained using lower cost or less prominent systems, devices and methods. For example, since the system or device does not need to directly sense atrial activity, system complexity and implementation costs can be reduced. It may be particularly beneficial to patients who are not indicated for atrial lead implantation for atrial activity sensing or atrial pacing. In addition, the arrhythmia detection discussed herein can more effectively utilize device memory by storing information clinically related to atrial tachyarrhythmia detection such as patient baseline cardiac characteristics. With improved atrial tachyarrhythmia detection, fewer alarms may be provided, battery life may be extended, fewer unnecessary medications and procedures may be prescribed, employed or provided, and overall system cost and energy savings may be achieved compared to existing medical devices and systems.

[0036] This summary is an overview of some of the teachings of this application, and is not an exclusive or exhaustive discussion of the subject matter of the present invention. More details about the subject matter of the present invention can be found in the detailed description and claims section. Other aspects of the present disclosure will be apparent to those of ordinary skill in the art upon reading and understanding the detailed description below and viewing the drawings that form a part thereof, and no aspect of the device is intended to be limiting. The scope of the present disclosure is defined by the attached claims and their legal equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Various embodiments are shown by way of example in the figures of the accompanying drawings. These embodiments are illustrative and are not exhaustive or exclusive embodiments of the inventive subject matter.

[0038] Figure 1 An example of a patient management system and a portion of an environment in which the system may operate is generally shown.

[0039] Figure 2 An example of an arrhythmia detection system configured to detect an arrhythmia episode, such as an atrial tachyarrhythmia, is generally shown.

[0040] Figure 3Examples are generally shown for adjusting arrhythmia detection criteria based on a patient's baseline cardiac characteristics.

[0041] Figure 4A-4B is a graph showing examples of patient-specific atrial fibrillation detection thresholds associated with signal metrics representing different cardiac characteristics.

[0042] Figure 5 is a graph showing an example of using R-wave amplitude trends and patient-specific thresholds to detect asystole.

[0043] Figure 6 is a flow chart illustrating an example of a method for detecting an arrhythmia, such as an atrial tachyarrhythmia episode, using a patient-specific detection threshold.

[0044] Figure 7 is a flow chart illustrating an example of a method for determining patient-specific criteria for detecting arrhythmias.

[0045] Figure 8 A block diagram generally illustrates an example machine upon which any one or more of the techniques (eg, methodologies) discussed herein may be executed. DETAILED DESCRIPTION

[0046] Disclosed herein are systems, devices, and methods for detecting arrhythmias. Embodiments of an arrhythmia detection system may use baseline cardiac characteristics when the patient does not have an arrhythmia to determine patient-specific detection criteria. The patient-specific detection criteria may include a patient-specific arrhythmia detection threshold of a signal metric. The arrhythmia detection system may use physiological signals sensed from a patient and a patient-specific arrhythmia detection threshold to detect an arrhythmia episode.

[0047] Figure 1 Patient management system 100 and portions of an environment in which system 100 may operate are generally shown. Patient management system 100 may perform a range of activities, including remote patient monitoring and disease diagnosis. These activities may be performed proximate to the patient, such as at the patient's home or office; through a central server, such as at a hospital, clinic, or doctor's office; or through a remote workstation, such as a secure wireless mobile computing device.

[0048] The patient management system 100 may include a mobile system 105 associated with the patient 102 , an external system 125 , and a telemetry link 115 that provides communication between the mobile system 105 and the external system 125 .

[0049] The mobile system 105 may include an ambulatory medical device (AMD) 110. In one example, the AMD 110 may be an implantable device that is implanted subcutaneously in the chest, abdomen, or other part of the patient 102. Examples of implantable devices may include, but are not limited to, pacemakers, pacemaker / defibrillators, cardiac resynchronization therapy (CRT) devices, cardiac remodeling control therapy (RCT) devices, neuromodulators, drug delivery devices, biotherapeutic devices, diagnostic devices such as cardiac monitors or cycle recorders, or patient monitors, etc. Alternatively or additionally, the AMD 110 may include: a subcutaneous medical device such as a subcutaneous monitor or diagnostic device; a medical device for external monitoring or treatment such as an automatic external defibrillator (AED) or a Holter monitor; or a wearable medical device such as a patch-based device, a smart watch, or a smart accessory.

[0050] As an example, AMD 110 can be coupled to a lead system 108. Lead system 108 can include one or more leads or catheters placed intravenously, subcutaneously, or non-invasively. Each lead or catheter can include one or more electrodes. The arrangement and use of lead system 108 and associated electrodes can be determined based on the patient's needs and the capabilities of AMD 110. Associated electrodes on lead system 108 can be located on the patient's chest or abdomen to sense physiological signals indicative of cardiac activity, or physiological responses to diagnostic or therapeutic stimulation of target tissue. As an example and not limitation and as Figure 1 As shown, the lead system 108 can be surgically inserted into the heart 101 or located on the surface of the heart 101. The electrodes on the lead system 108 can be located on a part of the heart 101 such as the right atrium (RA), the right ventricle (RV), the left atrium (LA) or the left ventricle (LV), or on any tissue between or near these heart parts. In some examples, the lead system 108 and its associated electrodes can alternatively be located on other parts of the body to sense physiological signals containing information about the patient's heart rate or pulse. In one example, the mobile system 105 can include one or more wireless sensors that are not tied to the AMD110 via the lead system 108. The leadless mobile sensor can be configured to sense physiological signals and communicate wirelessly with the AMD 110.

[0051] AMD 110 can be configured as a monitoring or diagnostic device. AMD 110 can include a hermetically sealed canister containing one or more sensing circuits, control circuits, communication circuits, batteries, and other components. The sensing circuit can sense physiological signals, for example, using electrodes associated with a physiological sensor or lead system 108. Examples of physiological signals can include one or more of the following: electrocardiogram, intracardiac electrogram, arrhythmia, heart rate, heart rate variability, intrathoracic impedance, intracardiac impedance, arterial pressure, pulmonary artery pressure, left atrial pressure, right ventricular (RV) pressure, left ventricular (LV) coronary pressure, coronary blood temperature, blood oxygen saturation, one or more heart sounds, intracardiac acceleration, physical activity or exertion, physiological response to activity, posture, respiratory rate, tidal volume, breath sounds, weight, or body temperature.

[0052] AMD 110 may include a physiological event detector circuit 160, which is configured to detect physiological events using sensed physiological signals. In one example, physiological events include arrhythmia attacks, such as atrial fibrillation, atrial flutter, atrial tachycardia, supraventricular tachycardia, ventricular tachycardia or ventricular fibrillation, cardiac arrest and other slow or tachycardia attacks. In one example, the physiological event detector circuit 160 is configured to detect syncope or pre-syncope events, or sudden events that may cause complete syncope. In some examples, the physiological event detector circuit 160 is configured to detect the deterioration of chronic diseases, such as worsening heart failure (WHF). The physiological event detector circuit 160 can execute a detection algorithm to continuously or periodically monitor one or more physiological signals and automatically detect physiological events. Additionally or alternatively, the physiological event detector circuit 160 can be configured to operate in a patient-triggered mode, register patient-triggered attacks, and record physiological data in response to a user-activated trigger. When a patient exhibits a certain sign or symptom or experiences a precursor event indicating a medical event, the patient can activate the trigger.

[0053] AMD 110 can be configured as a therapeutic device alternatively, which is configured to treat arrhythmias or other heart diseases. AMD 110 can additionally include a therapeutic unit, which can generate and transmit one or more treatments. Treatment can be transmitted to patient 102 through lead system 108 and associated electrodes. Treatment can include electrical, magnetic or other types of treatment. Treatment can include antiarrhythmic therapy to treat arrhythmias, or treat or control complications caused by one or more arrhythmias, such as syncope, congestive heart failure or stroke. Examples of antiarrhythmic treatments can include pacing, cardioversion, defibrillation, neuromodulation, drug therapy or biological therapy and other types of treatment. In one example, treatment can include cardiac resynchronization therapy (CRT) for correcting asynchrony in CHF patients and improving their cardiac function. In some examples, AMD 110 can include a drug delivery system, such as a drug infusion pump that transmits drugs for managing arrhythmias or complications caused by arrhythmias to patients.

[0054] The external system 125 may include a dedicated hardware / software system, such as a programmer, a patient management system based on a remote server, or alternatively a system defined by software running on a standard personal computer or mobile device. The external system 125 may manage the patient 102 through the AMD 110, which is connected to the external system 125 via a telemetry link 115. This may, for example, include programming the AMD 110 to perform one or more of the following: acquiring physiological data, performing at least one self-diagnostic test (such as device operating status), analyzing physiological data to detect arrhythmias, or optionally delivering or adjusting treatment to the patient 102. In addition, the external system 125 may receive device data from the AMD 110 via the telemetry link 115. Examples of device data received by the external system 125 may include: real-time or stored physiological data from the patient, diagnostic data such as detection of arrhythmias or worsening heart failure events, response to treatment delivered to the patient 102, or device operating status of the AMD 110 (e.g., battery status and lead impedance). Telemetry link 115 may be an inductive telemetry link, a capacitive telemetry link, or a radio frequency (RF) telemetry link, or wireless telemetry based on, for example, "strong" Bluetooth or IEEE 802 Wireless Fidelity "WiFi" interface standards. Other configurations and combinations of patient data source interfaces are possible.

[0055] By way of example and not limitation, external system 125 may include external device 120 proximal to AMD 110 and remote device 124 relatively remote from AMD 110 that communicates with external device 120 via telecommunications network 122. Examples of external device 120 may include a programmer device.

[0056] The remote device 124 may be configured to evaluate the collected patient data and provide alarm notifications and other possible functions. In one example, the remote device 124 may include a central server as a central hub for storage and analysis of collected patient data. The server may be configured as a single, multiple or distributed computing and processing system. The remote device 124 may receive patient data from multiple patients including, for example, patient 102. Patient data such as medical event episodes may be collected by AMD 110 and other data acquisition sensors or devices associated with patient 102. The remote device 124 may include a storage unit to store patient data in a patient database. The storage unit may additionally store associations between multiple episode features and multiple detection algorithms for detecting medical events with respective episode features. The server may process the event episodes generated by the device to verify that a specific medical event (e.g., arrhythmia type) is detected so that the event detected by the device is a true positive (TP) detection; or whether such a medical event is not detected so that the event detected by the device is a false positive (FP) detection. The processing of the medical event episodes generated by the device may be based on the stored associations. In one example, a first event episode may be presented to a user (e.g., a physician) who provides a judgment decision and first episode characteristics. If the judgment decision indicates that the first event episode is a FP detection, the server may identify a detection algorithm corresponding to the first episode characteristics from the stored associations and use at least one identified detection algorithm to process a second event episode to determine whether the second event episode is a TP or FP detection. The server may schedule the presentation of at least a portion of the second episode based on the processing results of the second episode. By using a detection algorithm adapted to distinguish episodes having episode characteristics associated with FP episodes, more FP episodes having the same or similar episode characteristics may be identified and thus avoided from being reviewed and judged by the user. If the second event episode is determined to be a TP episode, an alert is generated to indicate that further user review may be necessary.

[0057] As examples, the alarm notifications may include: a web page update, a telephone or paging call, an email, an SMS, an "instant" message, and a message to the patient and a direct notification to both the emergency services and the physician. Other alarm notifications are also possible. In some examples, the server may include a medical event prioritizer circuit configured to prioritize the alarm notifications. For example, a similarity metric between physiological data associated with the detected medical event and physiological data associated with historical alarms may be used to prioritize alarms for detected medical events.

[0058] Remote device 124 may additionally include one or more locally configured clients or remote clients securely connected to the server via network 122. Examples of clients may include personal desktops, notebook computers, mobile devices, or other computing devices. Users such as doctors or other qualified medical professionals may use clients to securely access stored patient data stored in a server database and select and prioritize patients and alarms for medical services. The remote device 124, including the server and interconnected clients, may execute subsequent scenarios: send subsequent requests to AMD 110, or send messages or other communications as compliance notifications to patients 102, doctors, or authorized third parties.

[0059] The network 122 can provide wired or wireless interconnectivity. In one example, the network 122 can be based on the Transmission Control Protocol / Internet Protocol (TCP / IP) network communication specification, but other types or combinations of network connection implementations are also possible. Similarly, other network topologies and arrangements are also possible.

[0060] One or more of the external device 120 or the remote device 124 can output the detected medical event to a user such as a patient or a doctor, or to a process, which for example includes an instance of an executable computer program in a microprocessor. In one example, the process can include automatically generating a treatment suggestion, or suggesting further diagnostic tests or treatments. In one example, the external device 120 or the remote device 124 can include a display unit, respectively, for displaying physiological or functional signals, or alarms, warnings, emergency calls, or other forms of warnings to signal arrhythmia detection. In some examples, the external system 125 can include an external data processor configured to analyze the physiological or functional signals received by the AMD 110, and to confirm or deny the detection of the medical event. Computationally intensive algorithms such as machine learning algorithms can be implemented in the external data processor to retrospectively process data to detect arrhythmias.

[0061] Portions of AMD 110 or external system 125 may be implemented using hardware, software, firmware, or a combination thereof. Portions of AMD 110 or external system 125 may be implemented using dedicated circuits that may be constructed or configured to perform one or more specific functions, or may be implemented using general purpose circuits that may be programmed or otherwise configured to perform one or more functions. Such general purpose circuits may include a microprocessor and a portion thereof, a microcontroller and a portion thereof, or a programmable logic circuit, a memory circuit, a network interface, and various components for interconnecting these components. For example, a "comparator" may include (among other things) an electronic circuit comparator that may be constructed to perform a specific function of comparing between two signals, or a comparator may be implemented as a portion of a general purpose circuit that may be driven by a code that commands a portion of the general purpose circuit to perform a comparison between two signals.

[0062] Figure 2 An example of an arrhythmia detection system 200 configured to detect arrhythmia episodes such as atrial tachyarrhythmia erroneous episodes is generally shown. Portions of the arrhythmia detection 200 may be included in the physiological event detector circuit 160 of the AMD 110. The arrhythmia detection system 200 may include one or more of a sensor circuit 210, a detection criteria circuit 220, an arrhythmia detector circuit 230, a controller circuit 240, and a user interface unit 250. The arrhythmia detection system 200 may additionally include an optional therapy circuit 260.

[0063] The sensor circuit 210 may include a sense amplifier circuit to sense physiological signals from the patient via one or more implantable, wearable or other flow sensors or electrodes associated with the patient. The sensed physiological signals may include information about pulsatile cardiac activity such as heart rate or pulse. Examples of physiological signals may include: a surface electrocardiogram (ECG) sensed, for example, from electrodes on the body surface, a subcutaneous ECG sensed, for example, from electrodes placed under the skin, an intracardiac electrogram (ECM) sensed from one or more electrodes on the lead system 108, a chest or cardiac impedance signal, an arterial pressure signal, a pulmonary artery pressure signal, a left atrial pressure signal, a RV pressure signal, a LV coronary pressure signal, a coronary blood flow temperature signal, a blood oxygen saturation signal, a heart sound signal sensed, for example, by a flow accelerometer or an acoustic sensor, a physiological response to activity, an apnea hypopnea index, one or more respiratory signals such as a respiratory rate signal or a tidal volume signal, a brain natriuretic peptide (BNP), a blood panel, sodium and potassium levels, blood glucose levels, and other biomarkers and biochemical markers, etc. The sensor circuit 210 may include one or more other subcircuits for digitizing, filtering, or performing other signal conditioning operations on the received physiological signals.

[0064] In some examples, the physiological signals may be stored in a storage device such as an electronic medical record system. The sensor circuit 210 may retrieve the physiological signals from the storage device in response to a command signal (provided by a system user or automatically generated in response to a specific event occurring).

[0065] The detection criteria circuit 220 can be coupled to the sensor circuit 210 to determine or adjust detection criteria, such as thresholds, for detecting physiological events such as arrhythmia attacks. The detection criteria circuit 220 can additionally determine or adjust thresholds for classifying arrhythmia events as specific arrhythmia types, such as classifying a detected atrial tachyarrhythmia attack as AF, AFL, or atrial tachycardia. The detection criteria can be determined or adjusted based on patient baseline characteristics to account for inter-patient differences and intra-patient changes over time, such as due to changes in the patient's medical condition. The resulting detection criteria can be patient-specific criteria.

[0066] The detection criteria circuit 220 may include a baseline cardiac characteristic detector 222 and a detection criteria adjuster circuit 224. The baseline cardiac characteristic detector 222 may determine the patient's baseline cardiac characteristics using physiological signals sensed by the sensor circuit 210 when the patient is not experiencing an arrhythmia, such as in sinus rhythm. In one example, the detection criteria circuit 220 may be coupled to a sinus rhythm detector to detect sinus rhythm. In another example, sinus rhythm may be confirmed by a user, such as a physician. The baseline cardiac characteristic detector 222 may determine the patient's baseline cardiac characteristics during sinus rhythm. For example, referring to Figure 3 The discussion includes an example of normal sinus rhythm detection used to generate baseline features.

[0067] Baseline cardiac characteristics can be represented by statistical or morphological signal characteristics of physiological signals acquired during a baseline period (e.g., when the patient is in sinus rhythm). In the example of detecting atrial tachyarrhythmias such as atrial fibrillation or atrial tachycardia, the baseline cardiac characteristics can include a baseline ventricular rate variability measure. The ventricular rate or cardiac cycle length can be detected using electrophysiological signals such as ECG, subcutaneous ECG, intracardiac electrogram, etc. Alternatively, the ventricular rate can be detected using mechanical physiological signals, such as heart sound signals sensed using accelerometers or microphone sensors, heart or chest impedance signals, or blood pressure signals in other sensors. The ventricular rate variability or cardiac cycle length variability can be calculated using the standard deviation, variance, range (e.g., the difference between the minimum and maximum values, the interquartile range between the upper quartile and the lower quartile, the difference between the 10th and 90th percentiles, or other ranges) or other distribution measures of multiple HR or CL measurements. Variability can be calculated over a specific period of time such as one minute, a day, a few days, or other specific time ranges.

[0068] In another example, the cardiac feature may include a ventricular rate pattern in which the ventricular rate continuously decreases. In one example, the ventricular rate pattern includes a pair of consecutive ventricular rate changes. Both ventricular rate changes are negative, referred to as a "double decrement" ventricular rate pattern. A double decrement ratio, which represents the prevalence of the double decrement ventricular rate pattern over a particular period of time or over multiple ventricular beats, can be calculated and used to detect atrial fibrillation or to distinguish AF from ectopic beats. The baseline cardiac feature detector 222 can determine a baseline number of double decrement beat patterns or a baseline double decrement ratio. This baseline double decrement pattern of ventricular rate can distinguish frequent premature ventricular contractions (PVCs) from AF attacks, because PVCs alone do not typically cause a double decrement pattern of ventricular rate. Krueger et al., in U.S. patent application Ser. No. 14 / 825,669, entitled "ATRIAL FIBRILLATION DETECTION USING VENTRICULAR RATEVARIABILITY," disclose a ventricular rate double decrement model and its use in atrial arrhythmia detection, the disclosure of which is hereby incorporated by reference in its entirety.

[0069] In yet another example, a cardiac feature may include ventricular rate clusters represented by a statistical distribution or histogram of ventricular rate or cardiac cycle length. Ventricular rate clusters indicate the regularity of the ventricular rate with respect to cardiac cycle length. Patients with AF typically present with irregular ventricular contractions. However, premature atrial contractions (PACs) may occur at irregular intervals. When PACs are conducted to the ventricles, they may produce irregular ventricular rates, resulting in ventricular clusters that are distinct from AF. Therefore, ventricular rate clusters can be used to distinguish frequent PACs from AF episodes. Perschbacher et al., U.S. patent application serial number 15 / 864,953, entitled "ATRIALFIBRILLATION DISCRIMINATION USING HEART RATE CLUSTERING," mention histogram clusters of ventricular rate and their use in distinguishing AF from non-AF events, the entire contents of which are hereby incorporated by reference.

[0070] Additionally or alternatively, the baseline cardiac features may include a measure of the occurrence of various beat patterns representing cardiac cycle length or heart rate. For example, the beat pattern may include the number or percentage of consecutive heart beats within + / - 5 beats per minute during a 2 minute window period. In one example, the baseline cardiac features may include an atrioventricular block measure that indicates the presence or degree of conduction abnormalities during sinus rhythm, such as a Wenckebach score representing the prevalence of Wenckebach block over a period of time. An example of a Wenckebach detector may be based on a repeatability indicator of various beat patterns of cycle length or heart rate, as discussed in U.S. Patent Application Serial No. 15 / 786,824 by Perschbacher et al., entitled "SYSTEMS AND METHODS FOR ARRHYTHMIADETECTION", the entire contents of which are hereby incorporated by reference. In addition, the baseline cardiac features may include a signal morphology measure representing the regularity of the ventricular depolarization signal morphology during sinus rhythm, or a signal quality measure such as a signal-to-noise ratio (SNR). The signal quality or signal morphology indicator may distinguish between AF and noise.

[0071] In various examples, the baseline cardiac characteristic detector 222 can determine a baseline cardiac characteristic during a training session having a training duration. The training period may be different for various signal metrics representing different cardiac characteristics. In one example, the training duration can be based on a user-specified priority of the signal metrics used by the arrhythmia detector circuit 230 to detect arrhythmic events or the amount of data collected. In another example, the training duration can be based on a quality measure of the determined baseline cardiac characteristic (e.g., the variability measured during the training period). For example, if the training period is nominally set to 30 days, but a large amount of variability in the patient's baseline characteristic is observed during this schedule, the baseline cardiac characteristic detector 222 can adjust the training period, such as by extending the training duration to collect more data to establish the baseline characteristic. Such a training period adjustment achieves a more reliable baseline characterization before generating a patient-specific detection threshold.

[0072] The detection criteria adjuster circuit 224 may determine a patient-specific arrhythmia detection criteria, or adjust an existing arrhythmia detection criteria, based at least on the patient's baseline characteristics as generated by the baseline cardiac characteristics detector 222. In one example, the patient-specific arrhythmia detection criteria include a patient-specific detection threshold (X) of the cardiac characteristic. TH ). Patient-specific threshold X THThe function of the patient's baseline cardiac characteristics can be used to calculate. In one example, an association between various baseline cardiac characteristics and corresponding detection thresholds can be created and stored in a memory. The association can be implemented using a lookup table, an association map, or other data structure.

[0073] In one example, the patient-specific threshold X TH can be automatically set relative to a baseline signal metric (X) that indicates the patient's baseline cardiac characteristics BL ), such as according to the following equation (1):

[0074] X TH = X BL - Δ (1)

[0075] For example, when using ventricular rate clustering or cardiac characteristics of ventricular rate or CL variability to detect atrial tachyarrhythmias, the detection criteria adjuster circuit 224 may set the patient-specific threshold (VR TH ) was set as the patient's baseline ventricular rate cluster or ventricular rate or CL variability (VR BL ) minus the specific difference Δ. For patients with high baseline ventricular rate variability, a lower patient-specific ventricular rate variability threshold corresponds to a higher sensitivity for detecting AF events in the patient. This can avoid or reduce the chance of missing AF episodes. In another example, the Wenckebach score can be used to detect or confirm atrial tachyarrhythmia episodes. The detection criteria adjuster circuit 224 can convert the Wenckebach score (WS TH The patient-specific threshold for WS was set as the patient's baseline Wenckebach score (WS BL ) minus the specific difference Δ. Since a high Wenckebach score indicates a lesser likelihood of atrial fibrillation, a lower patient-specific Wenckebach score threshold corresponds to a higher sensitivity for detecting AF events. This can avoid or reduce false positive detections of AF episodes. In some examples, the detection criteria adjuster circuit 224 can determine the patient-specific arrhythmia detection criteria by adjusting a population-based threshold, as described below. Figure 3 Discuss its details.

[0076] In various examples, the baseline cardiac characteristic detector 222 can evaluate and update one or more baseline characteristics periodically, triggered by a specific event such as a change in the patient's medical condition, or a user command. In one example, the baseline characteristics can be updated in response to a change in the patient's posture. Based on the update of the baseline characteristics, the detection criteria adjuster circuit 224 can automatically adjust the patient-specific detection criteria.

[0077] The arrhythmia detector circuit 230 can use patient-specific detection criteria to detect arrhythmias. In one example, the arrhythmia includes an atrial tachyarrhythmia such as atrial fibrillation (AF), atrial flutter (AFL), atrial tachycardia, or paroxysmal supraventricular tachycardia (PSVT). The arrhythmia detector circuit 230 can generate a signal metric from a physiological signal such as an electrophysiological or mechanical physiological signal sensed by the sensor circuit 210, and use a comparison result between the signal metric and a patient-specific atrial tachyarrhythmia detection threshold to detect atrial tachyarrhythmia. The signal metric represents a cardiac characteristic. In one example, if the ventricular rate variability exceeds the patient-specific variability threshold VR TH Atrial tachyarrhythmia can be detected if a specific difference is found. In another example, the arrhythmia detector circuit 230 can detect atrial tachyarrhythmia based on signal morphology. Atrial tachyarrhythmia can be detected if a similarity measure between a morphological feature intercepted from the physiological signal and a morphological template falls within a predetermined range relative to a patient-specific similarity threshold. The morphological template includes morphological features intercepted from a patient's baseline physiological signal, such as sensed during sinus rhythm. Examples of similarity measures can include correlation or can be based on distance in signal feature space.

[0078] In various examples, the detection criteria circuit 220 may additionally use the baseline cardiac characteristics to determine or adjust the patient-specific arrhythmia confirmation threshold ( TH-C ). The arrhythmia detector circuit 230 may use a patient-specific detection threshold X TH to detect the onset of arrhythmia and further use a patient-specific confirmation threshold X TH-C To confirm or deny the detected arrhythmia. Confirmation threshold X TH-C may be different from the detection threshold X TH , and correspond to different sensitivities or specificities for detecting or confirming arrhythmias. In one example, the confirmation threshold X TH-C Can correspond to the confirmation threshold X TH Higher specificity and detection threshold X TH Can correspond to the confirmation threshold X TH-C Higher sensitivity. In one example, both detection and confirmation of arrhythmia onset may be based on a measurement of ventricular rate variability, and the detection criteria adjuster circuit 224 may set the patient-specific atrial tachyarrhythmia onset threshold to VR TH =VR BL -Δ1, and the patient-specific atrial tachyarrhythmia confirmation threshold was set to VR TH-C =VR BL -Δ2, where the difference Δ2<Δ1 and thus VR TH-C >VRTH . Higher confirmation threshold VR TH-C This may correspond to a higher specificity, which may reduce false positive detections of atrial tachyarrhythmia episodes.

[0079] The arrhythmia detector circuit 230 may use the same physiological signals or the same signal metrics as those used to detect the onset of an arrhythmia (e.g., ventricular rate variability exemplified above) to confirm an arrhythmia. Alternatively, different types of physiological signals or different signal metrics may be used to confirm the detection of an arrhythmia. By way of example and not limitation, signal metrics representing cardiac characteristics for the detection of the onset of an atrial tachyarrhythmia may include one or more of ventricular rate variability or CL variability, ventricular rate clustering or histogram characteristics, or a double decremental ventricular rate pattern. Signal metrics representing cardiac characteristics for confirmation of an atrial tachyarrhythmia may include one or more of morphological similarity, an atrioventricular block metric such as the Wenckebach score, or a signal quality measurement such as the SNR.

[0080] like Figure 2 As shown, the detection standard circuit 220 or the arrhythmia detector circuit 230 can include a circuit group, which includes one or more other circuits or sub-circuits. The circuits or sub-circuits can perform the functions, methods and techniques described herein alone or in combination. In one example, the hardware of the circuit group can be designed to implement specific operations (e.g., hard-wired) invariably. In one example, the hardware of the circuit group can include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.), which include physically modified (e.g., magnetic, electrical, removably placed constant mass particles, etc.) to encode computer-readable media for specific operation instructions. When connecting physical components, the fundamental electrical properties of the hardware composition change, such as from insulators to conductors, and vice versa. These instructions enable embedded hardware (e.g., execution units or loading mechanisms) to create members of the circuit group in hardware via variable connections so as to implement parts of specific operations when operating. Therefore, when the device is working, the computer-readable medium is coupled to other components of the circuit group members in a communication manner. In one example, any physical component can be used in more than one member of more than one circuit group. For example, in operation, an execution unit may be used in a first circuit of a first circuit group at one point in time, and reused by a second circuit in the first circuit group, or reused by a third circuit in the second circuit group at a different time.

[0081] In various examples, the detection criteria circuit 220 and the arrhythmia detector circuit 230 can be implemented as part of a microprocessor circuit. The microprocessor circuit can be a special-purpose processor such as a digital signal processor, an application-specific integrated circuit (ASIC), a microprocessor, or other type of processor for processing information including physiological signals received from the sensor circuit 210. Alternatively, the microprocessor circuit can be a general-purpose processor that can receive and execute a set of instructions for performing the functions, methods, or techniques described herein.

[0082] Controller circuit 240 controls the operation of sensor circuit 210, detection criteria circuit 220, arrhythmia detector circuit 230, user interface unit 250 and the data and instruction flow between these components. User interface unit 250 may include input devices and output devices. In one example, at least a portion of user interface unit 250 may be implemented in external system 130. Input devices may receive user's programming input, such as parameters for adjusting detection criteria and parameters for detecting arrhythmias. Input devices may include keyboards, on-screen keyboards, mice, trackballs, touch pads, touch screens or other fixed-point or navigation devices. Input devices may enable system users to program parameters for sensing physiological signals, detecting arrhythmias, and generating alarms, etc.

[0083] The output device can generate a human-perceivable presentation of the detected arrhythmia. The output device can include a display for displaying sensed physiological signals, intermediate measurements or calculated values, such as patient baseline cardiac characteristics and patient-specific detection criteria, etc. The output unit can include a printer for printing a hard copy of the detection information. The information can be presented in a table, chart, diagram, or any other type of text, table, or graphic presentation format. The presentation of the output information can include audio or other media formats to send an alarm of the detected arrhythmia event to the system user. In one example, the output device can generate an alarm, an alert, an emergency call, or other forms of warning to notify the system user of the detected arrhythmia event with a signal.

[0084] Optional therapy circuit 260 can be configured to deliver therapy to the patient in response to a detected arrhythmia. Examples of therapy can include electrical stimulation therapy delivered to the heart, neural tissue, other target tissue, cardioversion therapy, defibrillation therapy, or drug therapy. In some examples, therapy circuit 260 can modify an existing therapy, such as adjusting stimulation parameters or drug dosage.

[0085] Figure 3An example of a system 300 for adjusting arrhythmia detection criteria based on a patient's baseline cardiac characteristics is generally shown. System 300 represents an embodiment of at least a portion of arrhythmia detection system 200 and includes sensor circuit 210 and detection criteria circuit 220, which are described above with reference to Figure 2 System 300 may additionally include a sinus rhythm detector 330 configured to detect sinus rhythm from physiological signal 302 sensed from the patient using sensor circuit 210. In one example, sinus rhythm detector 330 may generate a signal metric (X) representing a cardiac characteristic from signal 302 and compare the signal metric with a population-based arrhythmia detection threshold (X) stored in memory circuit 340. TH P ) to detect sinus rhythm. Threshold X based on population TH_P It can be determined based on arrhythmic episodes from a patient cohort, such as patients at risk for arrhythmia.

[0086] In response to the detected sinus rhythm, the baseline cardiac characteristics detector 222 may determine a baseline cardiac characteristic of the patient using the physiological signal 303, which may be a physiological signal of the same type as the signal 302 used to detect the sinus rhythm. Alternatively, the physiological signal 303 may be a physiological signal of a different type than the physiological signal 302. The baseline cardiac characteristics detector 222 may determine the baseline cardiac characteristics from the physiological signal 303. The baseline cardiac characteristics may be of a different type than the signal metric used to detect the sinus rhythm. For example, if the morphological similarity with a predetermined morphological template exceeds a population-based threshold, the sinus rhythm detector 330 may detect sinus rhythm, and the baseline cardiac characteristics detector 222 may determine the baseline ventricular rate variability during the period when the sinus rhythm was detected.

[0087] Alternatively, the baseline cardiac characteristic may be of the same type as the signal metric used to detect sinus rhythm. In this case, the detection criteria adjuster circuit 224 may use a population-based threshold associated with the same signal metric to determine a patient-specific detection criterion, such as an arrhythmia detection threshold or an arrhythmia confirmation threshold. In one example, the detection criteria adjuster circuit 224 may adjust the population-based threshold X by, for example, according to the following equation (2): TH_P To determine the patient-specific arrhythmia detection threshold X TH :

[0088] X TH = X TH_P + δ (2)

[0089] The amount of adjustment delta can be based on baseline cardiac characteristics. In the example where the signal metric represents ventricular rate specificity, the adjustment can be proportional to the baseline ventricular rate variability, so that a large adjustment delta can be used if the patient has a more variable baseline ventricular rate. If the patient's baseline ventricular rate is stable, a smaller positive adjustment delta or a negative adjustment delta can be applied to the population-based threshold to generate a patient-specific detection threshold.

[0090] Figure 4A-4B is a diagram showing an example of patient-specific atrial fibrillation (AF) detection thresholds associated with signal metrics representing different cardiac characteristics. Patient-specific AF thresholds may be determined using system 300, which, as described above, may use patient baseline cardiac characteristic information to modify population-based thresholds. In particular, Figure 4A The trend of the ventricular rate scatter index over time for the subjects is shown. The ventricular rate scatter index represents a quantitative measure of ventricular rate specificity or cardiac cycle length. Smaller scatter index values ​​indicate a more stable ventricular rate and a less likely AF episode. Figure 4A As shown, the sinus rhythm detector 330 can detect sinus rhythm ("NSR") using a population-based threshold 410. The detection criteria adjuster circuit 224 can generate a patient-specific detection threshold 420, for example, by modifying the population-based threshold 410 according to equation (2) above. Because the ventricular rate scatter index is always below the population-based threshold 410, a negative adjustment δ can be used so that the patient-specific detection threshold 420 is lower than the population-based threshold 410. A lower patient-specific AF detection threshold corresponds to a higher sensitivity for detecting AF. The patient-specific detection threshold 420 can improve the detection of AF by reducing false negative detections (corresponding to events that fall between the thresholds 410 and 420) that would otherwise occur using the population-based threshold 410.

[0091] Figure 4B The trend of the Wenckebach score of the subject over time is shown. The Wenckebach score represents a quantitative measurement of a heart rhythm pattern or a stable heart rhythm. A higher Wenckebach score indicates a higher likelihood of a heart rhythm pattern, and thus an ongoing AF episode is less likely. The detection criteria adjuster circuit 224 can generate a patient-specific AF confirmation threshold 440 by modifying the population-based threshold 430 according to equation (2) above. Figure 4BAs shown, the Wenckebach score during sinus rhythm varies and approaches the population-based threshold 410. A negative adjustment delta may be used so that the patient-specific AF confirmation threshold 440 is lower than the population-based threshold 430. A lower patient-specific AF confirmation threshold corresponds to a higher specificity for detecting AF. The patient-specific detection threshold 440 may reduce false positive detections (corresponding to events falling between the thresholds 430 and 440) that would otherwise be produced using the population-based threshold 430.

[0092] Figure 5 2 is an example of detecting cardiac arrest using an R-wave amplitude trend measured by, for example, an ECG or EGM sensed by sensor circuit 210 and a patient-specific threshold. Cardiac arrest refers to the absence of electrical activity in the heart for an extended period of time. This may be due to sinus arrest, in which the heart's sinoatrial node momentarily stops producing electrical impulses that normally stimulate the myocardial tissue to contract and thus the heart to beat. Sinus arrest may cause severe dizziness, lightheadedness, near-syncope, or syncope or fainting episodes. Arrhythmia detection system 200 can be configured to detect cardiac arrest by comparing the R-wave amplitude to an amplitude threshold. In one example, a patient-specific R-wave amplitude threshold can be established using system 300, which can modify a population-based R-wave amplitude threshold using information about the patient's baseline R-wave amplitude during sinus rhythm.

[0093] In one example, the baseline cardiac characteristic detector 222 may determine a signal metric, such as a change in R-wave amplitude over time (ΔR). As an example and not limitation, the temporal change in R-wave amplitude may be calculated as a difference or ratio between a central tendency of a plurality of R-waves within a first time window (e.g., three consecutive R-waves) and a central tendency of a plurality of R-waves within a second time window preceding the first time window (e.g., three consecutive R-waves immediately preceding the first three consecutive R-waves in time). Figure 5 As shown, cardiac pause may cause the central tendency of the R wave amplitude to decrease, while the termination of pause may cause the central tendency of the R wave amplitude to increase. The detection criteria adjuster circuit 224 may determine the patient-specific onset threshold (ΔR) for detecting the onset of cardiac pause. TH ON ), and optionally determining a patient-specific termination threshold (ΔR TH_OFF ). Starting threshold (△R TH_ON ) and termination threshold (△R TH_OFF) can be determined by modifying the population-based threshold according to equation (2) above. For example, if the patient has a larger baseline R-wave amplitude, a smaller adjustment δ can be applied so that the patient-specific threshold does not differ significantly from the population-based threshold. Conversely, if the patient has a smaller baseline R-wave amplitude, a negative adjustment δ can be applied so that the patient-specific threshold may be significantly less than the population-based threshold. This can improve the sensitivity of detecting cardiac arrest in patients with lower than normal R-wave amplitudes.

[0094] Figure 6 6 is a flow chart illustrating an example of a method 600 for detecting an arrhythmia (e.g., an atrial tachyarrhythmia episode) in a patient. Examples of atrial tachyarrhythmias that can be detected using method 600 may include atrial fibrillation (AF), atrial flutter (AF), atrial tachycardia, paroxysmal supraventricular tachycardia (PSVT), and the like. Method 600 may be implemented and executed in an ambulatory medical device such as an implantable or wearable device or in a remote patient management system. In one example, method 600 may be implemented in and executed by arrhythmia detection circuit 160 in AMD 110, external system 130, or arrhythmia detection system 200.

[0095] Method 600 begins at step 610, where one or more physiological signals may be sensed from a subject. Examples of physiological signals may include electrocardiographic signals such as ECG or EGM, or signals indicating cardiac mechanical activity such as pressure, impedance, heart sounds, or respiratory signals. The sensed physiological signals may be pre-processed, including amplification, digitization, filtering, or other signal conditioning operations. In some examples, patient physiological signals may be sensed and stored in a storage device such as an electronic medical record system, and retrieved for use, such as according to method 600.

[0096] At 620, a baseline cardiac characteristic may be determined using physiological signals sensed from the patient when the patient is not experiencing an arrhythmia (e.g., during sinus rhythm), such as automatically determined by baseline cardiac characteristic detector 222. The baseline cardiac characteristic may include statistical or morphological signal metrics generated from the physiological signals sensed during sinus rhythm. In the example of detecting atrial tachyarrhythmias, the baseline cardiac characteristic may include a baseline ventricular rate or cardiac cycle length (CL) variability metric, a baseline ventricular rate pattern such as a continuous decrease in ventricular rate for at least two cardiac cycles (referred to as a "double decrement" pattern), a baseline ventricular rate cluster or signature captured from a ventricular rate histogram, a quantitative measure of a rhythm pattern such as a Wenckebach score, a signal morphology metric representing the regularity of ventricular depolarization, or a signal quality metric, among other metrics generated from physiological signals during sinus rhythm.

[0097] At 630, the determined baseline cardiac characteristics may be used to determine patient-specific arrhythmia detection criteria, such as by the detection criteria adjuster circuit 224. The patient-specific arrhythmia detection criteria include a patient-specific arrhythmia detection threshold (X) for the cardiac characteristics. TH ). Patient-specific threshold X TH The patient-specific detection threshold X may be calculated using a function of the patient's baseline cardiac characteristics, such as, for example, as represented in equation (1) above, by subtracting a specific offset from a signal metric indicative of the patient's baseline cardiac characteristics. Alternatively, the patient-specific detection threshold X may be determined based on an association between various baseline cardiac characteristics and corresponding detection thresholds created and stored in memory. TH .

[0098] Patient-specific arrhythmia detection criteria may additionally include a patient-specific arrhythmia confirmation threshold for cardiac characteristics. The confirmation threshold may be different from the detection criteria and correspond to a higher specificity than the confirmation threshold. The detection threshold may correspond to a higher sensitivity than the confirmation threshold. For example, both detection and confirmation of arrhythmia onset may be based on measurements of ventricular rate variability, and the patient-specific atrial tachyarrhythmia confirmation threshold may be set to a level that is higher than the patient-specific atrial tachyarrhythmia detection threshold. This may help reduce false positive declarations of atrial tachyarrhythmia episodes, thereby maintaining a higher specificity for detected atrial tachyarrhythmia episodes.

[0099] At 640, a target arrhythmia, such as an atrial tachyarrhythmia, may be detected, for example, using a physiological signal sensed from the patient and a patient-specific arrhythmia detection criteria using an arrhythmia detector circuit 230. A signal metric may be generated based on the physiological signal, and a comparison between the signal metric and a patient-specific atrial tachyarrhythmia detection threshold may be used to detect an atrial tachyarrhythmia episode. The physiological signal used to detect the arrhythmia may be of a different type (e.g., sensed using a different physiological sensor) than the physiological signal used to establish the baseline cardiac characteristics. In some examples, step 640 also includes using a patient-specific confirmation threshold to confirm or deny the detected arrhythmia. Confirmation may be based on the same physiological signal or the same signal metric as used to detect the onset of the arrhythmia. Alternatively, different types of physiological signals or different signal metrics may be used to confirm the detection of an arrhythmia.

[0100] The detected arrhythmia (optionally further confirmed arrhythmia) may be provided to one or more of processes 652, 654, or 656. At 652, the arrhythmia may be output to a user or process, such as via an output device of user interface 250. In one example, the detected arrhythmia episode may be displayed on a display, including sensed physiological signals, patient baseline cardiac characteristics, and patient-specific detection criteria, etc. A hard copy of the detection information may be generated. In various examples, an alarm, alert, emergency call, or other form of warning may be generated to signal a system user about the detected arrhythmia episode.

[0101] At 654, a recommendation may be generated and provided to the user. The recommendation may include one or more of: further diagnostic tests to be performed, antiarrhythmic therapy to treat the detected arrhythmia or to mitigate complications of the arrhythmia. The recommendation may include adjusting one or more arrhythmia detection parameters, such as the offset relative to the patient's baseline cardiac characteristics provided in equation (1). Method 600 may include optional step 656, wherein, for example, via Figure 2 The optional therapy circuit 260 shown in FIG. 2 delivers therapy to the patient in response to the detected arrhythmia. Examples of therapy may include electrical stimulation therapy delivered to the heart, neural tissue, other target tissue, cardioversion therapy, defibrillation therapy, or drug therapy involving the delivery of drugs to tissues or organs. In some examples, an existing therapy or treatment regimen may be modified to treat the detected arrhythmia.

[0102] Figure 7 6 is a flow chart illustrating an example of a method 700 for determining patient-specific criteria for detecting arrhythmias. Method 700 represents an embodiment of a portion of method 600, such as steps 620-630, and may be implemented in and performed by system 300.

[0103] like Figure 7 As shown, sinus rhythm can be detected using physiological signals sensed from a patient at 701. A signal metric representing a cardiac characteristic can be generated from the sensed physiological signal and compared to a population-based arrhythmia detection threshold. The population-based threshold can be determined using a patient cohort, such as arrhythmic episodes in patients at risk for arrhythmia.

[0104] A baseline cardiac characteristic may be determined during a training session having a training duration. At 702, the training duration may be adjusted for a signal metric used to establish the baseline cardiac characteristic. The training period may be different for different signal metrics. In one example, the training duration may be based on the amount of data collected or a user-specified priority of the signal metric. In another example, the training duration may be based on a quality measurement of the determined baseline cardiac characteristic (e.g., the variability of the measurements during training). Such training period adjustment allows for a more reliable baseline characterization prior to generating a patient-specific detection threshold.

[0105] At 703, a patient baseline cardiac characteristic may be determined in response to the detected sinus rhythm. The physiological signal used to determine the baseline cardiac characteristic may be the same type of signal used to detect sinus rhythm. Alternatively, a different type of physiological signal (such as sensed by a different physiological sensor) may be used to determine the baseline cardiac characteristic or to detect sinus rhythm.

[0106] One or more baseline features may be updated to account for changes in the patient's condition. At 704, a baseline update condition is checked to determine whether the baseline update is in accordance with a specified periodic update schedule, or whether an update trigger condition such as a change in the patient's medical condition, posture, or physical activity has been met. If the baseline update condition is met, the baseline feature may be updated by re-detecting sinus rhythm at 701; otherwise, at 705, the baseline cardiac feature may be used to modify the population-based threshold to generate a patient-specific arrhythmia detection threshold. The patient-specific arrhythmia detection threshold may be set to a value relative to the population-based threshold, for example, according to equation (2). In one example, the adjustment amount may be based on the degree of baseline ventricular rate variability. For example, if the patient's baseline ventricular rate varies greatly, a larger adjustment may be used. Also at 705, the baseline cardiac feature may be used to modify the population-based threshold, for example, according to equation (2) above, to generate a patient-specific arrhythmia confirmation threshold. At 640, a patient-specific arrhythmia detection criteria may be used to detect arrhythmias.

[0107] Figure 8 A block diagram generally illustrates an example machine 800 upon which any one or more of the techniques (eg, methods) discussed herein may be executed. Portions of this description may apply to computing frameworks of various portions of an LCP device, an IMD, or an external programmer.

[0108] In alternative embodiments, machine 800 can be operated as a stand-alone device, or can be connected (e.g., networked) to other machines. In a networked deployment, machine 800 can operate with the capabilities of a server machine, a client machine, or both in a server-client network environment. In one example, machine 800 can act as a peer in a peer-to-peer (P2P) (or other distributed) network environment. Machine 800 can be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a mobile phone, a web device, a network router, a switch or a bridge, or any machine capable of executing (sequential or otherwise) instructions, wherein the instructions specify the actions to be taken by the machine. In addition, although only a single machine is shown, the term "machine" should also be considered to include any machine collection that executes a set (or multiple sets) of instructions individually or jointly to perform any one or more methods discussed herein, such as cloud computing, software as a service (SaaS), other computer cluster configurations.

[0109] The examples described herein may include logic or multiple components or mechanisms, or be operated by logic or multiple components or mechanisms. A circuit group is a collection of circuits implemented in a tangible entity including hardware (e.g., simple circuits, gates, logic, etc.). The members of the circuit group may change flexibly over time and with changes in the underlying hardware. The circuit group includes members that can perform specified operations when the operation is performed individually or in combination. In one example, the hardware of the circuit group may be designed to implement specified operations (e.g., hardwired) invariably. In one example, the hardware of the circuit group may include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.), which include physically modified (e.g., magnetic, electrical, particles of constant mass can be placed movably, etc.) to encode computer-readable media of specific operation instructions. When the physical components are connected, the fundamental electrical properties of the hardware composition change, such as from an insulator to a conductor, and vice versa. These instructions enable embedded hardware (e.g., execution units or loading mechanisms) to create members of the circuit group in hardware via variable connections so as to implement parts of specific operations when operating. Therefore, when the device is working, the computer-readable medium is coupled to other components of the circuit group members in a communication manner. In one example, any physical component can be used in more than one member of more than one circuit group. For example, in operation, an execution unit can be used in a first circuit of a first circuit group at one point in time, and reused by a second circuit in the first circuit group at a different time, or reused by a third circuit in the second circuit group.

[0110] The machine (e.g., computer system) 800 may include a hardware processor 802 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination of these), a main memory 804, and a static memory 806, some or all of which may communicate with each other via an interconnect (e.g., a bus) 808. The machine 800 may also include a display unit 810 (e.g., a raster display, a vector display, a holographic display, etc.), an alphanumeric input device 812 (e.g., a keyboard), and a user interface (UI) navigation device 814 (e.g., a mouse). In one example, the display unit 810, the input device 812, and the UI navigation device 814 may be a touch screen display. The machine 800 may additionally include a storage device (e.g., a drive unit) 816, a signal generating device 818 (e.g., a speaker), a network interface device 820, and one or more sensors 821, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or other sensors. The machine 800 may include an output controller 828, such as a serial (e.g., Universal Serial Bus (USB)), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate with or control one or more peripheral devices (e.g., printers, card readers, etc.).

[0111] The storage device 816 may include a machine-readable medium 822 having stored thereon one or more sets of data structures or instructions 824 (e.g., software), wherein the data structures or instructions include or are used by any one or more of the techniques or functionality described herein. The instructions 824 may also reside, completely or at least partially, in the main memory 804, static storage 806, or in the hardware processor 802 during execution thereof by the machine 800. In one example, one or any combination of the hardware processor 802, the main memory 804, the static storage 806, or the storage device 816 may constitute a machine-readable medium.

[0112] Although the machine-readable medium 822 is illustrated as a single medium, the term “machine-readable medium” may include a single medium or multiple media (eg, a central or distributed database, and / or associated caches and servers) configured to store one or more instructions 824 .

[0113] The term "machine-readable medium" may include any medium capable of storing, encoding or carrying instructions for execution by the machine 800 and causing the machine 800 to perform any one or more of the techniques of the present invention, or capable of storing, encoding or carrying data structures used by or related to such instructions. Non-limiting examples of machine-readable media may include solid-state memory, and optical and magnetic media. In one example, a massed machine-readable medium includes a machine-readable medium having a plurality of particles of a constant (e.g., stationary) mass. Therefore, a massed machine-readable medium is not a transient propagation signal. Specific examples of massed machine-readable media may include: non-volatile memory, such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices; disks, such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM optical disks.

[0114] The instructions 824 may also be sent or received over the communication network 826 using a transmission medium via the network interface device 820 using any of a variety of transmission protocols (e.g., frame relay, Internet Protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Exemplary communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), a mobile telephone network (e.g., a cellular network), a plain old telephone (POTS) network, and a wireless data network (e.g., a wireless network called a cellular network). The Institute of Electrical and Electronics Engineers (IEEE) 802.11 series of standards, known as 802.16 series of standards), IEEE 802.15.4 series of standards, peer-to-peer (P2P) networks, etc. In one example, the network interface device 820 may include one or more physical jacks (e.g., Ethernet, coaxial or telephone jacks) or one or more antennas to connect to the communication network 826. In one example, the network interface device 820 may include multiple antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) technologies. The term "transmission medium" should be deemed to include any intangible medium that can store, encode or carry instructions for execution by the machine 800, and includes digital or analog communication signals or other intangible media that facilitate such software communication.

[0115] The above figures illustrate various embodiments. One or more features from one or more of these embodiments may be combined to form other embodiments.

[0116] The method examples described herein may be implemented at least in part by a machine or computer. Some examples may include a computer-readable medium or a machine-readable medium, which is encoded with instructions operable to configure an electronic device or system to perform the methods described in the above examples. The implementation of this method may include codes such as microcode, assembly language code, high-level language code, etc. Such code may include computer-readable instructions for performing various methods. These codes may constitute part of a computer program product. In addition, during execution or at other times, the code may be tangibly stored on one or more volatile or non-volatile computer-readable media.

[0117] The foregoing detailed description is intended to be illustrative rather than limiting.The scope of the disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.

Claims

1. A system for detecting cardiac arrhythmia in a patient, comprising: The detection standard circuit is configured as follows: receiving a first physiological signal sensed from the patient when the patient does not have arrhythmia; detecting sinus rhythm of the patient using the first physiological signal and a population-based threshold; determining a baseline cardiac characteristic using the first physiological signal during the detected period of sinus rhythm; and determining a patient-specific arrhythmia detection threshold as a deviation from the determined baseline cardiac characteristic; as well as An arrhythmia detector circuit is configured to detect an arrhythmia episode using a second physiological signal sensed from the patient and the patient-specific arrhythmia detection threshold.

2. The system of claim 1, wherein: The detection criteria circuit is further configured to determine a patient-specific arrhythmia confirmation threshold using the determined baseline cardiac characteristics; and the arrhythmia detector circuitry configured to use the patient-specific arrhythmia confirmation threshold to confirm or deny a detected arrhythmia episode; Wherein the patient-specific arrhythmia confirmation threshold corresponds to a higher specificity for detecting arrhythmias than the arrhythmia detection threshold.

3. The system of claim 2, wherein the arrhythmia detector circuit is configured to: generating a first signal metric and a second signal metric different from the first signal metric respectively from the second physiological signal; detecting an arrhythmia episode by comparing the first signal metric to the patient-specific arrhythmia detection threshold; and The detected arrhythmia episode is confirmed or denied by comparing the second signal metric to the patient-specific arrhythmia confirmation threshold.

4. The system of any one of claims 1-3, wherein the baseline cardiac characteristics include statistical or morphological signal characteristics or ventricular rate variability measures of the first physiological signal.

5. The system of any one of claims 1-3, wherein the detection criteria circuit is configured to determine the patient-specific arrhythmia detection threshold by modifying a population-based threshold according to the baseline cardiac characteristics.

6. The system of any one of claims 1-3, wherein the detection criteria circuit is configured to update the baseline cardiac characteristic of the patient periodically or in response to a change in the patient's condition.

7. The system of claim 6, wherein the detection criteria circuit is configured to update the baseline cardiac characteristic in response to a change in posture.

8. The system according to any one of claims 1 to 3, wherein the detection standard circuit is configured as: determining the baseline cardiac characteristic during a training session having a training duration; assessing quality measures of the identified baseline cardiac characteristics; as well as The training duration is adjusted based on the quality measure of the baseline cardiac characteristic.

9. A system according to any one of claims 1-3, wherein the arrhythmia includes an atrial tachyarrhythmia, and the patient-specific arrhythmia detection threshold includes a patient-specific atrial tachyarrhythmia detection threshold, and wherein the arrhythmia detector circuit is configured to detect an atrial tachyarrhythmia episode using the patient-specific atrial tachyarrhythmia detection threshold.

10. The system of claim 9, wherein the baseline cardiac characteristic comprises a measure of ventricular rate variability during sinus rhythm.

11. The system of claim 9, wherein the baseline cardiac characteristics include a measure of atrioventricular block during sinus rhythm.

12. The system of any one of claims 1-3, wherein the arrhythmia comprises cardiac arrest, and wherein: The detection criteria circuit is configured to determine a patient-specific R-wave amplitude threshold using the baseline cardiac characteristic; and The arrhythmia detector circuit is configured to determine an R-wave amplitude metric from the second physiological signal and to detect an asystolic episode by comparing the R-wave amplitude metric to the patient-specific R-wave amplitude threshold.

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