Determine different sleep stages in patients of a wearable medical device

Abstract

A patient monitoring device is provided that is configured to monitor a patient's cardiac activity and sleep stage information. The device includes a plurality of electrodes for obtaining an electrocardiogram signal, i.e., an ECG signal, from the patient, at least one motion sensor configured to generate a motion signal based on the patient's movement, and at least one processor. The processor is configured to: derive motion parameters from the motion signal; derive ECG parameters from the ECG signal; determine whether the patient is in a stationary sleep stage or a non-stationary sleep stage based on the motion parameters and the ECG parameters; adjust one or more cardiac arrhythmia detection parameters such that when the patient is in the stationary sleep stage, the device operates in a first monitoring and handling mode; and use the first monitoring and handling mode to monitor the patient for cardiac arrhythmias.

Description

[0001] Cross - Reference to Related Applications

[0002] This application claims priority under 35 U.S.C.§119(e) to U.S. Provisional Application No. 63 / 002,663, filed on March 31, 2020, entitled "Determining Different Sleep Stages in a Wearable Medical Device Patient", the entire content of which is incorporated herein by reference. Background of the Invention

[0003] The present invention relates to monitoring a sleeping patient who is prescribed a wearable medical device.

[0004] If left untreated, heart failure can lead to certain life - threatening cardiac arrhythmias. Both atrial and ventricular arrhythmias are common in patients with heart failure. One of the most lethal cardiac arrhythmias is ventricular fibrillation, which occurs when normal regular electrical impulses are replaced by irregular and rapid impulses, causing the myocardium to stop contracting normally. Since the victim has no perceivable warning of the impending fibrillation, death often occurs before necessary medical assistance can arrive. Other cardiac arrhythmias can include a heart rate that is too slow, called bradycardia, or a heart rate that is too fast, called tachycardia. Cardiac arrest can occur in patients in whom various cardiac arrhythmias (such as ventricular fibrillation, ventricular tachycardia, pulseless electrical activity (PEA), and asystole (when the heart stops all electrical activity)) result in an insufficient level of blood flow from the heart to the brain and other vital organs that sustain life. Monitoring patients with heart failure to early assess heart failure symptoms and provide intervention as soon as possible is generally useful.

[0005] Patients at risk of, hospitalized for, or otherwise suffering from an adverse cardiac condition may be prescribed a wearable cardiac monitoring and / or treatment device. In addition to the wearable device, a battery charger and a set of rechargeable batteries may also be given to the patient. Since wearable devices are typically prescribed for continuous or near - continuous use (e.g., removed only for bathing), the patient wears the device during all daily activities such as walking, sitting, climbing stairs, resting or sleeping, and other similar daily activities.

[0006] When asleep, a patient's body undergoes physiological changes during the course of the sleep cycle. For example, the patient may be in a rapid eye movement (REM) sleep stage, a light sleep stage (non-REM), or one of different deep sleep stages (e.g., deep sleep graded from 1 to 3 or other predetermined levels), which different deep sleep stages are characterized by a reduction in movement of the patient's body during the deep sleep stage. Summary of the Invention

[0007] In an example, there is provided a patient monitoring device configured to monitor a patient's cardiac activity and sleep stage information. The patient monitoring device includes: a plurality of electrodes configured to be externally coupled to a patient to obtain an electrocardiogram signal, i.e., an ECG signal, from the patient and to provide a therapeutic shock to the patient in response to detecting a cardiac arrhythmia; at least one motion sensor configured to generate at least one motion signal based on movement of the patient; and at least one processor operatively coupled to the plurality of electrodes and the at least one motion sensor. The at least one processor is configured to: receive the at least one motion signal from the at least one motion sensor and derive one or more motion parameters from the at least one motion signal; receive the ECG signal from the at least one electrode and derive one or more ECG parameters from the ECG signal; determine whether the patient is in a stationary sleep stage or a non-stationary sleep stage based on an analysis of the one or more motion parameters and the one or more ECG parameters; in the case where the at least one processor determines that the patient is in a stationary sleep stage, adjust one or more cardiac arrhythmia detection parameters such that the device operates in a first monitoring and treatment mode; and use the first monitoring and treatment mode to monitor the patient for cardiac arrhythmia.

[0008] The implementation of the patient monitoring device may include one or more of the following features.

[0009] In the patient monitoring device, the at least one processor may be configured to determine whether a patient is in a stationary sleep stage by being configured to perform the following operations: monitor the ECG signal to determine whether a heart rate deviation from the patient's baseline resting heart rate exceeds a deviation threshold over a period of time; analyze the one or more movement parameters over the period of time; and determine whether the patient is in a stationary sleep stage based on the heart rate deviation and the analysis of the one or more movement parameters over the period of time. In some examples, the deviation threshold includes at least one of a deviation greater than 1% from the baseline resting heart rate, a deviation greater than 2% from the baseline resting heart rate, and a deviation greater than 5% from the baseline resting heart rate. In some examples, the period of time may include at least one of 5 minutes, 7 minutes, 10 minutes, 30 minutes, 45 minutes, and 1 hour.

[0010] In the patient monitoring device, the at least one processor may be configured to: adjust the one or more cardiac arrhythmia detection parameters such that the device operates in a second monitoring and treatment mode; and use the second monitoring and treatment mode to monitor the patient for the cardiac arrhythmia. In some examples, the at least one processor is further configured to: monitor the patient using the second monitoring and treatment mode; determine whether the patient has transitioned from the non-stationary sleep stage to the stationary sleep stage; and in the case where the at least one processor determines that the patient has transitioned from the non-stationary sleep stage to the stationary sleep stage, monitor the patient using the first monitoring and treatment mode.

[0011] In the patient monitoring device, the at least one processor may be configured to, in the case where the at least one processor determines that the patient is in a stationary sleep stage, start monitoring physiological signals other than the ECG signal. In some examples, the patient monitoring device may include a radio frequency sensor, i.e., an RF sensor, the physiological signals other than the ECG signal may include RF-based physiological signals, and the at least one processor may be configured to determine at least one of cardiac wall movement information and thoracic cavity fluid level information based on the RF-based physiological signals. In some examples, the patient monitoring device may include a cardiac vibration sensor, the physiological signals other than the ECG signal may include one or more cardiac vibration signals of the patient, and the at least one processor may be configured to determine one or more electromechanical parameters of the patient's heart based on the cardiac vibration signals.

[0012] In the patient monitoring device, the at least one processor may be configured to adjust one or more treatment parameters when the at least one processor determines that the patient is in a fixed sleep stage. In some examples, the one or more treatment parameters may include one or more of a pacing pulse rate, a high-energy pacing pulse level, a low-energy pacing pulse level, a defibrillation shock level, and defibrillation shock timing information.

[0013] In the patient monitoring device, the at least one processor may be configured to adjust one or more alert parameters when the at least one processor determines that the patient is in a fixed sleep stage. In some examples, the one or more alert parameters may include at least one of an alert type, an alert volume, an alert duration, and patient response time information.

[0014] In the patient monitoring device, the one or more movement parameters may include rotational movement parameters that quantify rotational movement of the patient measured by the at least one movement sensor.

[0015] In the patient monitoring device, the fixed sleep stage may include at least one of an N3 sleep stage, an N4 sleep stage, and a REM sleep stage.

[0016] In the patient monitoring device, the non-fixed sleep stage may include at least one of wakefulness, an N1 sleep stage, and an N2 sleep stage.

[0017] In the patient monitoring device, the one or more movement parameters may include one or more of patient respiration information, patient limb movement information, and patient body position information.

[0018] In the patient monitoring device, the at least one processor may further be configured to derive one or more additional movement parameters from one or more impedance-based measurements from the plurality of electrodes.

[0019] In the patient monitoring device, the one or more ECG parameters may include one or more of the following: heart rate, heart rate variability, ventricular premature beat load or count (i.e., PVC load or count), atrial fibrillation load, intermittency, heart rate turbulence, QRS height, QRS width, change in size or shape of the morphology of the ECG signal, cosine R-T, artificial pacing, QT interval, QT variability, T wave width, T wave alternans, T wave variability, and ST segment change.

[0020] In the patient monitoring device, the one or more cardiac arrhythmia detection parameters may include one or more of a ventricular tachycardia onset heart rate, a ventricular fibrillation onset heart rate, a bradycardia onset heart rate, a tachycardia onset heart rate, and a cardiac arrest onset threshold.

[0021] In the patient monitoring device, the at least one processor may further be configured to adjust at least one of a cardiac arrhythmia detection confidence level and a noise threshold when the at least one processor determines that the patient is in a stationary sleep stage.

[0022] In the patient monitoring device, the at least one processor may further be configured to: monitor a patient's heart rate to derive patient heart rate information when the processor determines that the patient is in a stationary sleep stage; compare the patient heart rate information with a patient's baseline resting heart rate; and adjust and / or validate the baseline resting heart rate based on the comparison of the heart rate information with the baseline resting heart rate to determine an updated baseline resting heart rate of the patient.

[0023] In the patient monitoring device, the at least one processor may further be configured to determine at least one occurrence of a PVC from the ECG signal when the processor determines that the patient is in a stationary sleep stage; monitor changes in the ECG signal at the time of the occurrence of the PVC to measure a cardiac response of the patient's heart after the PVC; determine a heart rate turbulence value of the patient based on the changes in the monitored electrical signal; and store the heart rate turbulence value on a computer-readable medium operatively coupled to the at least one processor for analysis.

[0024] In another example, a second patient monitoring device is provided that is configured to monitor a patient's cardiac activity and sleep stage information. The second patient monitoring device includes: a plurality of electrodes configured to be externally coupled to the patient to obtain an ECG signal from the patient and to provide a therapeutic shock to the patient in response to detecting a cardiac arrhythmia; at least one motion sensor configured to generate at least one motion signal based on the patient's movement; and at least one processor operatively coupled to the plurality of electrodes and the at least one motion sensor. The at least one processor is configured to: receive the at least one motion signal from the at least one motion sensor and derive one or more motion parameters from the at least one motion signal; receive the ECG signal from the at least one electrode and derive one or more ECG parameters from the ECG signal; determine whether the patient is in a stationary sleep stage or a non-stationary sleep stage based on an analysis of the one or more motion parameters and the one or more ECG parameters; in the case where the at least one processor determines that the patient is in a stationary sleep stage, adjust one or more alarm parameters such that the device operates in a first alarm mode; and use the first alarm mode to warn the patient of the cardiac arrhythmia.

[0025] The implementation of the second patient monitoring device may include one or more of the following features.

[0026] In the second patient monitoring device, the at least one processor may be configured to determine whether the patient is in a stationary sleep stage by being configured to: monitor the ECG signal to determine whether a heart rate deviation from the patient's baseline resting heart rate exceeds a deviation threshold over a period of time; analyze the one or more motion parameters over the period of time; and determine whether the patient is in a stationary sleep stage based on the heart rate deviation and the analysis of the one or more motion parameters over the period of time. In some examples, the deviation threshold may include at least one of a deviation greater than 1% from the baseline resting heart rate, a deviation greater than 2% from the baseline resting heart rate, and a deviation greater than 5% from the baseline resting heart rate. In some examples, the period of time may include at least one of 5 minutes, 7 minutes, 10 minutes, 30 minutes, 45 minutes, and 1 hour.

[0027] In the second patient monitoring device, the at least one processor may be configured to: adjust the one or more alarm parameters such that the device operates in a second alarm mode; and use the second alarm mode to warn the patient of the cardiac arrhythmia.

[0028] In the second patient monitoring device, the one or more alert parameters may include at least one of an alert type, an alert volume, an alert duration, and patient response time information.

[0029] In the second patient monitoring device, the one or more motion parameters may include rotational motion parameters that quantify the rotational motion of the patient measured by the at least one motion sensor.

[0030] In the second patient monitoring device, the fixed sleep stages may include at least one of N3 sleep stage, N4 sleep stage, and REM sleep stage.

[0031] In the second patient monitoring device, the non-fixed sleep stages may include at least one of wakefulness, N1 sleep stage, and N2 sleep stage.

[0032] In the second patient monitoring device, the one or more motion parameters may include one or more of patient respiration information, patient limb movement information, and patient body position information.

[0033] In the second patient monitoring device, the at least one processor may further be configured to derive one or more additional motion parameters from one or more impedance-based measurements from the plurality of electrodes.

[0034] In the second patient monitoring device, the one or more ECG parameters may include one or more of the following: heart rate, heart rate variability, ventricular premature beat burden or count (i.e., PVC burden or count), atrial fibrillation burden, pauses, heart rate turbulence, QRS height, QRS width, change in magnitude or shape of the morphology of the ECG signal, cosine R-T, artificial pacing, QT interval, QT variability, T wave width, T wave alternans, T wave variability, and ST segment change.

[0035] In another example, a third patient monitoring device is provided that is configured to monitor a patient's cardiac activity and sleep stage information. The third patient monitoring device includes: a plurality of electrodes configured to be externally coupled to the patient to obtain an electrocardiogram signal, i.e., an ECG signal, from the patient, and to provide a therapeutic shock to the patient in response to detecting a cardiac arrhythmia; at least one physiological sensor configured to be externally coupled to the patient to obtain physiological signals other than the ECG signal; at least one motion sensor configured to generate at least one motion signal based on the patient's movement; and at least one processor operatively coupled to the plurality of electrodes and the at least one motion sensor. The at least one processor is configured to: receive the at least one motion signal from the at least one motion sensor and derive one or more motion parameters from the at least one motion signal; receive the ECG signal from the at least one electrode and derive one or more ECG parameters from the ECG signal; determine whether the patient is in a stationary sleep stage or a non-stationary sleep stage based on an analysis of the one or more motion parameters and the one or more ECG parameters; in the case where the at least one processor determines that the patient is in a stationary sleep stage, start monitoring the physiological signals other than the ECG signal; and determine at least one additional physiological parameter of the patient based on an analysis of the physiological signals other than the ECG signal.

[0036] The implementation of the third patient monitoring device may include one or more of the following features.

[0037] In the third patient monitoring device, the at least one physiological sensor may include a radio frequency sensor, i.e., an RF sensor, and the physiological signals other than the ECG signal may include RF-based physiological signals. In some examples, the at least one additional physiological parameter may include at least one of cardiac wall movement information and thoracic fluid level information from the RF-based physiological signals.

[0038] In the third patient monitoring device, the at least one physiological sensor may include a cardiac vibration sensor, and the physiological signals other than the ECG signal may include one or more cardiac vibration signals of the patient. In some examples, the at least one additional physiological parameter may include one or more electromechanical parameters of the patient's heart based on the cardiac vibration signals.

[0039] In the third patient monitoring device, the at least one processor may be configured to determine whether a patient is in a stationary sleep stage by being configured to perform the following operations: monitor the ECG signal to determine whether a heart rate deviation from the patient's baseline resting heart rate exceeds a deviation threshold over a period of time; analyze the one or more movement parameters over the period of time; and determine whether the patient is in a stationary sleep stage based on the heart rate deviation and the analysis of the one or more movement parameters over the period of time. In some examples, the deviation threshold may include at least one of a deviation greater than 1% from the baseline resting heart rate, a deviation greater than 2% from the baseline resting heart rate, and a deviation greater than 5% from the baseline resting heart rate. In some examples, the period of time includes at least one of 5 minutes, 7 minutes, 10 minutes, 30 minutes, 45 minutes, and 1 hour.

[0040] In the third patient monitoring device, the one or more movement parameters may include a rotational movement parameter that quantifies the rotational movement of the patient measured by the at least one movement sensor.

[0041] In the third patient monitoring device, the stationary sleep stage may include at least one of N3 sleep stage, N4 sleep stage, and REM sleep stage.

[0042] In the third patient monitoring device, the non-stationary sleep stage may include at least one of wakefulness, N1 sleep stage, and N2 sleep stage.

[0043] In the third patient monitoring device, the one or more movement parameters may include one or more of patient respiration information, patient limb movement information, and patient body position information.

[0044] In the third patient monitoring device, the at least one processor may further be configured to derive one or more additional movement parameters from one or more impedance-based measurements from the plurality of electrodes.

[0045] In the third patient monitoring device, the one or more ECG parameters may include one or more of the following: heart rate, heart rate variability, ventricular premature beat burden or count (i.e., PVC burden or count), atrial fibrillation burden, intermittency, heart rate turbulence, QRS height, QRS width, change in size or shape of the morphology of the ECG signal, cosine R-T, artificial pacing, QT interval, QT variability, T wave width, T wave alternans, T wave variability, and ST segment change. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The following discussion of various aspects of at least one example refers to the accompanying drawings, which are not necessarily drawn to scale. The figures are included to provide illustration and further understanding of the various aspects and examples, and are incorporated into and form a part of this specification, but are not intended to limit the scope of the invention. The figures, together with the remainder of the specification, are used to explain the principles and operation of the described and claimed aspects and examples. In the figures, like or substantially like components shown in the various figures are represented by like reference numerals. For clarity, each component may not be labeled in every figure.

[0047] Figure 1A and Figure 1B show a sample sensor arrangement for use by a patient according to an example of the present invention.

[0048] Figure 2 show the output of a sample accelerometer according to an example of the present invention.

[0049] Figure 3 show a schematic diagram of a sample controller of a wearable medical device according to an example of the present invention.

[0050] Figure 4 show a sample controller having multiple sleep stage modes for operation according to an example of the present invention.

[0051] Figure 5A show a processing flow for determining which sleep stage a patient is in according to an example of the present invention.

[0052] Figure 5B show additional details of a processing flow according to an example of the present invention as Figure 5A shown.

[0053] Figure 5C show additional details of a processing flow according to an example of the present invention as Figure 5A shown.

[0054] Figure 6 show a processing flow for determining whether a patient has transitioned between sleep stages according to an example of the present invention.

[0055] Figure 7 show a processing flow for additional monitoring when a patient is in a fixed sleep stage according to an example of the present invention.

[0056] Figure 8 show a processing flow for establishing baseline sleep stage information of a patient according to an example of the present invention.

[0057] Figure 9A show a processing flow for measuring pacing capture when a patient is in a fixed sleep stage according to an example of the present invention.

[0058] Figure 9B Shows a processing flow for measuring heart rate variability when a patient is in a specific sleep stage according to an example of the present invention.

[0059] Figure 10 Shows a processing flow for optimizing sensing electrode pairs when a patient is in a specific sleep stage according to an example of the present invention.

[0060] Figures 11A to 11D Shows an example of a sample non-fixed medical device that can be prescribed to a heart failure patient according to the present invention. Detailed Description

[0061] Wearable medical devices (such as cardiac event monitoring and / or treatment devices) are used in clinical or outpatient settings to monitor and / or record various ECGs and other physiological signals of patients. These ECGs and other physiological signals can be used to monitor arrhythmias, and in the example devices described herein, provide treatments such as defibrillation or pacing shocks in the case of life-threatening arrhythmias. Example cardiac monitoring and treatment devices that can implement the sleep stage features and / or processes described herein include wearable defibrillators (also known as wearable cardioverter-defibrillators (WCDs)). Another example cardiac monitoring and treatment device that can implement the sleep stage features and / or processes described herein includes hospital wearable defibrillators (HWDs).

[0062] During continuous monitoring of a patient's arrhythmia condition (e.g., over the course of 24 hours a day, 7 days a week), the patient can regularly enter sleep periods that are typically characterized by reduced or no body movement. During such sleep periods, the wearable medical devices described herein can be automatically or manually caused to change their operation from a non-sleep period mode to a sleep period mode. For example, these devices can automatically enter the sleep period mode based on detecting certain parameters such as the patient's movement dropping below a specific patient motion threshold and / or the heart rate dropping below a predetermined heart rate threshold. Additionally or alternatively, these devices can automatically enter the sleep period mode based on the time of day (e.g., a preset time such as 2200 (10:00 p.m.)). In an implementation, the specific patient motion threshold, the predetermined heart rate threshold, and the time of day can be set via parameters controllable by the prescriber. For example, default values for these parameters can be set, and the prescriber can change the default to other desired values. Still additionally or alternatively, these devices can enter the sleep period mode based on user input (e.g., the patient indicates via a user interface parameter that the patient is about to go to sleep). Upon receiving such user input, the device can enter the sleep period mode after a predetermined delay (e.g., a default value of about 30 minutes that can be controlled by the prescriber to be different other desired values).

[0063] In an example, these devices can be configured to ignore short sleep periods such as naps. For example, these devices can be configured to enter a sleep period mode after a predetermined delay (e.g., a default value of about 45 minutes) that can be controlled by a prescriber to a desired value (e.g., set in a range from 15 minutes to 90 minutes). This predetermined delay period can be measured from when the device determines that the patient's movement has dropped below a specific patient movement threshold and / or a predetermined heart rate threshold.

[0064] The sleep period mode can have additional modes based on what sleep stage the patient is in. For example, when the patient is in a fixed sleep stage, the wearable medical device can monitor the patient in a fixed sleep stage mode that includes one or more operating parameters that have been adjusted to accommodate the patient's fixed sleep stage. In some examples, when the patient is in a non-fixed sleep stage, the wearable medical device can monitor the patient in a non-fixed sleep stage mode.

[0065] Devices that implement the sleep stage monitoring features and / or processes described herein provide advantages and benefits. For example, the implementations described herein help with better diagnosis and / or improvement of arrhythmia monitoring and management of a sleeping patient compared to implementations that do not include these features and / or processes. In one example, the implementations herein can better monitor a condition such as bradycardia by distinguishing certain arrhythmias from a low resting heart rate condition when the patient is in a fixed sleep stage. Such implementations can improve wearable defibrillators by appropriately monitoring and managing the arrhythmias that the patients are actually experiencing. Additionally, the implementations herein can help reduce false alarms during the time periods when the patient is asleep.

[0066] In another example advantage or benefit, the implementations herein can help reduce inappropriate shock management by making the alarm scheme (e.g., audio and / or vibration alarms) better adapt to the patient's sleep stage. In some examples, if the patient is in a deep sleep stage, the patient may not perceive an alarm or other warning generated by the device before an unwarranted management. Therefore, it is advantageous to determine the sleep stage of a sleeping patient and, if the patient is experiencing an arrhythmia, determine what action (if any) can be taken to adjust one or more operating parameters of the wearable medical device without affecting the patient's monitoring and / or management.

[0067] To address these and other aspects of enhancing the implementation of arrhythmia monitoring and treatment of a patient while sleeping, a system and process are provided that are configured to classify sleep stage information of a patient and modify one or more operating parameters of a wearable medical device based on the sleep stage information. For example, a WCD can include multiple sleep stage-based monitoring and treatment modes that can be selected by a processor based on what sleep stage the patient is currently in. For example, the multiple monitoring and treatment modes can include a default monitoring and treatment mode (non-sleep period mode) when the patient is awake. As described herein, the sleep period modes can also include a non-fixed sleep stage mode when the patient is in a non-fixed sleep stage and a fixed sleep stage mode when the patient is in a fixed sleep stage.

[0068] For example, a patient monitoring device configured to monitor a patient's cardiac activity and sleep stage information can include: multiple electrodes configured to be externally coupled to the patient, obtain an ECG signal from the patient, and provide a therapeutic shock to the patient in response to detecting a cardiac arrhythmia; and a motion sensor configured to generate a motion signal based on the patient's movement. The device can also include a processor operably coupled to the multiple electrodes and at least one motion sensor. In some examples, the processor can be configured to receive the motion signal from at least one motion sensor and derive one or more motion parameters from the at least one motion signal. The processor can also receive the ECG signal from the electrodes and derive one or more ECG parameters from the ECG signal. Based on the one or more motion parameters and the one or more ECG parameters, the processor can determine whether the patient is in a fixed sleep stage or a non-fixed sleep stage. If the patient is in a fixed sleep stage, the processor can adjust one or more cardiac arrhythmia detection parameters such that the patient monitoring device operates in a first monitoring and treatment mode and monitors the patient for cardiac arrhythmia using the first monitoring and treatment mode. In some examples, if the patient is in a non-fixed sleep stage, the processor can be configured to cause the patient monitoring device to operate in a second monitoring and treatment mode.

[0069] In a similar example, the processor can be configured to update one or more operating parameters when the patient is in a fixed sleep stage. For example, the processor can be configured to adjust one or more alert parameters when the patient is in a fixed sleep stage. In certain implementations, as described herein, the one or more alert parameters can include alert type, alert volume, alert duration, and patient response time to the alert.

[0070] In another example, a patient monitoring device can be configured to perform additional patient monitoring when the patient is in a specific sleep stage. For example, a patient monitoring device such as the above-described patient monitoring device can also include an additional physiological sensor that is externally coupled to the patient and configured to acquire physiological signals other than the ECG signal for analysis. In some implementations, the additional physiological sensor can include an RF sensor configured to acquire RF-based physiological signals of the patient. In some examples, the processor as described above can be configured to start monitoring physiological signals other than the ECG signal when the patient is in a fixed sleep stage and determine one or more non-ECG physiological parameters of the patient based on the analysis of the non-ECG physiological signals.

[0071] These examples and various other similar examples of the benefits and advantages of the techniques, processes, and methods provided herein are described in more detail below.

[0072] Patients at increased risk of sudden cardiac death, unexplained syncope, previous symptoms of heart failure, ejection fraction less than 45%, less than 35%, or other such thresholds that a doctor deems of concern, and other similar patients with deteriorating cardiac health can be prescribed such specialized cardiac monitoring and treatment devices. The sleep stage monitoring features and / or processes described herein with reference to the WCD can be applied in the HWD in substantially a similar manner.

[0073] The various monitoring processes described herein are implemented in the WCD or HWD device itself or in a data processing device such as a remote server system that communicates with or is otherwise associated with the WCD or HWD. For example, some or all of the steps of the processes described herein can be executed on a server, and one or more of the results of these steps can be implemented by the device.

[0074] In one example, the WCD as described herein can include that from ZOLL Medical Corporation (Chelmsford, Massachusetts) Wearable cardioverter defibrillator. As described in further detail below, such a device includes a garment configured to be worn around a patient's torso. The garment can be configured to house various components, such as ECG sensing electrodes, therapy electrodes, one or more accelerometers configured to measure a patient's motion data, one or more audio and / or vibration sensors configured to record a patient's vibration signals (such as cardiac vibration signals, etc.), and one or more radio frequency (RF) sensors configured to measure RF-based physiological signals. The components in the garment can be operably connected to a monitoring device disposed in a separate housing (e.g., that can be waterproof and / or dust or other physical particle proof), the monitoring device being configured to receive and process signals from the ECG sensing electrodes to determine the patient's cardiac condition and, if needed, provide treatment to the patient using the therapy electrodes.

[0075] The HWD can include two or more attached ECG sensing and / or therapy electrodes that are coupled via a cable to a monitoring device disposed in a housing similar to the housing described above for the WCD.

[0076] The monitoring device of the WCD described herein is configured to determine whether the patient is currently in a stationary sleep stage and, accordingly, adjust one or more operating parameters of the monitoring device.

[0077] Figure 1A and Figure 1B Various examples are shown of a patient 100 wearing one or more sensors, such as accelerometers, audio and / or vibration sensors, RF sensors, stretch or pressure sensors embedded in the garment, and other similar sensors as described herein. It should be noted that the accelerometer is described herein as an example of a motion sensor for illustrative purposes only. In certain implementations, additional motion sensors, such as gyroscopes, magnetic sensors, pressure-based motion sensors, and other similar motion sensors, can be used.

[0078] As Figure 1A shown, a mobile medical device such as a WCD (or, for inpatients, an HWD) can be prescribed for a patient. The WCD can include a controller 102 operably connected to one or more sensing electrodes and therapy electrodes. Additional details of an example of the controller 102 can be found in the following discussion of Figure 3

[0079] The WCD can also include one or more accelerometers or other motion sensors. As Figure 1AAs shown, the WCD may include three accelerometers 104a, 104b, and 104c (collectively, accelerometers 104) positioned at various locations on the body of patient 100. For example, accelerometer 104a may be positioned on the front of the chest of patient 100, accelerometer 104b may be positioned on the back of the patient, and accelerometer 104c may be integrated into controller 102. Each accelerometer 104 may be configured to measure movement associated with patient 100 and output an electrical signal representative of the direction and magnitude of the patient's movement.

[0080] It should be noted that the number and arrangement of accelerometers 104 shown in FIG. 1 are merely examples. In some implementations, the number and location of accelerometers 104 may vary. Additionally, when included in a device such as a WCD, one or more of accelerometers 104 may be integrated into components of the WCD. For example, as described above, accelerometer 104c may be integrated into controller 102 of the WCD. Similarly, one or more of accelerometers 104a and 104b may be integrated into one or more components of the WCD. For example, the front accelerometer 104a may be integrated into, for example, a therapy electrode that is operably connected to controller 102 and configured to deliver a therapy shock to patient 100. In some implementations, accelerometer 104a may be integrated into a sensing electrode that is configured to measure an electrical signal generated by patient 100 and representative of the patient's cardiac activity. Similarly, accelerometer 104b may be integrated into one or more components of the WCD (such as connection nodes, sensing electrodes, therapy electrodes, and other similar components of the WCD as described herein). Alternatively or additionally, one or more of accelerometers 104 may be distinct components of the WCD.

[0081] In HWD implementations, accelerometers may be integrated into one or more of the adhesive ECG sensing and / or therapy electrode patches. For example, a first accelerometer may be integrated into a first adhesive ECG sensing and / or therapy electrode patch, and a second accelerometer may be integrated into a second adhesive ECG sensing and / or therapy electrode patch. Additional accelerometers may be disposed within a controller (similar to controller 102 of the WCD) associated with the HWD.

[0082] In addition to the accelerometers associated with the WCD as described above with respect to Figure 1A a patient such as patient 100 may also wear additional sensors. As Figure 1BAs shown, patient 100 may wear a vibration sensor 106 configured to record the patient's bio-vibration signals. For example, vibration sensor 106 may be configured to detect vibrations of the patient associated with, for example, heart and lung activities. In some implementations, vibration sensor 106 may be configured to detect cardiac vibration values including any one or all of S1, S2, S3, and S4. Based on these cardiac vibration values, certain cardiac vibration metrics or combined metrics may be calculated, including any one or more of electromechanical activation time (EMAT), left ventricular systolic time (LVST), and percentage of left ventricular systolic time (%LVST). In some examples, vibration sensor 106 may include a vibration sensor configured to detect vibrations from the cardiac system of a subject and provide an output signal in response to the detected cardiac vibration values. Vibration sensor 106 may also include a multi-channel accelerometer, e.g., a three-channel accelerometer, configured to sense movement on each of three orthogonal axes such that patient movement / body position can be detected and associated with the detected cardiac vibration values. Vibration sensor 106 may send information describing the cardiac vibration values to, for example, a sensor interface for subsequent analysis as described below.

[0083] Additionally, patient 100 may wear an RF sensor 108. For example, the RF sensor may be configured to evaluate the liquid level and accumulation in the patient's body tissue using RF-based techniques. For example, RF sensor 108 may be configured to measure the liquid content in the lungs, typically for the diagnosis and follow-up of pulmonary edema or pulmonary congestion in heart failure patients. Similarly, the RF sensor may be configured to measure the thoracic liquid content of the patient. In some implementations, RF sensor 108 may include one or more than one antenna configured to direct radio frequency waves through the patient's tissue and measure an output radio frequency signal in response to the waves that have passed through the tissue. In some implementations, the output radio frequency signal includes a parameter representing the liquid level in the patient's tissue. RF sensor 108 may send information describing the tissue liquid level to the sensor interface for subsequent analysis as described below.

[0084] It should be noted that Figure 1A and 1B the placement and number of sensors shown are presented only as examples. In the actual implementation of the patient sleep stage determination technique as described herein, the number and location of sensors may vary based on the type of patient monitoring to be performed, the patient's typical sleep habits, the patient's typical sleep position and body orientation during sleep, and various other factors.

[0085] To appropriately acquire and output signals representing a patient's movement while sleeping, an accelerometer, such as the accelerometer described above, can be configured to output one or more output signals representing any detected movement or motion. For example, as Figure 2 shown, accelerometer 200 can be configured to measure movement on three axes: the x-axis, the y-axis, and the z-axis. Depending on the orientation of accelerometer 200 and the output configuration of the accelerometer, each axis can define movement of the accelerometer in a particular direction.

[0086] Additionally, as Figure 2 shown, accelerometer 200 can be configured to provide one or more outputs 202. In this example, output 202 can include an X output (i.e., a signal representing measured movement along the x-axis), a Y output (i.e., a signal representing measured movement along the y-axis), and a Z output (i.e., a signal representing measured movement along the z-axis).

[0087] In some implementations, an accelerometer, such as accelerometer 200, can be configured to output an electrical signal having one or more controlled characteristics, such as voltage, on each output 202. For example, accelerometer 200 can be configured to output a signal between 0 and 5 volts on each output 202. In some examples, the output voltage on each output 202 can be proportional to the measured motion on the corresponding axis. For example, if accelerometer 200 is configured to measure movement of acceleration as a measure of gravity, the accelerometer can be configured to measure a specific range of g-forces, such as -5g to +5g, etc. In such an example, the output voltage on each output 202 can be proportional to the measured g-force on each axis. For example, if no g-force is measured (i.e., accelerometer 200 is at rest), each output signal 202 can be measured at 2.5 volts. If movement with a positive g-force along an axis is measured, the voltage on the corresponding output 202 can increase. Conversely, if movement with a negative g-force along an axis is measured, the voltage on the corresponding output 202 can decrease. Based on these outputs 202, a processor, such as the processor described herein, can determine one or more motion parameters of the patient for use in determining whether the patient is in a stationary or non-stationary sleep stage. These details are provided in more detail below.

[0088] Table 1 below shows the sample voltage output levels for an accelerometer configured to measure g-forces between -5g and +5g and output a signal between 0 and 5 volts.

[0089] Measuring g-force Output voltage -5g 0 volts -4g 0.5 volts -3g 1.0 volts -2g 1.5 volts -1g 2.0 volts 0g 2.5 volts 1g 3.0 volts 2g 3.5 volts 3g 4.0 volts 4g 4.5 volts 5g 5.0 volts

[0090] Table 1

[0091] It should be noted that the sample g-force and voltage ranges as described above and shown in Table 1 are provided only by way of example for illustrative purposes. Depending on the design and capabilities of the accelerometer used, the measured g-force range and the corresponding output voltage can vary accordingly.

[0092] Figure 3 An example component-level diagram of a medical device controller 300 is shown, which is included in a wearable medical device such as a WCD or HWD as described herein, for example. The medical device controller 300 is Figure 1A and Figure 1B an example of the controller 102 as shown and described above. As Figure 3 shown, the medical device controller 300 can include a housing 301 configured to house: a therapy delivery circuit 302 configured to provide one or more therapy shocks to a patient via at least two therapy electrodes 320; a data storage unit 304; a network interface 306; a user interface 308; at least one rechargeable battery 310 (e.g., in a battery compartment configured for this purpose); a sensor interface 312 (e.g., to connect to both an ECG sensing electrode 322 and a non-ECG physiological sensor 323 such as a vibration sensor (e.g., vibration sensor 106), a lung fluid sensor (e.g., RF sensor 108), an infrared and near-infrared based pulse oximeter, a blood pressure sensor, etc.), a cardiac event detector 316; and at least one processor 318.

[0093] In some examples, a patient monitoring medical device can include a medical device controller 300 that includes components similar to those described above but does not include the therapy delivery circuit 302 and the therapy electrodes 320 (shown in dashed lines). That is, in certain implementations, the medical device can include only the ECG monitoring components and not provide therapy to the patient. In such an implementation, the construction of the patient monitoring medical device is similar to that of the medical device controller 300 in many respects but does not need to include the therapy delivery circuit 302 and the associated therapy electrodes 320.

[0094] As Figure 3As further shown, the controller 300 may also include an accelerometer interface 330 and a set of accelerometers 332. The accelerometer interface 330 may be operably coupled to each of the accelerometers 332 and is configured to receive one or more outputs from these accelerometers. The accelerometer interface 330 may also be configured to condition the output signals, for example, by converting analog accelerometer signals into digital signals (in the case of using analog accelerometers), filtering the output signals, and combining the output signals into a combined direction signal (e.g., combining the respective x-axis signals into a composite x-axis signal, combining the respective y-axis signals into a composite y-axis signal, and combining the respective z-axis signals into a composite z-axis signal). In some examples, the accelerometer interface 330 may be configured to filter the signals using a high-pass or band-pass filter to separate the acceleration of the patient due to movement from the components of the acceleration due to gravity.

[0095] Additionally, the accelerometer interface 330 may condition the output for further processing. For example, the accelerometer interface 330 may be configured to arrange the outputs of the individual accelerometers 332 as a vector representing the x-axis, y-axis, and z-axis acceleration components received from each accelerometer. The accelerometer interface 330 may be operably coupled to the processor 318 and is configured to transmit the output signals from the accelerometers 332 to the processor for further processing and analysis.

[0096] As described above, one or more of the accelerometers 332 may be integrated into one or more components of the medical device. For example, as Figure 3 shown, the accelerometer 332 may be integrated into the controller 300. In some examples, the accelerometer 332 may be integrated into one or more of the treatment electrodes 320, sensing electrodes 322, physiological sensors 323, and other components of the medical device. When the controller 300 is included in the HWD, the accelerometer may be integrated into an adhesive ECG sensing and / or treatment electrode patch.

[0097] As described above, when a patient is sleeping, the patient's cardiac activity and other similar physiological functions can change based on what sleep stage the patient is in. Additionally, if the patient is in a stationary sleep stage, additional monitoring and / or treatment functions can be enabled while the patient remains in the stationary sleep stage. In such an example, a medical device controller as described herein can be configured to monitor motion information from, for example, an accelerometer interface as described above, the motion information representing the movement of the patient. The medical device controller can analyze the motion information and derive one or more motion parameters. The medical device controller can also monitor ECG signals from one or more sensing electrodes and derive one or more ECG parameters from these ECG signals. Based on the motion parameters and the ECG parameters, the medical device controller can determine whether the patient is in a stationary sleep stage or a non-stationary sleep stage and adjust the monitoring and / or treatment of the patient accordingly.

[0098] For example, as Figure 4 shown, the arrhythmia detector 316 of the medical device controller 300 can include multiple monitoring and / or treatment modes for monitoring and / or treating a sleeping patient. As Figure 4 shown, the arrhythmia detector 316 can include a non-stationary sleep mode 402. If the processor 318 derives motion parameters and / or ECG parameters associated with a non-stationary sleep stage, the processor can switch to the non-stationary sleep mode 402 for monitoring and treating the patient. As Figure 4 further shown therein, the arrhythmia detector 316 can also include a stationary sleep mode 404. If the processor 318 derives motion parameters and / or ECG parameters associated with a stationary sleep stage, the processor can switch to the stationary sleep mode 404 for monitoring and treating the patient. More specific details for determining the patient's sleep stage and switching the monitoring and / or treatment mode are described in the discussion of Figure 5A and Figure 5B below.

[0099] Figure 5AIllustrated is a sample process 500 for determining which sleep stage a patient is in and adjusting the patient's monitoring and / or treatment accordingly. A processor, such as processor 318 of a medical device controller 300 as described above, can be configured to receive 502 and monitor patient information. For example, as described herein, patient information can include patient movement information, patient ECG information, and other similar patient information such as patient respiration information. Based on this information, the processor can determine 504 whether the patient is asleep. For example, the processor can analyze the patient movement information to determine if the patient's movement has decreased. The processor can further analyze the patient's ECG information to determine if the patient's heart rate has decreased, and analyze the patient's respiration information to determine if the patient's respiration has slowed. In some implementations, the processor can also consider additional data such as the current time and the patient's historical sleep pattern information to determine if the patient is asleep.

[0100] Based on this analysis, the processor can determine 504 whether the patient is asleep. If the processor determines 504 that the patient is not asleep, the processor can continue to receive 502 and monitor the patient's information. When the processor determines 504 that the patient is asleep, the processor can further determine 506 whether the patient is in a fixed sleep stage. Additional details regarding determining whether the patient is asleep are provided in the following discussion of Figure 5B Similarly, additional details regarding determining whether the patient is in a fixed sleep stage are provided in the following discussion of Figure 5C .

[0101] As Figure 5A further shown, if the processor does not determine that the patient is in a fixed sleep stage, the processor can monitor 508 the patient using a non-fixed sleep mode as described herein. Conversely, if the processor determines 506 that the patient is in a fixed sleep stage, the processor can determine 510 one or more updated operating parameters of the wearable medical device. For example, the patient's doctor can provide updated operating parameters of the patient's wearable medical device based on historical patient information and / or changes in the patient's treatment plan. The doctor can input the updated operating parameters into, for example, the doctor's online portal, and the updated operating parameters can be stored on a remote server or other similar remote computing device. In some implementations, the updated operating parameters can include updated operating parameters for monitoring the patient when the patient is in a fixed sleep stage. The processor can be connected to the remote server and can access the updated operating parameters. Then, the processor can determine 510 the updated operating parameters for monitoring the patient when the patient is in a fixed sleep stage. Based on the updated operating parameters, the processor can adjust 512 one or more operating parameters of the wearable medical device and monitor 514 the patient in a fixed sleep mode as described herein.

[0102] Note that, in some implementations, the processor may determine that the wearable medical device 510 has operated in accordance with a most recently updated set of operating parameters. In such an example, the processor may adjust 512 the operating parameters for monitoring a patient in a fixed sleep stage based on the existing operating parameters. Additionally, as described below, the processor may also be configured to automatically adjust one or more operating parameters for monitoring the patient based on historical patient activity and monitored and / or observed morbidity associated with the patient in the fixed sleep stage.

[0103] In some implementations, adjusting one or more operating parameters of the wearable medical device 512 may include adjusting treatment parameters, adjusting alert parameters, adjusting cardiac arrhythmia detection parameters, adjusting arrhythmia detection confidence levels, adjusting noise thresholds, and other similar operating parameters.

[0104] In some examples, adjusting treatment parameters may include adjusting one or more of pacing pulse rate, high energy pacing pulse level, low energy pacing pulse level, defibrillation shock level, and defibrillation shock timing information. In some examples, adjusting alert parameters may include adjusting one or more of alert type (e.g., tactile, audio, visual, combinations thereof), alert volume, alert duration, and patient response time information. For example, when the patient is in a fixed sleep stage, the alert type may include vibration followed by a high volume sound alert. In some examples, when the patient is in a fixed sleep stage, the alert may include a mild electric shock to the patient via electrical pacing or transcutaneous electrical nerve stimulation (TENS) that has no effect on the patient's cardiac function but is uncomfortable enough to wake the patient to a waking state. The patient response time may also be adjusted based on the type of arrhythmia detected and the patient sleep stage. For example, if the patient is experiencing ventricular tachycardia (VT), the patient may typically have 60 seconds to respond (programmable, e.g., up to 180 seconds). However, if the patient is in a fixed sleep stage, the response time may automatically increase to 90 seconds. Similarly, if the patient is experiencing ventricular fibrillation (VF), the patient may typically have 20 seconds to respond (programmable, e.g., up to 55 seconds). If the patient is in a fixed sleep stage, the response time may automatically increase to 30 seconds.

[0105] In some implementations, adjusting cardiac arrhythmia detection parameters includes changing the thresholds at which the device delivers therapy to the patient. For example, adjusting cardiac arrhythmia detection parameters can include changing one or more of the VT onset rate, VF onset rate, bradycardia onset rate, tachycardia onset rate, and asystole onset rate. For example, for a particular patient, when the patient is in a fixed sleep stage as described herein, the bradycardia onset rate can be adjusted from 20 bpm to 25 bpm. Since the patient is less likely to wake up from the fixed sleep stage when being treated for bradycardia and thus less likely to discontinue the treatment, the onset rate can be increased to initiate pacing pulses more quickly when the patient experiences bradycardia. However, this is described by way of example only. In some examples, the bradycardia onset rate can be adjusted between approximately 20 bpm and approximately 45 bpm. Similarly, the default VT onset rate can be approximately 150 bpm. When the patient is in a fixed sleep stage, the VT onset rate can be reduced to approximately 100 bpm. In some examples, the VT onset rate can be set between a lower limit of approximately 100 bpm and an upper limit of the VF onset rate. In some examples, the default VF onset rate can be set to approximately 200 bpm. When the patient is in a fixed sleep stage, the VF onset rate can be reduced to approximately 150 bpm. In some examples, the adjusted VF onset rate can be adjusted between approximately 120 bpm and 200 bpm. In some examples, the asystole onset rate can be five average heart beat lengths without a heartbeat (e.g., if no heartbeat is detected within five heart beat lengths, it is determined that the patient is experiencing asystole). The asystole onset rate can have a range of approximately three heart beat lengths to approximately ten heart beat lengths.

[0106] The various cardiac arrhythmia detection parameters as described herein can be set by an authorized caregiver such as a prescriber or the patient's healthcare provider. In some examples, the cardiac arrhythmia detection parameters can be adjusted dynamically and automatically by a processor. For example, if there are repeated false alarms (e.g., three false alarms within a seven-day period), the processor can automatically adjust one or more of the cardiac arrhythmia detection parameters. For example, if a wearable medical device is detecting that the patient is experiencing VF using a modified VF onset rate of 120 bpm and has three false alarms within a seven-day period, the processor can be configured to increase the VF onset rate to, for example, 135 bpm. If false alarms are reduced or eliminated using the updated VF onset rate, the processor can maintain the updated VF onset rate. However, if the device continues to have false alarms, the processor can continue to update the VF onset rate.

[0107] In some examples, adjusting the confidence level can include adjusting the confidence level associated with the identification of an arrhythmia. In certain implementations, the confidence level is used to verify a treatment to be delivered to a patient prior to delivery. To calculate the confidence level, a predetermined mathematical relationship among various parameters is established and presented as a percentage. For example, the various parameters can include whether a patient response to an alert has been received, noise values on one or more sensing electrode channels, ECG morphology matching information, FFT analysis of an arrhythmia, FFT analysis of a heart rate, sensing electrode channel stability information, axis information, and other similar parameters. The parameters can be weighted, and the confidence level can be calculated. Generally, the default confidence level should reach approximately 80% within a verification period (e.g., 10 to 30 seconds) before initiating a treatment. However, when the patient is in a fixed sleep stage as described herein, the algorithm used to calculate the confidence level can be adjusted dynamically. For example, if the patient is in a fixed sleep stage, the verification period timing can be shortened. Similarly, one or more input parameters used in the confidence level algorithm can be discarded and / or one or more weights can be adjusted in the confidence level algorithm. For example, the noise value on a sensing electrode channel can be discarded from the confidence level algorithm, or conversely, the weight associated with the noise value can be decreased such that when the patient is in a fixed sleep stage, the contribution of the noise value to the overall confidence level is less. Additionally, if the patient is in a fixed sleep stage, the confidence level threshold required to trigger a treatment can be decreased. For example, when the patient is in a fixed sleep stage, the confidence level threshold can be decreased to 75%.

[0108] Adjusting the noise threshold can include adjusting the acceptable noise level on each sensing electrode before ignoring the ECG signals received from the sensing electrodes. For example, each sensing electrode pair channel can have an associated noise flag. When the patient is in a fixed sleep stage, the time used for noise verification on a channel can be increased. Similarly, when the patient is in a fixed sleep stage, the threshold used to identify a noisy channel can be increased. For example, when the patient is in a fixed sleep stage, the threshold used to identify a noisy channel can be increased by 10%, 15%, 20%, or 25%.

[0109] As Figure 5A shown and as described above, the processor can determine 504 whether the patient is asleep or still active. Based on this determination, the processor can switch from an activity monitoring mode to a sleep monitoring mode, which includes, for example, a fixed sleep monitoring and treatment mode and a non-fixed monitoring and treatment mode as described herein. Figure 5B A more detailed process for determining 504 whether the patient is asleep is shown.

[0110] As Figure 5BAs shown, the processor can monitor the patient 520. Patient monitoring can include, for example, monitoring patient movement, patient ECG information (such as heart rate, etc.), patient breathing patterns, and other similar information of the patient. Based on the patient information, the processor can determine 522 whether the patient is currently moving or performing a specific action. For example, the processor can determine 522 whether the patient is walking or moving in a way that would indicate that the patient is not lying down or in a relaxed position to sleep. If the processor determines 522 that the patient is moving, the processor can continue to monitor the patient 520. If the processor determines 522 that the patient is not moving, the processor can determine 524 whether one or more other parameters for indicating that the patient is asleep are met.

[0111] For example, the processor can determine 524 whether there are any changes in the patient's heart rate and / or respiratory rate. For example, a certain percentage decrease in both the heart rate and the respiratory rate can indicate that the patient is asleep. In some implementations, a 5% to 10% decrease in heart rate can indicate that the patient may be asleep. Similarly, a decrease in respiratory rate of about 10 to 20% can indicate that the patient may be asleep. The combination of changes in heart rate and respiratory rate along with limited movement or no movement can provide an indication that the patient may be asleep.

[0112] As Figure 5B shown, if the processor determines 524 that the other parameters are not met (for example, there are no changes in the heart rate and / or respiratory rate), the processor can continue to monitor the patient 520. If the processor determines 524 that the other parameters are met, the processor can start a timer. For example, the processor can start a 30 - minute timer. In some examples, the timer can be in the range of 15 minutes to 45 minutes. During the timer, the processor can determine 526 whether the timer has expired. If the processor determines 526 that the time has not expired, the processor can continue to monitor the patient 520 as described above, determine 522 whether the patient is moving, and determine 524 whether the other monitoring parameters are met. If there are any changes in determining 526 whether the timer has expired, determining 522 whether the patient is moving, or determining 524 whether the other parameters are met, the processor can reset or otherwise stop the timer. Otherwise, once the processor determines 526 that the timer has expired, the processor can enter the sleep mode 528 and monitor the patient accordingly as described herein.

[0113] It should be noted that the processor can also monitor various other information, such as the time of day and patient input on a user interface associated with, for example, a wearable medical device. In some implementations, if the patient provides an indication that they are going to sleep, the processor can change one or more parameters associated with determining whether the patient is asleep as described above. For example, if the patient provides an indication that they are going to sleep, the processor can reduce the time from a default value (e.g., 30 minutes) to a shorter value (e.g., 15 or 20 minutes). Similarly, if the processor determines that the time of day is later than a particular time (e.g., later than 10 PM), the processor can similarly change one or more parameters associated with determining whether the patient is asleep.

[0114] Return reference Figure 5A And as described above, once the processor determines that the patient is asleep (e.g., as Figure 5B shown and as described above), the processor can determine 506 whether the patient is in a stationary sleep stage. Figure 5C Illustrates a more detailed process for determining 506 whether the patient is in a stationary sleep stage or a non-stationary sleep stage. For example, as Figure 5C shown, the processor can analyze 530 motion information and derived motion parameters. In some examples, the motion parameters can include a rotational motion parameter that quantifies the amount of rotational movement of the patient measured by at least one accelerometer. For example, the rotational movement of the patient can be measured and represented on a scale of 0 to 360 degrees of rotation of the patient's body during sleep. In other examples, the motion parameters can include patient breathing information, patient limb movement information, and patient body position information. However, it should be noted that determining the motion parameters solely from the motion information is provided by way of example only. In some examples, one or more motion parameters (such as patient breathing information, etc.) can be derived from one or more impedance-based measurements received from, for example, one or more sensing electrodes as described herein.

[0115] In one example, the detected motion parameter can be respiratory-related motion sensed using electrical impedance tomography (EIT). EIT is a non-invasive imaging technique for non-destructive examination for medical applications as well as for technical devices or processes. EIT was first introduced for medical imaging by Barber and Brown in the mid-1980s. For EIT imaging, electrodes are placed equidistantly around a cylindrical object such as a human chest. Between any two adjacent electrode pairs, a constant current is injected into the body being examined, while all other electrodes measure the voltages generated at the locations of these electrodes in pairs. By repeating the current injection and voltage measurement between all electrode pairs, a data set is established for reconstructing the internal structure of the body through a mathematical operation called filtered backprojection. By reconstructing over a period of time, the respiratory rate of the patient can be determined and this respiratory rate can be used to estimate their sleep stage. For example, the initial respiratory rate when a person falls asleep can be used as a baseline. If the respiratory rate drops by more than a predetermined threshold (e.g., 80% of the baseline), the sleep stage of the patient can be considered fixed. For example, when a patient is asleep, their respiration can be measured at 20 breaths per minute. Once the measured respiratory rate of the patient reaches 16 breaths per minute (i.e., 80% of the baseline of 20 breaths per minute), the patient can be considered to be in a fixed sleep stage.

[0116] In some examples, the sensor can be configured to detect movement parameters when the patient rolls over and "switches sides" during sleep. Switching sides during sleep is typically a very intermittent movement with a relatively long period of no movement, but is highly indicative of a non-fixed sleep stage. In some examples, detecting movement characterized as switching sides can be achieved by force sensors mounted circumferentially around the patient's chest. The force sensors can include load cells coupled to signal processing and filtering circuitry and an analog-to-digital converter device. In some examples, two force sensors can be mounted to the garment of the wearable medical device as described herein, with one force sensor near the sternum and the other force sensor near the spine. The force sensors can be configured to detect when the patient is prone and supine based on which sensor has a positive force output. A patient lying on their side can be detected when neither of the two force sensors has any weight on them and subsequently no force output is produced. In some implementations, four sensors can be employed, with two sensors placed on each side of the patient in addition to the sensors placed on the front and back of the patient. In some examples, when the force measured by the force sensor exceeds a threshold (e.g., 10% of the patient's body weight), it is determined that the patient is lying on that side. In some versions, to account for the fact that when the patient is lying on their side, their arm may be across the force sensor facing upwards, if more than one force sensor is measuring a non-zero force, the sensor with the largest force measurement is determined to be the one facing downwards towards the mattress surface. When the determination of the force sensor facing downwards changes, it is determined that the patient has switched sides. In one example, when a side switch is detected, the patient will be considered to be in a non-fixed state for a predetermined amount of time (e.g., 10 minutes) after that detection, even though no other movement is detected during that time period. If the patient does not make any additional movement (e.g., another side switch) after the predetermined time period, the patient can be considered to be in a fixed state.

[0117] It should be noted that the 10-minute predetermined time period described above for measuring patient movement is provided only as an example. In some examples, the predetermined time can include 5 minutes, 15 minutes, 20 minutes, 25 minutes, and 30 minutes.

[0118] In some implementations, the motion information can include output information from one or more motion sensors such as an accelerometer (e.g., accelerometers 200 and 322 as described above). The output information can represent the movement information of the patient on each of the three axes (i.e., the x-axis, the y-axis, and the z-axis). Based on this output information, the processor can determine whether the patient is moving and the distance and direction of any movement. The processor can monitor this information over a period of time to determine whether the patient is stationary. For example, the processor can monitor the motion information and the derived motion parameters over time periods of two minutes, five minutes, seven minutes, ten minutes, thirty minutes, forty-five minutes, one hour, and other similar time periods. Based on the analysis 530 of the motion information, the processor can determine whether the patient is stationary, or if the patient is not stationary, determine how much the patient has moved during sleep.

[0119] As Figure 5C As further shown, the processor can be configured to analyze 532 the patient's ECG information and any derived ECG parameters as described herein. In some implementations, the ECG parameters can include one or more of heart rate, heart rate variability, premature ventricular contraction (PVC) burden or count, atrial fibrillation burden, intermittency, heart rate turbulence, QRS height, QRS width, changes in the size or shape of the morphology of the ECG signal, cosine R-T, artificial pacing, QT interval, QT variability, T wave width, T wave alternans, T wave variability, and ST segment changes.

[0120] For example, the processor can monitor the patient's heart rate compared to a baseline resting heart rate threshold. Based on the deviation of the monitored heart rate compared to the baseline resting heart rate over a period of time, the processor can determine the percentage deviation over that period of time. For example, the processor can compare the monitored heart rate to a baseline resting heart rate threshold over a period of time to determine deviation thresholds of less than 1% deviation from the baseline resting heart rate, less than 2% deviation from that resting heart rate, less than 5% deviation from that baseline resting heart rate, and other similar percentage deviations. In some examples, the period of time can include two minutes, five minutes, seven minutes, ten minutes, thirty minutes, forty-five minutes, one hour, and other similar time periods.

[0121] In a specific example, the baseline resting heart rate of a patient when in a fixed sleep stage can be about 55 bpm. The processor can monitor the patient's current heart rate over a period of time and compare the current heart rate to the baseline resting heart rate. For example, the patient's current heart rate in the past two minutes can be 57 bpm. In such an example, when compared to a baseline resting heart rate of 55 bpm, the processor can determine that the patient currently has a deviation from the baseline resting heart rate of less than 2%.

[0122] It should be noted that in Figure 5CThe analysis of motion and ECG information is shown only by way of example. In some implementations, additional information such as respiratory rate information can be used to determine whether the patient is in immobilized sleep data or non-immobilized sleep data.

[0123] As Figure 5C As further shown, based on the results of the analysis of motion and ECG information, the processor can determine what specific sleep stage the patient is in. For example, the processor can determine whether the patient is in one of the American Academy of Sleep Medicine (AASM) sleep stages based on the analyzed motion and ECG information. For example, sample sleep stage information and associated motion and ECG information are shown only by way of example in Table 2.

[0124]

[0125]

[0126] Table 2

[0127] As shown in Table 2, certain sleep stages such as N3, N4, and REM can be considered immobilized sleep stages. These stages represent deep sleep (N3) with the body immobilized, slow-wave sleep (N4), or dreaming sleep (NREM) stages. Also as shown in Table 2, certain sleep stages such as N1 and N2, as well as wakefulness or arousal, can be considered non-immobilized sleep stages. These stages represent relaxed wakefulness (N1) and light sleep (N2) stages.

[0128] Each sleep stage is associated with specific brain waves and neuronal activity. Most patients cycle through all stages of non-REM and REM sleep several times during a typical night. In the example, the patient experiences increasingly longer and deeper REM periods towards morning.

[0129] In a hypothetical scenario, the sleep stages can occur as follows. Stage 1 non-REM sleep can be characterized as the transition from wakefulness to sleep. During this short period of relatively light sleep (e.g., lasting about 5 to 15 minutes), the patient's heart rate, breathing, and eye movements are slow, and the muscles are relaxed, with occasional twitches. During this period, the patient's brain waves begin to slow down from the daytime wakefulness pattern.

[0130] During stage 2 non-REM sleep, before the patient enters deep sleep, the patient experiences a period of light sleep. The patient's heart rate and breathing are slow, and the muscles are even more relaxed than in stage 1 non-REM sleep. In stage 2 non-REM sleep, the patient's body temperature drops and eye movements stop. Brain wave activity slows down, but may be marked by brief bursts of electrical activity. The patient spends more of the patient's repeating sleep cycles in stage 2 sleep than in other sleep stages.

[0131] During stage 3 non-REM sleep, the patient enters a deep sleep period necessary to feel refreshed in the morning. This stage occurs during a longer time period in the first half of the night. During this sleep stage, the patient's heart rate and breathing slow down to their lowest levels. In stage 3 non-REM sleep, the patient's muscles relax, and it may be difficult to wake the patient with audio or vibration stimuli during this stage compared to other sleep stages. Additionally, this stage is characterized by even slower brain waves compared to other sleep stages.

[0132] Continuing with the hypothetical scenario, REM sleep typically occurs about 90 minutes after falling asleep (e.g., 90 minutes after entering stage 1 non-REM sleep). The patient's eyes move rapidly from side to side behind closed eyelids. Mixed-frequency brain wave activity becomes closer to the brain wave activity seen during wakefulness. The patient's breathing becomes faster and irregular, and the patient's heart rate and blood pressure increase to near waking levels. Most patients' dreaming occurs during this time period of REM sleep. The patient's arm and leg muscles become temporarily immobilized, which tends to prevent the patient from acting out their dreams.

[0133] Two internal biological mechanisms, the circadian rhythm and homeostasis, work together to regulate when the patient is awake and asleep. The circadian rhythm guides multiple functions from the daily fluctuations of wakefulness to body temperature, metabolism, and hormone release. This mechanism controls the timing of sleep and makes the patient feel sleepy at night and tend to wake up in the morning. For the implementation of this article, it is assumed that the patient's body generally follows a biological clock based on about a 24-hour day and thus controls most of the circadian rhythm. However, it should be understood that the circadian rhythm can be synchronized with environmental cues (e.g., light, temperature) regarding the actual time of day.

[0134] Sleep-wake homeostasis tracks the patient's need for sleep. The homeostatic sleep drive reminds the patient's body to sleep after a certain time and regulates the intensity of sleep. The sleep drive becomes stronger every hour the patient is awake and causes the patient to sleep longer and deeper after a period of sleep deprivation. Factors that affect the patient's sleep-wake needs include medical conditions, medications, stress, sleep environment, and what the patient has to eat and drink.

[0135] Refer again to Figure 5C, as described above, the processor may also be configured to determine the current sleep stage of the patient 534. Based on the current sleep stage, the processor may determine 536 whether the current sleep stage is within a threshold considered to be fixed sleep. For example, as shown in Table 2, the sleep stages N3, N4, and REM are each considered to be fixed sleep stages. Thus, if the processor determines 534 that the patient is in one of the sleep stages N3, N4, and REM, the processor may determine 536 that the patient is within the threshold of fixed sleep and identify 538 the patient as being in a fixed sleep stage. Conversely, if the processor determines 534 that the patient is in one of the sleep stages N1 and N2, the processor may determine 536 that the patient is not within the threshold of fixed sleep and identify 540 the patient as being in a non-fixed sleep stage.

[0136] During sleep, the patient may transition between fixed sleep stages and non-fixed sleep stages. During such a transition, the processor of the medical device controller may be configured to determine that the patient has transitioned between stages and, if needed, adjust the monitoring and treatment modes as described herein accordingly. For example, Figure 6 illustrates a sample process 600 for determining whether a patient has transitioned between sleep stages. As Figure 6 shown, the processor may monitor 602 the patient who is sleeping. For example, the processor may monitor 602 the patient's movement information, the patient's ECG information, and the patient's information such as respiration information. Based on the analysis of the patient's information, the processor may determine 604 whether the patient has transitioned from one sleep stage to another. For example, the processor may determine 604 whether the patient has transitioned from the N2 sleep stage to the N3 sleep stage. In such an example, the processor may update 606 the operating parameters of the medical device and monitor 608 the patient using the updated sleep stage mode. In this example, the processor may monitor 608 the patient using the fixed sleep monitoring and treatment mode as described herein. However, the transition from a non-fixed sleep stage (e.g., N2) to a fixed sleep stage (e.g., N3) as described above is only an example. In some examples, the patient may transition from a fixed sleep stage (e.g., N4) to a non-fixed sleep stage (e.g., N1). In such an example, the processor may update 606 the operating parameters such that the processor may monitor 608 the patient using the non-fixed monitoring and treatment mode as described herein.

[0137] As Figure 6 further shown, in some examples, the processor may determine 604 that the patient has not transitioned between sleep stages. In such an example, the processor may continue to monitor 602 for changes in the patient's monitoring information for the patient who is sleeping.

[0138] In some implementations, it can be advantageous to perform additional monitoring when the patient is in a stationary sleep stage. For example, when using RF-based monitoring, the quality of any measured information can be improved if the patient remains stationary while information is being collected. Thus, it can be advantageous to perform RF-based monitoring when the patient is in a stationary sleep stage.

[0139] Figure 7 Illustrates sample process 700 for performing additional monitoring when the patient is in a stationary sleep stage. As Figure 7 shown, the processor can monitor 702 the patient and confirm that the patient is in a stationary sleep stage. Additionally, the processor can determine 704 that the patient is to be subjected to additional monitoring. For example, the medical device can be configured to perform an additional test once a week. If the processor determines 704 that the patient does not require any additional tests, the processor can continue to monitor 702 the patient as described herein. However, if the processor determines 704 that the patient will have additional monitoring, the processor can monitor one or more additional non-ECG signals as described herein.

[0140] For example, as Figure 7 shown, the processor can receive 706 an RF-based physiological signal from an RF sensor as described above. The processor can analyze the RF-based physiological signal to determine additional patient information such as cardiac wall movement information and thoracic fluid level information. Additionally or alternatively, the processor can also be configured to receive 710 vibration information from a vibration sensor as described above. The processor can analyze 712 the vibration information to determine various information such as the patient's cardiac vibration information. Based on the cardiac vibration information, the processor can determine one or more electromechanical parameters of the patient's heart.

[0141] As described above, to determine whether the patient is in a stationary or non-stationary sleep stage, the processor can compare the current patient information with the baseline information. Figure 8 Illustrates sample process 800 for collecting patient baseline information at regular intervals and re-establishing the patient's baseline information. For example, the patient baseline information can be re-established weekly, bi-weekly, tri-weekly, monthly, and at other similar intervals.

[0142] As Figure 8As shown, the processor can establish 802 initial patient baseline information. For example, the processor can collect and analyze patient information within a time period of two to three days after the initial provision and wearing of the wearable medical device as described above. Based on the information collected, the processor can determine various information such as active heart rate and movement information, sleep heart rate and movement information, breathing information, and other similar patient information. Based on the determined information, the processor can establish 802 the initial patient baseline information of the patient. Based on the initial patient baseline information, the processor can determine one or more sleep stage thresholds such as the sleep stage thresholds shown in Table 2 above to determine and identify the current sleep stage of the sleeping patient.

[0143] Once the baseline information is established, the processor can monitor 804 patient sleep information such as heart rate information, movement information, and breathing information collected while the patient is asleep. The processor can analyze 806 the monitored information to determine any trends in the sleep information. For example, as the patient's heart health improves, the patient's baseline resting heart rate can decrease. Thus, over time, the processor can continue to analyze 806 the trends in the sleep information. After a period of time (e.g., 1 week) or if the change in the sleep information exceeds a specific threshold (e.g., an information deviates from the baseline by more than 10% a certain number of times within the monitoring period), the processor can determine 808 whether to change the patient baseline information. If the processor does not determine 808 to make any changes to the baseline information, the processor can continue to monitor 804 the patient using the existing baseline information. However, if the processor determines 808 to change the patient baseline information, the processor can re-establish 810 one or more baseline information based on the updated monitored patient information. As Figure 8 further shown, the processor can continue to monitor 804 patient sleep information using the updated baseline information.

[0144] As described above, additional monitoring of the patient can be performed when the patient is in a fixed sleep stage. As Figure 9A and Figure 9B shown, additional testing and monitoring of the patient can be performed when the patient is in a certain sleep stage. For example, as Figure 9A shown, when the patient is in a fixed sleep stage, the patient capture of the applied pacing pulse can be measured. In this case, as a result of the patient being in a fixed sleep stage, the likelihood of the patient having a poor response to the test or turning off the device in the test is reduced.

[0145] As Figure 9AAs shown, the process 900 includes a processor monitoring 902 the sleep stage information of a patient. Based on the monitored sleep stage information, the processor can determine 904 whether the patient is in a fixed sleep stage. If the processor determines 904 that the patient is not in a fixed sleep stage, the processor can continue to monitor 902 the sleep stage information of the patient. However, if the processor determines 904 that the patient is in a fixed sleep stage, the processor can determine 906 whether any additional test criteria for a pacing capture test are met. For example, if the patient's heart rate is below a certain threshold (e.g., more than 10% lower than the patient's baseline resting heart rate), the processor can determine 906 that the test should not be performed and continue to monitor 902 the sleep stage information of the patient. Conversely, if the processor determines 906 that any additional criteria are met, the processor can initiate a test procedure and apply 908 one or more pacing pulses to the patient via one or more therapy electrodes as described herein. After applying one or more pacing pulses, the processor can monitor the patient's ECG or information from another sensor (such as pulse oximeter information, etc.) to measure 910 the pacing capture information of the patient's heart in response to the applied one or more pacing pulses. In one example, the protocol for the pacing capture test is as follows: 1) Set the pacing amplitude to 50 mA; 2) Deliver two pacing pulses; 3) Check the pacing capture information of one or two pacing pulses via the ECG or pulse oximeter; 4) If capture occurs, set the pacing capture level to the pacing amplitude plus 10 mA; 5) If no capture occurs, increase the pacing amplitude by 10 mA and repeat 1 to 4.

[0146] In addition to measuring pacing capture, when the patient is in a specific sleep stage, the patient's response to premature ventricular contractions (PVCs) can be measured. A PVC is an extra heartbeat that originates from one of the ventricles and disrupts the regular rhythm of the heart. By measuring the time it takes for the heart to return to its normal rhythm, the heart rate turbulence value of the heart can be determined and subsequently analyzed to determine additional information related to the patient's heart health.

[0147] As Figure 9BAs shown, the processor 920 may include collecting information related to heart rate turbulence and analyzing heart rate turbulence. The processor 920 includes a processor that monitors 922 patient information. During the monitoring, the processor may determine 924 whether the patient has experienced a PVC. If the processor does not determine that the patient has experienced a PVC, the processor may continue to monitor the patient. However, if the processor determines 924 that the patient has experienced a PVC, the processor may measure 926 the patient's cardiac response to the PVC by monitoring changes in the patient's ECG signal. Based on the cardiac response, the processor may determine 928 the heart rate turbulence value of the patient based on the changes in the ECG signal and store the heart rate turbulence value. Then, the processor or another similar processing device may analyze the heart rate turbulence value to determine any changes in the patient's cardiac health. In some implementations, if the patient is in a fixed sleep stage, the processor may induce 932 a PVC in the patient to determine the heart rate turbulence value by applying pacing pulses as described above.

[0148] Additionally, in some implementations, information related to the patient's sleep stage and position may be used to determine an optimized sensing electrode pairing. For example, if the patient lies in a position where a particular sensing electrode is likely to be firmly pressed against the patient's body, that particular sensing electrode may provide a clear and highly reliable signal. For example, if the patient is lying on their right side, the sensing electrode near the patient's right hip may be pressed against the patient's body and is likely to provide a clear signal. In contrast, the sensing electrode near the patient's left hip may be held in place only by the elasticity of the clothing and does not contact the patient's skin as closely as the right hip electrode, and thus does not provide a clear or reliable signal.

[0149] In some implementations, one or more force sensors adjacent to or integral with the ECG sensing electrodes may be configured to sense when a sufficient amount of force is applied to the ECG sensing electrodes for proper ECG monitoring. In some examples, the minimum applied force threshold is 0.2 lbs / in 2 . In other examples, the minimum applied force threshold may be set to 0.5 lbs / in 2 , 1.0 lbs / in 2 , 2.0 lbs / in 2 , 2.5 lbs / in 2 and 5.0 lbs / in 2This is particularly important for sensing electrodes that are considered capacitive high-impedance electrodes. High-impedance sensing electrodes can be vulnerable to noise caused by friction with the patient's skin (since they are dry electrodes), as well as friction between the back surface of the sensing electrode and the patient's clothing or bedding material during sleep. One of the main causes of this type of noise is due to the triboelectric effect. The triboelectric effect (also known as tribocharging) is a type of contact electrification in which certain materials become charged after they are separated from different materials they come into contact with. Due to the high impedance of the ECG sensing electrodes, charge transfer caused by the triboelectric effect can lead to noise voltages. In some examples, a motion sensor capable of measuring motion parameters is a triboelectric motion sensor that detects lateral motion along a surface. Similar to the force sensors described above, the triboelectric motion sensor can be adjacent to or integral with the ECG sensing electrode to most closely approximate the triboelectric conditions on the ECG electrode. In some implementations, there can be two triboelectric sensors such that one sensor faces the patient while the other sensor faces away from the patient and towards the bedding material. The triboelectric motion sensor can be implemented as a microelectromechanical system (MEMS) microphone, such as a bone conduction sensor for voice pickup such as the one manufactured by Sonion (Denmark). Alternatively, the triboelectric motion sensor can be implemented as a standard electret microphone (e.g., 50GC31 manufactured by Sonion) or a MEMS microphone (such as P11AC03 manufactured by Sonion). With the triboelectric motion sensor, the triboelectric-induced noise can be estimated by measuring the acoustic or vibrational energy caused by friction. In one example, the processor measures the RMS output of the triboelectric motion sensor and determines that the sleep stage is non-stationary if the RMS energy exceeds a predetermined threshold representing potential movement of the patient during sleep. For example, when the RMS energy is a non-zero value, the output of the triboelectric motion sensor can indicate that the patient is in a non-stationary sleep stage. Conversely, if the RMS energy is zero (or within a certain range of zero, such as plus or minus 0.05 J of energy, etc.), it can be determined that the patient is in a stationary sleep stage.

[0150] As Figure 10As shown, process 1000 may include updating the sensing electrode pairs for a patient who is sleeping. As shown in the figure, process 1000 may include processor monitoring 1002 the sleep stage of the patient. During the monitoring, the processor may also determine 1004 the body position of the patient based on the accelerometer data received in the motion signal as described above. Additionally, the processor may further analyze 1006 the signal quality from each sensing electrode. Based on the position information and signal quality analysis, the processor may determine 1008 whether there are any possible improved sensing electrode pairings. If the processor determines 1008 that there are no improved sensing electrode pairs (e.g., sensing electrode pairs that would result in lower overall noise and higher overall signal quality), the processor may continue to monitor 1002 the patient using the existing sensing electrode pairs. Conversely, if the processor determines 1008 that there is an available improved sensing electrode pairing, the processor may update 1010 the sensing electrode pairs and use the updated sensing electrode pairs to monitor 1002 the patient.

[0151] The teachings of the present invention can generally be 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, non-fixed medical devices as described herein that are capable of and designed to move with the patient as the patient goes about his or her daily business. Example mobile medical devices can be wearable medical devices such as a WCD, wearable cardiac monitoring devices, in-hospital devices such as in-hospital wearable defibrillators (HWDs), short-term wearable cardiac monitoring and / or treatment devices, mobile cardiac event monitoring devices, and other similar wearable medical devices, etc.

[0152] Wearable medical devices can be capable of being continuously used by a patient. In some implementations, the continuous use may be essentially continuous or nearly continuous in nature. That is, except for sporadic periods when use is temporarily stopped (e.g., when the patient takes a bath, when the patient re-assembles new and / or different clothing, when the battery is charged and / or replaced, when the clothing is washed, etc.), the wearable medical device can be continuously used. However, such essentially continuous or nearly continuous use as described herein can still be considered continuous use. For example, a wearable medical device can be configured to be worn by a patient up to 24 hours a day. In some implementations, the patient may remove the wearable medical device during a short portion of the day (e.g., during a half-hour bath).

[0153] In addition, the wearable medical device can be configured as a medical device for long-term or extended use. Such a device can be configured to be used by a patient for an extended period of 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 continuous until a physician or other HCP provides a specific instruction to the patient to stop using the wearable medical device.

[0154] Regardless of the length of the extended period of wear, the use of the wearable medical device can include continuous or nearly continuous wear by the patient as described above. For example, continuous use can include wearing or attaching the wearable medical device to the patient continuously, such as by one or more electrodes as described herein, during a monitoring period and during periods when the device may not be actively monitoring the patient but is still otherwise worn by the patient or attached to the patient in some other way. The wearable medical device is configured to continuously monitor the patient to obtain cardiac-related information (e.g., ECG information, including arrhythmia information, cardiac vibrations, etc.) and / or non-cardiac information (e.g., blood oxygen, patient's body temperature, glucose level, tissue fluid level, and / or lung vibrations). The wearable medical device can perform its monitoring at periodic or aperiodic time intervals or times. For example, the monitoring during an interval or time can be triggered by a user action or other event.

[0155] As described above, the wearable medical device can be configured to monitor other non-ECG physiological parameters of the patient in addition to cardiac-related parameters. For example, the wearable medical device can be configured to monitor, e.g., lung vibrations (e.g., using a microphone and / or accelerometer), respiratory vibrations, sleep-related parameters (e.g., snoring, sleep apnea), tissue fluid (e.g., using a radio frequency transmitter and sensor), etc.

[0156] Other wearable medical devices include automatic heart monitors and / or defibrillators for use in specific professional conditions and / or environments such as combat zones or inside emergency vehicles. Such devices can be configured such that they can be used immediately (or substantially immediately) in life-saving emergencies. In some examples, the mobile medical devices described herein can be pacemaker-enabled, for example, capable of providing therapeutic pacing pulses to a patient. In some examples, the mobile medical devices can be configured to monitor and / or measure ECG metrics, which include, for example, heart rate (such as the mean, median, mode, or other statistical measures of heart rate, and / or maximum, minimum, resting, pre-exercise, and post-exercise heart rate values and / or ranges), heart rate variability metrics, PVC burden or count, atrial fibrillation burden metrics, pauses, heart rate turbulence, QRS height, QRS width, changes in the magnitude or shape of the 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.

[0157] As described above, Figure 3 FIG. shows an example component-level diagram of a medical device controller 300 included in, for example, a wearable medical device. As Figure 3 further shown therein, a therapy delivery circuit 302 can be coupled to one or more electrodes 320 configured to provide therapy to a patient. For example, the therapy delivery circuit 302 can include or be operatively connected to circuit components configured to generate and provide an electrical therapy shock. The circuit components can include, for example, resistors, capacitors, relays, and / or switches, an electrical bridge such as an h-bridge (e.g., including multiple insulated gate bipolar transistors or IGBTs), voltage and / or current measurement components, and other similar circuit components as follows, which are arranged and connected such that the circuit components work in cooperation with the therapy delivery circuit and under the control of one or more processors (e.g., processor 318) to provide, for example, at least one therapy electrode to a patient including one or more pacing, cardioversion, or defibrillation therapy pulses.

[0158] Pacing pulses can be used to treat cardiac arrhythmia conditions such as bradycardia (e.g., less than 30 beats per minute) and tachycardia (e.g., more than 150 beats per minute) using, for example, fixed-rate pacing, demand pacing, and anti-tachycardia pacing. Defibrillation pulses can be used to treat ventricular tachycardia and / or ventricular fibrillation.

[0159] The capacitor may include a parallel-connected capacitor bank composed of multiple capacitors (e.g., two, three, four, or more capacitors). In some examples, the capacitor may include a single film or electrolyte capacitor as a series-connected device including a set of identical capacitors. These capacitors can be switched to series connection during the discharge used for defibrillation pulses. For example, a single capacitor of about 140 uF or greater or four capacitors of about 650 uF can be used. These capacitors can have a rating of 1600 VDC or higher for a single capacitor, or a surge rating of 350 to 500 volts for parallel capacitors, and can be charged by a battery pack for about 15 to 30 seconds.

[0160] For example, each defibrillation pulse can deliver 60 to 180 joules of energy. In some implementations, the defibrillation pulse can be a biphasic truncated exponential waveform, whereby the signal can switch between a positive portion and a negative portion (e.g., the charging direction). This type of waveform can be effective for defibrillating a patient at a lower energy level compared to other types of defibrillation pulses (such as monophasic pulses, etc.). For example, the amplitudes and widths of the two phases of the energy waveform can be automatically adjusted to deliver an exact amount of energy (e.g., 150 joules) regardless of the patient's body impedance. The therapy delivery circuit 302 can be configured to perform switching and pulse delivery operations, for example, under the control of the processor 318. When energy is being delivered to the patient, the amount of energy being delivered can be tracked. For example, even when the pulse waveform is dynamically controlled based on factors such as the patient's body impedance to which the pulse is being delivered, the amount of energy can be maintained at a predetermined constant value.

[0161] In certain examples, the therapy delivery circuit 302 can be configured to deliver a set of cardioversion pulses to correct, for example, an improperly beating heart. Cardioversion generally involves delivering a less powerful electric shock at a specific frequency to mimic the normal rhythm of the heart compared to defibrillation as described above.

[0162] The data storage unit 304 can include one or more of non-transitory computer-readable media such as flash memory, solid-state memory, magnetic memory, optical memory, cache memory, combinations thereof, etc. The data storage unit 304 can be configured to store executable instructions and data for operating the medical device controller 300. In certain examples, the data storage unit can include executable instructions, where these executable instructions, when executed, are configured to cause the processor 318 to perform one or more operations. In some examples, the data storage unit 304 can be configured to store information such as ECG data received from, for example, a sensing electrode interface.

[0163] In some examples, network interface 306 may facilitate information communication between medical device controller 300 and one or more other devices or entities via a communication network. For example, in the case where medical device controller 300 is included in a mobile medical device, network interface 306 may be configured to communicate with a remote computing device such as a remote server or other similar computing devices. Network interface 306 may include communication circuitry for sending data according to a wireless standard to exchange such data over a short distance to an intermediate device. For example, such an intermediate device may be configured as a base station, a "hotspot" device, a smart phone, a tablet computer, a portable computing device, and / or other devices near a wearable medical device including medical device controller 300. The (one or more) intermediate devices may in turn communicate the data to the remote server via a broadband cellular network communication link. The communication link may implement broadband cellular technologies (e.g., 2.5G, 2.75G, 3G, 4G, 5G cellular standards) and / or Long Term Evolution (LTE) technologies or GSM / EDGE and UMTS / HSPA technologies for high-speed wireless communication. In some implementations, the (one or more) intermediate devices may communicate with the remote server via a Wi-Fi TM communication link based on the IEEE 802.11 standard.

[0164] In certain examples, user interface 308 may include one or more physical interface devices such as input devices, output devices, and combined input / output devices, as well as a software stack configured to drive the operation of the devices. These user interface elements may present visual, audio, and / or tactile content. Thus, user interface 308 may receive input or provide output to enable a user to interact with medical device controller 300.

[0165] Medical device controller 300 may also include at least one rechargeable battery 310 configured to provide power to one or more components integrated in medical device controller 300. Rechargeable battery 310 may include a rechargeable multi-cell battery pack. In one example implementation, rechargeable battery 310 may include three or more 2200 mAh lithium-ion batteries for providing power to other device components within medical device controller 100. For example, rechargeable battery 310 may provide a power output ranging from 20 mA to 1000 mA (e.g., 40 mA) and may support a runtime of 24 hours, 48 hours, 72 hours, or more between each charge. In certain implementations, the battery capacity, runtime, and type (e.g., lithium-ion, nickel-cadmium, or nickel-metal hydride) may be varied to best suit the specific application of medical device controller 300.

[0166] The sensor interface 312 may include physiological signal circuitry that is coupled to one or more sensors configured to monitor one or more physiological parameters of a patient. As shown, these sensors may be coupled to the medical device controller 300 via a wired or wireless connection. These sensors may include one or more ECG sensing electrodes 322 and non-ECG physiological sensors 323 (such as a vibration sensor 324, a tissue fluid monitor 326 (e.g., based on an ultra-wideband RF device), and a motion sensor (e.g., an accelerometer, a gyroscope, and / or a magnetometer), etc.). In some implementations, in addition to digital sensing electrodes, these sensors may also include a plurality of conventional ECG sensing electrodes.

[0167] The sensing electrode 322 may be configured to monitor the patient's ECG information. For example, by design, the digital sensing electrode 322 may include a skin contact electrode surface that may be considered polarizable or non-polarizable depending on various factors including the metal and / or coating used in constructing the electrode surface. All such electrodes may be used with the principles, techniques, devices, and systems described herein. For example, the electrode surface may be based on stainless steel, a noble metal such as platinum, or Ag-AgCl.

[0168] In some examples, the electrode 322 may be used with an electrolyte gel that is dispersed between the electrode surface and the patient's skin. In certain implementations, the electrode 322 may be a dry electrode that does not require an electrolytic material. For example, such a dry electrode may be based on tantalum metal and have a tantalum pentoxide coating as described above. Such a dry electrode may be more comfortable for long-term monitoring applications.

[0169] Return reference Figure 3, the vibration sensor 324 can be configured to detect cardiac or pulmonary vibration information. For example, the vibration sensor 324 can detect cardiac valve vibration information of a patient. For example, the vibration sensor 324 can be configured to detect cardiac vibration signal values including any one or all of S1, S2, S3, and S4. Based on these cardiac vibration signal values or cardiac vibration values, certain cardiac vibration metrics can be calculated, and these cardiac vibration metrics include any one or more than one of electromechanical activation time (EMAT), average EMAT, percentage of EMAT (%EMAT), systolic dysfunction index (SDI), and left ventricular systolic time (LVST). The vibration sensor 324 can also be configured to detect cardiac wall motion, for example, by placing the sensor in the region of the apical impulse. The vibration sensor 324 can include a vibration sensor configured to detect vibrations from the cardiac and pulmonary systems of a subject and provide an output signal in response to the detected vibrations of the target organ. For example, it can detect vibrations generated in the trachea or lungs due to air flow during breathing. In certain implementations, additional physiological information can be determined from the lung vibration signal (such as lung vibration characteristics, etc.) based on sounds generated in the lungs (such as wheezing sounds, rales, etc.). The vibration sensor 324 can also include a multi-channel accelerometer, for example, a three-channel accelerometer, configured to sense movement on each of the three orthogonal axes so that patient movement / body position can be detected and correlated with the detected cardiac vibration information. The vibration sensor 324 can send information describing the cardiac vibration information to the sensor interface 312 for subsequent analysis.

[0170] The interstitial fluid monitor 326 can use radio frequency (RF)-based techniques to evaluate the liquid level and accumulation in a patient's body tissue. For example, the interstitial fluid monitor 326 can be configured to measure the liquid content in the lungs, which is commonly used for diagnosing and subsequently observing pulmonary edema or pulmonary congestion in patients with heart failure. The interstitial fluid monitor 326 can include one or more than one antenna, where the one or more than one antenna is configured to direct RF waves through the patient's tissue and measure an output RF signal in response to the waves that have passed through the tissue. In certain implementations, the output RF signal includes a parameter representing the liquid level in the patient's tissue. The interstitial fluid monitor 326 can send information describing the tissue liquid level to the sensor interface 312 for subsequent analysis.

[0171] In some implementations, the cardiac event detector 316 can be configured to monitor an ECG signal of a patient for a cardiac event such as an arrhythmia or other similar cardiac event. The cardiac event detector can be configured to operate in cooperation with the processor 318 to perform one or more methods of processing the ECG signal received from, for example, the sensing electrodes 322 and determining the likelihood that the patient is experiencing a cardiac event. The cardiac event detector 316 can be implemented using hardware or a combination of hardware and software. For example, in some examples, the cardiac event detector 316 can be implemented as a software component stored within the data store 304 and executed by the processor 318. In this example, the instructions included in the cardiac event detector 316 can cause the processor 318 to perform one or more methods for analyzing the received ECG signal to determine whether an adverse cardiac event is occurring. In other examples, the cardiac event detector 316 can be a dedicated integrated circuit (ASIC) coupled to the processor 318 and configured to monitor for the occurrence of an adverse cardiac event for the ECG signal. Thus, examples of the cardiac event detector 316 are not limited to a particular hardware or software implementation.

[0172] In some implementations, the processor 318 includes one or more processors (or one or more processor cores), where each of the one or more processors is configured to perform a series of instructions for obtaining operational data and / or controlling the operation of other components of the medical device controller 300. In some implementations, when performing a specific process (e.g., cardiac monitoring), the processor 318 may be configured to make a determination based on specific logic based on the received input data, and is also configured to provide one or more outputs that can be used to control or otherwise notify subsequent processes to be executed by the processor 318 and / or other processors or circuits communicatively coupled to the processor 318. Thus, the processor 318 reacts to a specific input stimulus in a specific manner and generates a corresponding output based on the input stimulus. In some example cases, the processor 318 may continue to perform a series of logical transformations, where various internal register states and / or other bit cell states inside or outside the processor 318 may be set to logic high or logic low. As mentioned herein, the processor 318 may be configured to execute functions stored in a data storage coupled to the processor 318, and the software is configured to cause the processor 318 to continue to make a series of various logical decisions that result in the execution of the function. The various components described herein as being executable by the processor 318 may be implemented in various forms of dedicated hardware, software, or a combination thereof. For example, the processor 318 may be a digital signal processor (DSP), such as a 24-bit DSP. The processor 318 may be, for example, a multi-core processor having two or more processing cores. The processor 318 may be an advanced RISC machine (ARM) processor, such as a 32-bit ARM processor or a 64-bit ARM processor. The processor 318 may execute an embedded operating system and include services provided by the operating system that can be used for file system operations, display and audio generation, basic networking, firewall, data encryption, and communication.

[0173] As described above, a mobile medical device such as a WCD can be designed to include a digital front end in which analog signals sensed by the skin contact electrode surfaces of a set of digital sensing electrodes are converted to digital signals for processing. A typical mobile medical device with an analog front end configuration uses circuitry to accommodate signals from the sensing electrodes having a high source impedance (e.g., having an internal impedance range from about 100 kiloohms to 1 or more megaohms). The high source impedance signals are processed and sent to a monitoring device (such as the processor 318 of the controller 300 described above) for further processing. In some implementations, the monitoring device or another similar processor (such as a microprocessor or another dedicated processor operably coupled to the sensing electrodes) can be configured to receive common mode noise signals from the respective sensing electrodes, sum the common mode noise signals, invert the summed common mode noise signals, and use, for example, a driven right leg circuit to feed the inverted signal back to the patient as a driven ground to cancel the common mode signal.

[0174] Figure 11A FIG. shows an example medical device 1100 that is external, mobile, and wearable by a patient 1102 and is configured to implement one or more of the configurations described herein. For example, the medical device 1100 can be a non-invasive medical device configured to be located substantially outside of the patient's body. Such a medical device 1100 can be, for example, a mobile medical device that is capable and designed to move with the patient as the patient goes about his or her daily business. For example, the medical device 1100 as described herein can be attached to the patient's body, such as a wearable cardioverter defibrillator available from Medical Corporation. Such a wearable defibrillator is typically worn for 2 to 3 months at a time, almost continuously or substantially continuously. During the period of time the patient wears the wearable defibrillator, the wearable defibrillator can be configured to continuously or substantially continuously monitor the patient's vital signs and, when determined to be necessary, can be configured to deliver one or more therapeutic electrical pulses to the patient. For example, such a therapeutic shock can be a pacing, defibrillation, or transcutaneous electrical nerve stimulation (TENS) pulse.

[0175] The medical device 1100 can include one or more of the following: a garment 1110, one or more ECG sensing electrodes 1112, one or more non-ECG physiological sensors 1113, one or more therapeutic electrodes 1114a and 1114b (collectively referred to herein as therapeutic electrodes 1114), a medical device controller 1120 (e.g., as described above in Figure 3the controller 300 as described in the discussion), the connection box 1130, the patient interface box 1140, the strap 1150, or any combination thereof. In some examples, at least some of the components of the medical device 1100 may be configured to adhere to a garment 1110 that can be worn around the patient's torso (or, in some examples, permanently integrated into the garment 1110).

[0176] The medical device controller 1120 may be operably coupled to the sensing electrode 1112, which may be adhered to the garment 1110 (e.g., assembled into the garment 110 or removably attached to the garment) using, for example, hook-and-loop fasteners. In some implementations, the sensing electrode 1112 may be permanently integrated into the garment 1110. The medical device controller 1120 may be operably coupled to the therapy electrode 1114. For example, the therapy electrode 1114 may also be assembled into the garment 1110, or in some implementations, the therapy electrode 1114 may be permanently integrated into the garment 1110. In an example, the medical device controller 1120 includes a patient user interface 1160 to allow the patient to connect with the external wearable device. For example, the patient may use the patient user interface 1160 to respond to activity-related questions, prompts, and surveys as described herein.

[0177] In addition to Figure 11A component configurations other than those shown are possible. For example, the sensing electrode 1112 may be configured to be attached at various locations around the body of the patient 1102. The sensing electrode 1112 may be operably coupled to the medical device controller 1120 via the connection box 1130. In some implementations, the sensing electrode 1112 may be adhesively attached to the patient 1102. In some implementations, at least one of the sensing electrode 1112 and the therapy electrode 1114 may be included on a single integrated patch and adhesively applied to the patient's body.

[0178] The sensing electrode 1112 may 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 sensor 1113 is such as an accelerometer, a vibration sensor, an RF-based sensor, and other measurement devices for recording additional non-ECG physiological parameters. For example, as described above, such non-ECG physiological sensors are configured to detect other types of patient physiological parameters and acoustic signals, such as tissue fluid level, cardiac vibrations, lung vibrations, respiratory vibrations, patient movement, etc.

[0179] In some examples, the therapy electrode 1114 may also be configured to include sensors configured to detect ECG signals and other physiological signals of the patient. In some examples, the connection box 1130 may include a signal processor configured to amplify, filter, and digitize the cardiac signals before sending them to the medical device controller 1120. One or more than one therapy electrode 1114 may be configured to deliver one or more therapeutic defibrillation shocks to the body of the patient 1102 when the medical device 1100 determines, based on signals detected by the sensing electrode 1112 and processed by the medical device controller 1120, that such a treatment is approved. Example therapy electrodes 1114 may include metal electrodes such as stainless steel electrodes, where the conductive metal electrodes include one or more than one conductive gel deployment device configured to deliver a conductive gel to the metal electrode before delivering the therapeutic shock.

[0180] In some examples, the medical device 1100 may also include one or more than one motion sensor such as the accelerometer 1162. As Figure 11A shown, in some examples, the accelerometer 1162 may be integrated into one or more than one of the sensing electrode 1112, the therapy electrode 1114, the medical device controller 1120, and various other components of the medical device 1100.

[0181] In some implementations, a medical device as described herein may be configured to switch between a therapy medical device and a monitoring medical device, where the monitoring medical device is configured to only monitor the patient (e.g., not provide or perform any therapeutic functions). For example, therapy components such as the therapy electrode 1114 and associated circuitry may optionally be decoupled from (or coupled to) the medical device or disconnected from (or connected to) the medical device. For example, the medical device may have optional therapy elements (e.g., defibrillation and / or pacing electrodes, components, and associated circuitry) configured to operate in a therapy mode. The optional therapy elements may be physically decoupled from the medical device to convert the therapy medical device into a monitoring medical device for a particular use (e.g., for operating in a monitoring-only mode) or for a particular patient. Alternatively, the optional therapy elements may be deactivated (e.g., via a physical or software switch), thereby essentially causing the therapy medical device to behave as a monitoring medical device for a particular physiological purpose or a particular patient. As an example of a software switch, an authorized person may access a protected user interface of the medical device and select a pre-configured option or perform some other user action via the user interface to deactivate the therapy elements of the medical device.

[0182] Figure 11BIllustrated is an external, wearable, and flow-through hospital wearable defibrillator 1100A that can be worn by a patient 1102. In some implementations, the hospital wearable defibrillator 1100A can be configured to provide pacing therapy, e.g., to treat bradycardia, tachycardia, and asystole conditions. The hospital wearable defibrillator 1100A can include one or more ECG sensing electrodes 1112a, one or more therapy electrodes 1114a and 1114b, a medical device controller 1120, and a connection box 1130. For example, each of these components can be constructed and used as the same number of components of the medical device 1100. For example, the electrodes 1112a, 1114a, 1114b can include disposable adhesive electrodes. For example, the electrodes can include sensing and therapy components disposed on separate sensing electrode adhesive patches and therapy electrode adhesive patches. In some implementations, both the sensing and therapy components can be integrated and disposed on the same electrode adhesive patch that is then attached to the patient. For example, the front attachable therapy electrode 1114a is attached to the front of the patient's torso to deliver pacing or defibrillation therapy. Similarly, the back attachable therapy electrode 1114b is attached to the back of the patient's torso. In an example scenario, at least three ECG attachable sensing electrodes 1112a can be attached at least above the patient's chest near the right arm, above the patient's chest near the left arm, and attached towards the bottom of the patient's chest in a manner prescribed by a trained professional.

[0183] A patient being monitored by a hospital defibrillator and / or pacing device may be restricted to a hospital bed or room for a significant amount of time (e.g., 75% or more of the patient's hospital stay). As a result, the user interface 1160a can be configured to interact with a user outside of the patient (e.g., a nurse) to perform device-related functions such as initial device baselining, setting and adjusting patient parameters, and replacing the device battery.

[0184] In some examples, the hospital wearable defibrillator 1100A can also include one or more motion sensors such as an accelerometer 1162a. As Figure 11B shown, in some examples, the accelerometer 1162a can be integrated into one or more of the following: the sensing electrode 1112a (e.g., integrated into the same patch as the sensing electrode), the therapy electrode 1114a (e.g., integrated into the same patch as the therapy electrode), the medical device controller 1120, the connection box 1130, and various other components of the hospital wearable defibrillator 1100A.

[0185] In some implementations, examples of therapeutic medical devices including a digital front end in accordance with the systems and methods described herein can include short-term defibrillators and / or pacing devices. For example, such short-term devices can be prescribed by a doctor for a patient presenting with syncope. A wearable defibrillator can be configured to monitor a patient presenting with syncope by, for example, analyzing the patient's physiological and cardiac activity to detect abnormal patterns that may indicate abnormal physiological function. For example, such abnormal patterns can occur before, during, or after a syncope episode. In such an example implementation of a short-term wearable defibrillator, the electrode assembly can be adhesively attached to the patient's skin and have a configuration similar to that of the hospital wearable defibrillator described above in connection with Figure 11A and having a configuration similar to that of the hospital wearable defibrillator described above.

[0186] Figure 11C and Figure 11D illustrate example wearable patient monitoring devices having no treatment or therapeutic 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 the patient's condition. For example, such physiological parameters can include the patient's ECG information, tissue (e.g., lung) fluid levels, cardiac vibrations (e.g., using an accelerometer or microphone), and other relevant cardiac information. The cardiac monitoring device is a portable device that a patient can carry with them as they go about their daily business.

[0187] Referring Figure 11C , example wearable patient monitoring device 1100C can include a tissue fluid monitor 1165 that uses RF-based technology to evaluate the fluid levels and accumulations in a patient's body tissue. Such a tissue fluid monitor 1165 can be configured to measure the fluid content in the lungs, typically for the diagnosis and follow-up of pulmonary edema or pulmonary congestion in heart failure patients. The tissue fluid monitor 1165 can include one or more antennas configured to direct RF waves through the patient's tissue and measure an output RF signal in response to the waves that have passed through the tissue. In certain implementations, the output RF signal includes a parameter representing the fluid level in the patient's tissue. In an example, device 1100C can be a cardiac monitoring device that further includes digital sensing electrodes 1170 for sensing the patient's ECG activity. Device 1100C can preprocess the ECG signal under the control of a microprocessor via one or more ECG processing and / or conditioning circuits (such as an ADC, operational amplifier, digital filter, signal amplifier, etc.). Device 1100C can send information describing the ECG activity and / or tissue fluid level to a remote server for analysis via a network interface. Additionally, in certain implementations, device 1100C can include one or more accelerometers 1162c.

[0188] Referring Figure 11D, Another example wearable cardiac monitoring device 1100D can be attached to a patient via at least three adherent digital cardiac sensing electrodes 1175 disposed around the patient's torso. Additionally, in some implementations, device 1100D can include one or more accelerometers 1162D integrated into one or more of the digital sensing electrodes, for example, as described herein for measuring motion signals.

[0189] Cardiac devices 1100C and 1100D are used in cardiac monitoring and telemetry and / or continuous cardiac event monitoring applications, for example, in patient populations reporting irregular cardiac symptoms and / or conditions. These devices can send information describing ECG activity and / or tissue fluid levels to a remote server via a network interface for analysis. Example cardiac conditions that can be monitored include atrial fibrillation (AF), bradycardia, tachycardia, atrioventricular block, Lown-Ganong-Levine syndrome, atrial flutter, sinoatrial node dysfunction, cerebral ischemia, (one or more) ectopy and / or palpitations. For example, cardiac monitoring for an extended period of time (e.g., 10 to 30 days or longer) can be prescribed for such patients. In some ambulatory cardiac monitoring and / or telemetry applications, a portable cardiac monitoring device can be configured to substantially continuously monitor a patient for cardiac abnormalities, and when such an abnormality is detected, the monitor can automatically send data related to the abnormality to a remote server. The remote server can be located within a 24-hour manned monitoring center, where the data is interpreted by qualified cardiac-trained examiners and / or HCPs, and feedback is provided to the patient and / or designated HCP via detailed periodic or event-triggered reports. In some cardiac event monitoring applications, the cardiac monitoring device is configured to allow the patient to manually press a button on the cardiac monitor to report symptoms. For example, the patient can report symptoms such as skipped beats, shortness of breath, dizziness, rapid heart rate, fatigue, fainting, chest discomfort, weakness, lightheadedness, and / or blurred vision. The cardiac monitoring device can record a patient's predetermined physiological parameters (e.g., ECG information) for a predetermined amount of time (e.g., 1 to 30 minutes before and 1 to 30 minutes after reporting the symptom). As described above, the cardiac monitoring device can be configured to monitor physiological parameters other than cardiac-related parameters. For example, the cardiac monitoring device can be configured to monitor, for example, cardiac vibration signals (e.g., using an accelerometer or microphone), lung vibration signals, respiratory vibrations, sleep-related parameters (e.g., snoring, sleep apnea), tissue fluid, etc.

[0190] In some examples, the devices described herein (e.g., Figures 11A to 11D ) can communicate with a remote server via an intermediate or gateway device 1180 such as the intermediate or gateway device 1180 shown in Figure 11D . For example, such as Figure 11ADevices such as those shown in FIGS. A - D can be configured to include network interface communication capabilities as described herein with reference to, for example, Figure 3 as described.

[0191] Additionally, the devices described herein (e.g., Figures 11A to 11D ) can be configured to include one or more accelerometers as described herein. For example, as discussed above in Figure 1A and Figure 1B , one or more sensors such as accelerometers, vibration sensors, and RF sensors can be integrated into various components of a wearable device or included as stand - alone sensors configured to measure various signals of a patient.

[0192] Although the subject matter contained herein has been described in detail for purposes of illustration, it should be understood that these details are for that purpose only and that the invention is not limited to the disclosed embodiments. On the contrary, the invention is intended to cover modifications and equivalent arrangements within the scope of the appended claims. For example, it should be understood that the invention contemplates, to the extent possible, that one or more features of any embodiment can be combined with one or more features of any other embodiment.

[0193] Other examples are within the scope of the specification and claims. Additionally, certain of the above - described functions can be implemented using software, hardware, firmware, hard - wiring, or any combination thereof. The features implementing the functions can also be physically located in various positions, including being distributed such that portions of the functions are implemented at different physical locations.

Claims

1. A patient monitoring device configured to monitor a patient's cardiac activity and sleep stage information, the patient monitoring device comprising: A plurality of electrodes configured to be externally coupled to a patient to obtain an electrocardiogram signal, i.e., an ECG signal, from the patient and to provide a therapeutic shock to the patient in response to detecting a cardiac arrhythmia, the patient monitoring device being configured to provide detection of the cardiac arrhythmia based on one or more cardiac arrhythmia detection parameters; At least one motion sensor configured to generate at least one motion signal based on the movement of the patient; And At least one processor operatively coupled to the plurality of electrodes and the at least one motion sensor, the at least one processor being configured to: Receive the at least one motion signal from the at least one motion sensor and derive one or more motion parameters from the at least one motion signal, Receive the ECG signal from the plurality of electrodes and derive one or more ECG parameters from the ECG signal, Determine whether the patient is in a fixed sleep stage or a non-fixed sleep stage based on an analysis of the one or more motion parameters and / or the one or more ECG parameters, wherein the fixed sleep stage includes at least one of the N3 sleep stage, N4 sleep stage, and REM sleep stage, and the non-fixed sleep stage includes at least one of wakefulness, N1 sleep stage, and N2 sleep stage, In the case where the at least one processor determines that the patient is in a fixed sleep stage, adjust the one or more cardiac arrhythmia detection parameters such that the patient monitoring device operates in a first monitoring and treatment mode, and Use the first monitoring and treatment mode to monitor the patient for the cardiac arrhythmia.

2. The patient monitoring device according to claim 1, wherein, The at least one processor is capable of determining whether the patient is in a fixed sleep stage by being configured to: Monitor the ECG signal to determine whether a heart rate deviation from the patient's baseline resting heart rate exceeds a deviation threshold over a period of time; Analyze the one or more motion parameters over the period of time; and Determine whether the patient is in a fixed sleep stage based on the heart rate deviation and the analysis of the one or more motion parameters over the period of time.

3. The patient monitoring device according to claim 2, wherein there is one or both of the following two items: The deviation threshold includes one of a 1% deviation from the baseline resting heart rate, a 2% deviation from the baseline resting heart rate, and a 5% deviation from the baseline resting heart rate; and The period of time includes at least one of 5 minutes, 7 minutes, 10 minutes, 30 minutes, 45 minutes, and 1 hour.

4. The patient monitoring device according to any one of claims 1-3, wherein, The at least one processor is further configured to: Adjust the one or more cardiac arrhythmia detection parameters such that the patient monitoring device operates in a second monitoring and treatment mode; and Use the second monitoring and treatment mode to monitor the patient for the cardiac arrhythmia.

5. The patient monitoring device according to claim 4, wherein, The at least one processor is further configured to: Monitor the patient using the second monitoring and handling mode; Determine whether the patient has transitioned from the non-fixed sleep stage to the fixed sleep stage; And In the case where the at least one processor determines that the patient has transitioned from the non-fixed sleep stage to the fixed sleep stage, monitor the patient using the first monitoring and handling mode.

6. The patient monitoring device according to any one of claims 1-3, wherein, The at least one processor is further configured to start monitoring physiological signals other than the ECG signal when the at least one processor determines that the patient is in the fixed sleep stage.

7. The patient monitoring device according to claim 6, wherein, The patient monitoring device further includes one or both of the following two items: A radio frequency sensor, i.e., an RF sensor, wherein the physiological signals other than the ECG signal include RF-based physiological signals, and wherein the at least one processor is further configured to determine at least one of cardiac wall movement information and thoracic liquid level information based on the RF-based physiological signals; and A cardiac vibration sensor, wherein the physiological signals other than the ECG signal include one or more cardiac vibration signals of the patient, and wherein the at least one processor is further configured to determine one or more electromechanical parameters of the heart of the patient based on the cardiac vibration signals.

8. The patient monitoring device according to any one of claims 1-3, wherein, The at least one processor is further configured to adjust one or more handling parameters when the at least one processor determines that the patient is in the fixed sleep stage; and Wherein the one or more handling parameters include one or more of a pacing pulse rate, a high-energy pacing pulse level, a low-energy pacing pulse level, a defibrillation shock level, and defibrillation shock timing information.

9. The patient monitoring device according to any one of claims 1-3, wherein, The at least one processor is further configured to adjust one or more alarm parameters when the at least one processor determines that the patient is in the fixed sleep stage, and Wherein the one or more alarm parameters include at least one of an alarm type, an alarm volume, an alarm duration, and patient response time information.

10. The patient monitoring device according to any one of claims 1-3, wherein, The one or more motion parameters include rotational motion parameters that quantify the rotational motion of the patient measured by the at least one motion sensor.

11. The patient monitoring device according to any one of claims 1-3, wherein, The one or more motion parameters include one or more of patient breathing information, patient limb movement information, and patient body position information.

12. The patient monitoring device according to claim 1, wherein, The at least one processor is further configured to derive one or more additional motion parameters from one or more impedance-based measurement results from the plurality of electrodes, and Wherein the at least one processor is further configured to further determine whether the patient is in the fixed sleep stage or the non-fixed sleep stage based on the one or more additional motion parameters.

13. The patient monitoring device according to any one of claims 1-3, wherein, The one or more ECG parameters include one or more of the following: heart rate, heart rate variability, ventricular premature beat burden or count (i.e., PVC burden or count), atrial fibrillation burden, pauses, heart rate turbulence, QRS height, QRS width, change in magnitude or shape of the morphology of the ECG signal, cosine R-T, artificial pacing, QT interval, QT variability, T wave width, T wave alternans, T wave variability, and ST segment change.

14. The patient monitoring device according to any one of claims 1-3, wherein, The one or more cardiac arrhythmia detection parameters include one or more of ventricular tachycardia onset heart rate, ventricular fibrillation onset heart rate, bradycardia onset heart rate, tachycardia onset heart rate, and cardiac arrest onset threshold.

15. The patient monitoring device according to any one of claims 1-3, wherein, The at least one processor is further configured to adjust at least one of a cardiac arrhythmia detection confidence level and a noise threshold when the at least one processor determines that the patient is in a stationary sleep stage, wherein the cardiac arrhythmia detection confidence level is used by the at least one processor for one or both of the following: identification of the cardiac arrhythmia and verification of a disposition of the cardiac arrhythmia prior to delivery, and wherein the noise threshold defines a level of noise in the ECG signal within which derivation of the one or more ECG parameters is permitted.

16. The patient monitoring device according to any one of claims 1-3, wherein, The at least one processor is further configured to: monitor the patient's heart rate to derive patient heart rate information when the processor determines that the patient is in a stationary sleep stage; compare the patient heart rate information with the patient's baseline resting heart rate; and adjust and / or verify the baseline resting heart rate based on the comparison of the patient heart rate information with the baseline resting heart rate to determine an updated baseline resting heart rate for the patient.

17. The patient monitoring device according to any one of claims 1-3, wherein, The at least one processor is further configured to, when the processor determines that the patient is in a stationary sleep stage: determine at least one occurrence of a ventricular premature beat (i.e., PVC) from the ECG signal; monitor changes in the ECG signal at the time of the PVC occurrence to measure the cardiac response of the patient's heart after the PVC; determine a heart rate turbulence value for the patient based on the monitored changes in the ECG signal; and store the heart rate turbulence value on a computer-readable medium operatively coupled to the at least one processor for analysis.

18. A computer-readable medium storing instructions that, when executed by at least one processor of a patient monitoring device for monitoring a patient's cardiac activity and sleep stage information, cause the at least one processor to perform: obtain an electrocardiogram signal (i.e., ECG signal) from the patient from a plurality of electrodes configured to be externally coupled to the patient, the plurality of electrodes also being provided to deliver a therapeutic shock to the patient in response to detection of a cardiac arrhythmia; detect the cardiac arrhythmia based on one or more cardiac arrhythmia detection parameters; receive at least one motion signal from at least one motion sensor based on movement of the patient and derive one or more motion parameters from the at least one motion signal; Receive the ECG signal from the plurality of electrodes and derive one or more ECG parameters from the ECG signal; Determine whether the patient is in a stationary sleep stage or a non-stationary sleep stage based on an analysis of the one or more motion parameters and / or the one or more ECG parameters, wherein the stationary sleep stage includes at least one of N3 sleep stage, N4 sleep stage, and REM sleep stage, and the non-stationary sleep stage includes at least one of wakefulness, N1 sleep stage, and N2 sleep stage; In the case where the at least one processor determines that the patient is in a stationary sleep stage, adjust the one or more cardiac arrhythmia detection parameters such that the patient monitoring device operates in a first monitoring and handling mode; And Use the first monitoring and handling mode to monitor the patient for the cardiac arrhythmia.

19. The computer-readable medium according to claim 18, wherein, Cause the at least one processor to perform the following operations through the instructions, where the at least one processor is capable of determining whether the patient is in a stationary sleep stage: Monitor the ECG signal to determine whether a heart rate deviation from the patient's baseline resting heart rate exceeds a deviation threshold over a period of time; Analyze the one or more motion parameters over the period of time; and Determine whether the patient is in a stationary sleep stage based on the heart rate deviation and the analysis of the one or more motion parameters over the period of time.

20. The computer-readable medium according to claim 19, wherein there is one or both of the following two items: The deviation threshold includes one of a 1% deviation from the baseline resting heart rate, a 2% deviation from the baseline resting heart rate, and a 5% deviation from the baseline resting heart rate; and The period of time includes at least one of 5 minutes, 7 minutes, 10 minutes, 30 minutes, 45 minutes, and 1 hour.

21. The computer-readable medium according to any one of claims 18-20, wherein, The instructions further cause the at least one processor to: Adjust the one or more cardiac arrhythmia detection parameters such that the patient monitoring device operates in a second monitoring and handling mode; and Use the second monitoring and handling mode to monitor the patient for the cardiac arrhythmia.

22. The computer-readable medium according to claim 21, wherein The instructions further cause the at least one processor to: Use the second monitoring and handling mode to monitor the patient; Determine whether the patient has transitioned from the non-stationary sleep stage to the stationary sleep stage; And In the case where the at least one processor determines that the patient has transitioned from the non-stationary sleep stage to the stationary sleep stage, use the first monitoring and handling mode to monitor the patient.

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