Chronic periodic monitoring of atrial tachyarrhythmia detection

Abstract

Systems and methods for detecting arrhythmia are discussed. A medical device system includes a mobile monitoring device and an arrhythmia analysis device communicatively coupled to each other. The mobile monitoring device may sense a cardiac signal from a patient and intermittently collect data segments of the cardiac signal according to a data segment collection rate or a schedule during a monitoring period. Each data segment has a specific segment duration. The monitoring device may send intermittently collected data segments to the arrhythmia analysis device according to a transmission schedule or in response to a trigger event. The arrhythmia analysis device may detect an arrhythmia from the intermittently collected data segments. A segment duration or a data segment collection rate or schedule may be adjusted based on a performance measure of the arrhythmia detection.

Description

[0001] Related Applications

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 422,320, filed on Nov. 3, 2022, the entire content of which is incorporated herein by reference. Technical Field

[0003] The present invention generally relates to medical devices, and more particularly, to systems, devices, and methods for detecting atrial tachyarrhythmias. Background Art

[0004] Mobile medical devices (AMDs), such as wearable medical devices or implantable medical devices (IMDs), have been used to monitor a patient's health condition or disease state. Some AMDs are capable of delivering one or more modalities of treatment to a patient. For example, an implantable cardioverter defibrillator (ICD) can be used to monitor certain abnormal heart rhythms and deliver electrical energy to the heart to correct the abnormal rhythm. Some IMDs can be used to monitor the chronic deterioration of cardiac hemodynamic performance (such as due to congestive heart failure (CHF)) and provide cardiac stimulation therapy, including cardiac resynchronization therapy (CRT), to correct cardiac dyssynchrony within or between ventricles.

[0005] Some AMDs can record physiological data over a long period of time to monitor arrhythmias. One type of arrhythmia is atrial fibrillation (AF), which is considered the most common clinical arrhythmia affecting millions of people. During AF, chaotic electrical impulses originating from areas within or near the atria can lead to irregular conduction to the ventricles, resulting in a rapid and irregular heart rate. AF can be paroxysmal, lasting from a few minutes to several days before self-terminating. Persistent AF can last more than a week and typically requires medication or other treatment to restore normal sinus rhythm. If normal cardiac rhythm cannot be restored with treatment, AF is permanent. AF may be associated with stroke and requires anticoagulation therapy.

[0006] The timely detection of atrial arrhythmias can be clinically very important for assessing cardiac function. Atrial tachyarrhythmias are characterized by a rapid atrial rate and an irregular ventricular rate. However, the irregular ventricular rate can be caused by confounding factors such as respiration-mediated sinus arrhythmia and affect the specificity of atrial arrhythmia detection. Improper detection of atrial arrhythmias can have an adverse impact on the treatment outcome of a patient. Summary of the Invention

[0007] Among other things, this document discusses systems, devices, and methods for detecting arrhythmias, such as atrial fibrillation (AF). A medical device system includes a wearable monitoring device and an arrhythmia analysis device that are communicatively coupled to each other. The wearable monitoring device can sense cardiac signals from a patient and intermittently collect data segments (also referred to as snippets) of the cardiac signals during a monitoring period according to a data segment collection rate or schedule. Each data segment has a specific segment duration. The monitoring device can send the intermittently collected data segments to the arrhythmia analysis device according to a transmission schedule or in response to a trigger event. The arrhythmia analysis device can detect arrhythmias from the intermittently collected data segments. The segment duration or the data segment collection rate or schedule can be adjusted based on performance metrics of the arrhythmia detection.

[0008] Example 1 is a system for monitoring a patient at risk of arrhythmia, the system including: a wearable monitoring device configured to: sense cardiac signals from the patient via a cardiac sensor; and intermittently collect data segments of the sensed cardiac signals during a monitoring period according to a data segment collection rate or schedule, each data segment having a segment duration; and an arrhythmia analysis device communicatively coupled to the wearable monitoring device, the arrhythmia analysis device being configured to detect arrhythmias using the intermittently collected data segments received from the wearable monitoring device, wherein the wearable monitoring device is configured to send the intermittently collected data segments to the arrhythmia analysis device according to a transmission schedule or in response to a trigger event.

[0009] In Example 2, the subject matter of Example 1 optionally includes: an arrhythmia analysis device that can be configured to determine a confidence level of the detection of an arrhythmia; and a wearable monitoring device that can be configured to adjust the segment duration to include increasing the segment duration in response to the determined confidence level falling below a confidence threshold and maintaining or reducing the segment duration in response to the determined confidence level exceeding the confidence threshold.

[0010] In Example 3, the subject matter of any one or more of Examples 1-2 optionally includes: an arrhythmia analysis device that can be configured to determine the instability of a detected arrhythmia; and a wearable monitoring device that can be configured to adjust the segment duration to include increasing the segment duration in response to the determined instability exceeding an instability threshold and maintaining or reducing the segment duration in response to the determined instability falling below the instability threshold.

[0011] In Example 4, the subject matter of any one or more of Examples 1-3 optionally includes: a mobile monitoring device that can be configured to periodically collect data segments with a data collection period longer than the segmented duration.

[0012] In Example 5, the subject matter of any one or more of Examples 1-4 optionally includes: a mobile monitoring device that can be configured to intermittently collect data segments according to a data collection schedule such that the time interval between at least two adjacent collections is longer than the segmented duration.

[0013] In Example 6, the subject matter of any one or more of Examples 1-5 optionally includes: a mobile monitoring device that can be configured to adjust the data segment collection rate or schedule to include: intermittently collecting data segments according to a first data segment collection rate or schedule during a first monitoring period; and intermittently collecting data segments according to a second data segment collection rate or schedule different from the first data segment collection rate or schedule during a second monitoring period.

[0014] In Example 7, the subject matter of Example 6 optionally includes: an arrhythmia analysis device that can be configured to determine the characteristics of a detected arrhythmia, wherein the mobile monitoring device is configured to adjust the data segment collection rate or schedule based on the determined characteristics of the detected arrhythmia.

[0015] In Example 8, the subject matter of Example 7 optionally includes: the characteristics of the detected arrhythmia, which can include one or more of the duration or heart rate of the detected arrhythmia.

[0016] In Example 9, the subject matter of any one or more of Examples 6-8 optionally includes: an arrhythmia analysis device that can be configured to determine the arrhythmia risk of a patient, and a mobile monitoring device that can be configured to adjust the data segment collection rate or schedule based on the arrhythmia risk.

[0017] In Example 10, the subject matter of Example 9 optionally includes: an arrhythmia analysis device that can be configured to use a physiological signal different from the cardiac signal sensed from the patient to determine the arrhythmia risk.

[0018] In Example 11, the subject matter of any one or more of Examples 9-10 optionally includes: an arrhythmia analysis device that can be configured to determine the arrhythmia risk based on the time of day during the monitoring period.

[0019] In Example 12, the subject matter of any one or more of Examples 9-11 optionally includes: an arrhythmia analysis device that can be configured to use the patient's arrhythmia history to determine the arrhythmia risk.

[0020] In Example 13, the subject matter of any one or more of Examples 1-12 optionally includes: an arrhythmia analysis device that can be configured to detect an arrhythmia including an atrial fibrillation (AF) episode from each intermittently collected data segment.

[0021] In Example 14, the subject matter of any one or more of Examples 1-13 optionally includes: a user interface coupled to the arrhythmia analysis device, the arrhythmia analysis device being configured to provide a notification to a user on the user interface regarding the detected arrhythmia.

[0022] In Example 15, the subject matter of Example 14 optionally includes: a user interface that can be configured to receive a user determination as to whether the detected arrhythmia is a true positive detection or a false positive detection, wherein the mobile monitoring device is configured to adjust one or more of the segment duration, the data segment collection rate, or the schedule in response to a true positive detection.

[0023] Example 16 is a method of monitoring a patient at risk of arrhythmia, the method including: sensing cardiac signals from the patient using a cardiac sensor; intermittently collecting data segments of the sensed cardiac signals during a monitoring period using a mobile monitoring device according to a data segment collection rate or schedule, each data segment having a segment duration; sending the intermittently collected data segments to an arrhythmia analysis device according to a transmission schedule or in response to a trigger event; and using the arrhythmia analysis device to detect an arrhythmia using the intermittently collected data segments received from the mobile monitoring device.

[0024] In Example 17, the subject matter of Example 16 optionally includes: determining a confidence level of the detection of the arrhythmia; and adjusting the segment duration, including increasing the segment duration in response to the determined confidence level falling below a confidence threshold; and maintaining or reducing the segment duration in response to the determined confidence level exceeding the confidence threshold.

[0025] In Example 18, the subject matter of any one or more of Examples 16-17 optionally includes: determining the instability of the detected arrhythmia; and adjusting the segment duration, including increasing the segment duration in response to the determined instability exceeding an instability threshold; and maintaining or reducing the segment duration in response to the determined instability falling below the instability threshold.

[0026] In Example 19, the subject matter of any one or more of Examples 16-18 optionally includes: wherein intermittently collecting data segments includes periodically collecting data segments with a data collection period longer than the segment duration.

[0027] In Example 20, the subject matter of any one or more of Examples 16 - 19 optionally includes: wherein the intermittent collection of data segments includes changing the data segment collection rate or schedule such that data segments are intermittently collected according to a first data segment collection rate or schedule during a first monitoring period and data segments are intermittently collected according to a second data segment collection rate or schedule different from the first data segment collection rate or schedule during a second monitoring period different from the first monitoring period.

[0028] In Example 21, the subject matter of Example 20 optionally includes: determining characteristics of the detected arrhythmia, including one or more of the duration or heart rate of the detected arrhythmia, wherein the data segment collection rate or schedule is changed according to the determined characteristics of the detected arrhythmia.

[0029] In Example 22, the subject matter of any one or more of Examples 20 - 21 optionally includes: receiving information about the patient's arrhythmia risk, wherein the data segment collection rate or schedule is changed according to the arrhythmia risk.

[0030] In Example 23, the subject matter of any one or more of Examples 16 - 22 optionally includes: receiving a user determination, including a true positive designation or a false positive designation of the detected arrhythmia; and adjusting one or more of the segment duration or the data segment collection rate or schedule in response to a true positive designation of the detected arrhythmia.

[0031] This summary is an overview of some teachings of this application and is not intended to be an exclusive or exhaustive treatment of the subject matter. Further details regarding the subject matter can be found in the detailed description and the appended claims. Other aspects of the disclosure will be apparent to those skilled in the art upon reading and understanding the following detailed description and viewing the drawings that form a part thereof, each of which should not be regarded as restrictive. The scope of the disclosure is defined by the appended claims and their legal equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] In the figures of the drawings, various embodiments are shown by way of example. Such embodiments are exemplary and are not intended to be exhaustive or exclusive embodiments of the subject matter.

[0033] Figure 1 An example of a patient management system and a portion of the environment in which the system can operate are generally shown.

[0034] Figures 2A - 2C An example of intermittent data segment collection according to a corresponding data collection rate or schedule is generally shown.

[0035] Figure 3Generally illustrated is an example of an arrhythmia detection system configured to monitor a patient at risk of arrhythmia.

[0036] Figure 4 Is a flowchart showing an example of a method of assessing a patient's arrhythmia risk using a system such as that shown in Figure 3 An example of a method of assessing a patient's arrhythmia risk using a system such as that shown in

[0037] Figure 5 Generally illustrated is a block diagram of an example machine on which any one or more of the techniques (e.g., methods) discussed herein may be performed. Detailed Description

[0038] Some AMDs, such as implantable cardiac monitors (ICMs), are capable of long-term recording of a patient's cardiac information, optionally along with other physiological data. The recorded data can be stored in the ICM and sent to an external computing device, such as a remote server, where a clinician can remotely monitor the patient's health status. The external computing device can additionally or alternatively analyze the data recorded by the ICM and sent from the ICM and detect or predict cardiac events, such as the progression of arrhythmia or heart failure. The external computing device typically has the resources and computing power sufficient to analyze large amounts of data. In some cases where real-time event detection is not required (e.g., non-fatal arrhythmias such as chronic AF, or deterioration of chronic heart failure), the external computing device can perform retrospective batch-mode analysis on the data sent from the ICM.

[0039] The ICM may include a data acquisition circuit to collect cardiac information, including, for example, an electrocardiogram or ECG, subcutaneous electrogram or EGM, or other physiological information. The cardiac or physiological information may be collected continuously or periodically. The data collected may be stored in on-board memory before being transmitted to an external computing device (such as a remote server) according to a schedule. In an example of monitoring chronic AF, the ICM may collect a large number of data segments (also known as data snippets) over a long period of time, where the duration of each data segment is long enough (e.g., 4 - 5 minutes) to allow the external computing device to reliably detect the presence of an AF event therefrom. However, since the ICM typically has limited battery life, memory capacity, and communication bandwidth, on-board collection, storage, and transmission of large amounts of data may be technically challenging. On the other hand, in the case of advanced computing technologies and computationally intensive and high-performance arrhythmia detection algorithms, the external computing device can reliably detect arrhythmias using shorter data segments without significantly reducing the detection sensitivity or specificity. For example, the external computing device can use machine learning-based algorithms to detect AF events from one-minute-long or even shorter segments instead of using 4-minute-long data segments. From the perspective of battery life and memory usage, the number of segments and the size (i.e., duration) of the segments that the ICM can collect are an engineering trade-off. For example, sampling and storing one segment of 4-minute-long ECG will consume approximately the same amount of battery power and occupy approximately the same amount of memory space as sampling and storing four 1-minute-long ECG segments, or eight 30-second-long ECG segments, or sixteen 15-second-long ECG segments, or thirty-two 7.5-second-long ECG segments, etc. If the external computing device can detect AF using shorter ECG segments with sufficient performance (e.g., sensitivity and specificity higher than an acceptable threshold, or false positive rate or false negative rate lower than an acceptable threshold), more segments can be collected and sent to the external computing device without additional battery power or on-board resource requirements. Since more segments are typically acquired during a longer monitoring period, the likelihood of capturing more AF events increases, especially in patients with chronic or persistent AF. For example, if the external computing device can detect AF from 30-second ECG segments with sufficient accuracy, eight such segments can be provided to the external computing device for analysis by the detection algorithm and review by a clinician, instead of just one 4-minute segment (which will consume approximately the same amount of battery power and resources of the ICM). Using an appropriate data segment collection schedule, eight segments can cover a monitoring period of more than four minutes and may detect more AF events.

[0040] The present inventor has recognized that there is an unmet need to optimize the data segment collection and transmission schedule based on the capabilities of an external computing device such that longer monitoring periods and potentially more arrhythmia events can be detected without significantly reducing battery life or depleting the on-board resources of the ICM. Systems, devices, and methods for detecting arrhythmias such as atrial fibrillation (AF) are disclosed herein. A medical device system includes a wearable monitoring device and an arrhythmia analysis device communicatively coupled to each other. The wearable monitoring device can sense cardiac signals from a patient and intermittently collect data segments of the sensed cardiac signals during a monitoring period according to a data segmentation collection rate or schedule. Each data segment has a specific segment duration. The monitoring device can send the intermittently collected data segments to the arrhythmia analysis device according to a transmission schedule or in response to a triggering event. The arrhythmia analysis device can detect arrhythmias from the intermittently collected data segments. The segment duration or the data segment collection rate or schedule can be adjusted based on performance metrics of the arrhythmia detection.

[0041] The systems, devices, and methods discussed in the present invention can improve medical techniques for device-based arrhythmia detection, particularly the detection of AF events. According to various embodiments, the intermittent data segment collection, storage, and transmission at the ICM and the optimized segment size (i.e., data segment duration) can fully utilize the computationally intensive and high-performance arrhythmia detection capabilities of an external computing device, facilitate the clinician's review of patient data over an extended monitoring period, and allow for potentially detecting more AF events without overusing the on-board resources and battery power of the ICM. As a result, medical resources can be better allocated to serve more patients, and the patient management costs of medical institutions can also be reduced.

[0042] Figure 1 An example patient management system 100 and a portion of the environment in which the patient management system 100 can operate are shown. The patient management system 100 can perform a series of activities, including remote patient monitoring and diagnosis of disease conditions. These activities can be performed near the patient 101, such as in the patient's home or office, via a centralized server, such as in a hospital, clinic, or doctor's office, or via a remote workstation, such as a secure wireless mobile computing device.

[0043] The patient management system 100 can include one or more mobile medical devices, an external system 105, and a communication link 111 that provides communication between the one or more mobile medical devices and the external system 105. The one or more mobile medical devices can include an implantable medical device (IMD) 102, a wearable medical device (WMD) 103, or one or more other implantable, leadless, subcutaneous, external, wearable, or mobile medical devices configured to monitor, sense, or detect information from the patient 101, determine physiological information about the patient 101, or provide one or more therapies to treat various conditions of the patient 101, such as one or more cardiac or non-cardiac conditions (e.g., dehydration, sleep apnea, etc.).

[0044] In an example, the IMD 102 can include one or more conventional cardiac rhythm management devices implanted in the patient's chest, the IMD 102 having a lead system including one or more transvenous, subcutaneous, or non-invasive leads or catheters to position one or more electrodes or other sensors (e.g., heart sound sensors) within, on, or around the patient 101's heart or at one or more other locations in the chest, abdomen, or neck. In another example, the IMD 102 can include, for example, a monitor subcutaneously implanted in the patient 101's chest, the IMD 102 including a housing containing circuitry and, in some examples, also including one or more sensors, such as a temperature sensor, etc.

[0045] The IMD 102 may include an evaluation circuit configured to detect or determine specific physiological information of the patient 101, or determine one or more conditions, or provide information or an alert to a user (such as the patient 101 (e.g., the patient), a clinician, or one or more other caregivers or processes). In an example, the IMD 102 may be an implantable cardiac monitor (ICM) configured to collect cardiac information from the patient, optionally along with other physiological information. The IMD 102 may alternatively or additionally be configured as a treatment device configured to treat one or more medical conditions of the patient 101. The treatment may be delivered to the patient 101 via a lead system and associated electrodes or using one or more other delivery mechanisms. The treatment may include delivering one or more drugs to the patient 101, such as using the IMD 102 or one or more other mobile medical devices, etc. In some examples, the treatment may include cardiac resynchronization therapy for correcting asynchrony in a heart failure patient and improving their cardiac function. In other examples, the IMD 102 may include a drug delivery system, such as a drug infusion pump, for delivering drugs to the patient to manage arrhythmias or complications caused by arrhythmias, hypertension, or one or more other physiological conditions. In other examples, the IMD 102 may include one or more electrodes configured to stimulate the patient's nervous system or provide stimulation to the muscles of the patient's airway, etc.

[0046] The WMD 103 may include one or more wearable or external medical sensors or devices (e.g., an automated external defibrillator (AED), a Holter monitor, a patch-based device, a smartwatch, a smart accessory, a wrist- or finger-worn medical device, such as a finger-based photoplethysmography sensor, etc.).

[0047] The external system 105 may include a dedicated hardware / software system, such as a programmer, a remote server-based patient management system, or alternatively a system defined primarily by software running on a standard personal computer. The external system 105 may manage patient 101 through the IMD 102 or one or more other mobile medical devices connected to the external system 105 via a communication link 111. In other examples, the IMD 102 may be connected to the WMD 103 via the communication link 111, or the WMD 103 may be connected to the external system 105 via the communication link 111. This may include, for example, programming the IMD 102 to perform one or more of acquiring physiological data, performing at least one self-diagnostic test (such as for device operating status), analyzing physiological data, or optionally delivering or adjusting therapy for patient 101. Additionally, the external system 105 may send information to or receive information from the IMD 102 or the WMD 103 via the communication link 111. Examples of information may include: real-time or stored physiological data from patient 101; diagnostic data, such as detection of patient hydration status, hospitalization, response to therapy delivered to patient 101, or device operating status of the IMD 102 or the WMD 103 (e.g., battery status, lead impedance, etc.). The communication link 111 may be an inductive telemetry link, a capacitive telemetry link, or a radio frequency (RF) telemetry link, or a wireless telemetry based on, for example, the "strong" Bluetooth or IEEE 802.11 wireless fidelity "Wi-Fi" interface standards. Other configurations and combinations of patient data source interfaces are also possible.

[0048] External system 105 may include external device 106 located near one or more mobile medical devices, and remote device 108 located at a location relatively far from the one or more mobile medical devices to communicate with external device 106 via communication network 107. Examples of external device 106 may include medical device programmers. Remote device 108 may be configured to evaluate collected patient or patient information and provide alert notifications and other possible functions. In an example, remote device 108 may include a centralized server acting as a central hub for data storage and analysis of the collected data. The server may be configured as a single, multi- or distributed computing and processing system. Remote device 108 may receive data from multiple patients. The data may be collected by one or more mobile medical devices and other data acquisition sensors or devices associated with patient 101. The server may include a memory device to store the data in a patient database. The server may include an alert analyzer circuit for evaluating the collected data to determine if specific alert conditions are met. Satisfaction of the alert conditions may trigger the generation of an alert notification, such as provided by one or more human-perceivable user interfaces. In some examples, the alert conditions may alternatively or additionally be evaluated by one or more mobile medical devices (such as implantable medical devices). By way of example, alert notifications may include web page updates, phone or pager calls, emails, SMS, text or "instant" messages, and messages to patients and direct notifications to emergency services and clinicians simultaneously. Other alert notifications are possible. The server may include an alert prioritizer circuit configured to prioritize alert notifications. For example, a similarity metric between physiological data associated with a detected physiological event and psychological data associated with historical alerts may be used to prioritize alerts for detected psychological events.

[0049] Remote device 108 may additionally include one or more locally configured clients or remote clients securely connected to the server via communication network 107. Examples of clients may include personal desktops, laptops, mobile devices, or other computing devices. System users, such as clinicians or other qualified medical professionals, may use the clients to securely access the stored patient data assembled in the database in the server and select and prioritize patients and alerts for healthcare provision. In addition to generating alert notifications, remote device 108 including the server and interconnected clients may also execute a follow-up protocol by sending follow-up requests to one or more mobile medical devices, or by sending messages or other communications to patient 101 (e.g., the patient), clinicians, or authorized third parties as compliance notifications.

[0050] The communication network 107 can provide wired or wireless interconnection. In an example, the communication network 107 can be based on Transmission Control Protocol / Internet Protocol (TCP / IP) network communication specifications, although other types or combinations of networking implementations are possible. Similarly, other network topologies and arrangements are possible.

[0051] One or more of the external device 106 or the remote device 108 can output detected physiological events to a system user, such as a patient or a clinician, or to a process that includes, for example, an instance of a computer program executable in a microprocessor. In an example, the process can include automatically generating a recommendation for an anti-arrhythmia treatment or a recommendation for further diagnostic testing or treatment. In an example, the external device 106 or the remote device 108 can include a respective display unit for displaying physiological or functional signals, or an alert, alarm, emergency call, or other form of warning to signal the detection of an arrhythmia. In some examples, the external system 105 can include an external data processor configured to analyze physiological or functional signals received by one or more ambulatory medical devices and confirm or reject the detection of an arrhythmia. Computationally intensive algorithms, such as machine learning algorithms, can be implemented in the external data processor to retrospectively process data to detect arrhythmias.

[0052] One or more portions of the ambulatory medical devices or the external system 105 can be implemented using hardware, software, firmware, or a combination thereof. One or more portions of the ambulatory medical devices or the external system 105 can be implemented using a dedicated circuit that can be constructed or configured to perform one or more functions, or can be implemented using a general-purpose circuit that can be programmed or otherwise configured to perform one or more functions. Such a general-purpose circuit can include a microprocessor or a portion thereof, a microcontroller or a portion thereof, or a programmable logic circuit, a memory circuit, a network interface, and various components for interconnecting these components. For example, among other things, a "comparator" can include an electronic circuit comparator that can be constructed to perform a specific function of comparing two signals, or the comparator can be implemented as part of a general-purpose circuit that can be driven by code instructing a portion of the general-purpose circuit to perform a comparison between two signals. A "sensor" can include an electronic circuit configured to receive information and provide an electronic output representative of such received information.

[0053] The treatment device 110 may be configured to send information to or receive information from one or more of the mobile medical devices or the external system 105 using the communication link 111. In an example, one or more mobile medical devices, external devices 106, or remote devices 108 may be configured to control one or more parameters of the treatment device 110. The external system 105 may allow programming of one or more mobile medical devices and may receive information about one or more signals obtained by one or more mobile medical devices, such as information that may be received via the communication link 111. The external system 105 may include a local external implantable medical device programmer. The external system 105 may include a remote patient management system, which may monitor patient status or adjust one or more treatments, for example, from a remote location.

[0054] Figures 2A - 2C An example of intermittent data segment collection according to a corresponding data collection schedule based on characteristics of the underlying heart rhythm, such as the duration of an AF episode, is generally shown. Data segments may be collected intermittently (rather than continuously) during a specified monitoring period, such as in a 24-hour period in the non-limiting example shown in Figures 2A - 2C In some examples, the monitoring period may be a pre-determined period during which the patient is more likely to experience arrhythmias (e.g., AF), which may be determined based on the patient's medical history, time of day, or indication from certain physiological sensors (e.g., activity sensors). The data segment collection schedule may include segment size (i.e., the duration of the data segments) and segment collection rate (e.g., the number of segments per unit time for periodic segment collection) or scheduled timing for initiating data segment collection (which may be non-periodic segment collection). Figure 2A An example of periodic collection of data segments 210A, 210B, …, 210N at a rate of one segment every 60 minutes during a 24-hour monitoring period is shown. The length of each segment is 10 seconds. To ensure intermittent rather than continuous data collection, the segment size (e.g., 10 seconds in this example) is set to be shorter than the time interval between at least two adjacent data segments. For periodic segment collection, the segment size is set to be shorter than the segment collection period (e.g., 60 minutes in this example). Periodic collection results in 24 segments during the 24-hour monitoring period. Such a data segment collection schedule will ensure capture of at least one 10-second “snapshot” of an AF episode that lasts 60 minutes or longer. For example, data segment 210B starting at 1:00 may capture a 10-second portion of a 60-minute long AF episode 212.

[0055] Figure 2B An example is shown in Figure 2AAnother example of the periodic collection of data segments 220A, 220B, …, 220N within the same 24-hour monitoring period shown in [reference]. Each segment is also 10 seconds long, but the segments are collected at a rate of one segment every 30 minutes, resulting in a total of 48 segments within the 24-hour monitoring period. The data segment collection schedule will ensure that at least one 10-second “snapshot” of an AF episode lasting 30 minutes or longer is captured. For example, data segment 220F starting at 2:30 can capture a 10-second portion of a 30-minute-long AF episode 222. Similarly, Figure 2C shows an example of the periodic collection of data segments 230A, 230B, …, 230N within the same 24-hour monitoring period as shown in Figure 2A and Figure 2B Ten-second segments are collected at a rate of one segment every 20 minutes, resulting in a total of 72 segments collected within the 24-hour monitoring period. The data segment collection schedule will ensure that at least one 10-second “snapshot” of an AF episode lasting 20 minutes or longer is captured. For example, data segment 230E starting at 1:20 can capture a 10-second portion of a 20-minute-long AF episode 232. Similarly, in an example where 10-second segments are collected at a rate of one segment every four minutes (such as to capture a “snapshot” of an AF episode lasting at least four minutes), a total of 360 segments can be collected within the 24-hour monitoring period; or in another example where 10-second segments are collected at a rate of one segment every two minutes (such as to capture a “snapshot” of an AF episode lasting at least two minutes), a total of 720 segments can be collected within the 24-hour monitoring period.

[0056] As described above, the data segment collection schedule includes the segment size and the segment collection rate (for periodic segment collection) or the timing for initiating data segment collection. In an example, the segment size (e.g., 10 seconds as shown in the above examples) can be determined based on the ability of an external computing device (such as one or more devices in external system 105) to detect the presence or absence of an AF event in the data segments with sufficient accuracy. In an example, the segment collection rate or the timing schedule can be determined based on characteristics of the AF episode (such as heart rate or duration). For periodic segment collection, as shown in Figures 2A - 2C setting the segment collection period to be equal to or shorter than the AF episode duration will ensure that at least a portion of the AF episode is captured. In various examples, the segment collection rate or the timing schedule can be determined based on an estimate of the AF episode duration, the patient's arrhythmia risk or history of arrhythmias, the time of day, or other physiological information. Examples of determining the data segment collection schedule are discussed below with reference to Figure 3

[0057] ​Intermittently collected data segments, such as 210A - 210N, 220A - 220N, or 230A - 230N, can be stored in the memory of the IMD 102 or the WMD 103. The stored data segments can be sent to an external computing device, such as one or more devices in the external system 105, for further analysis (e.g., to detect the presence of an AF event), or presented to a clinician. The stored data segments can be sent via a communication link according to a transmission schedule or in response to a trigger event. As discussed previously, optimizing the data segment size and the intermittent segment collection schedule as described herein according to various embodiments can better utilize the computationally intensive and high-performance arrhythmia detection capabilities of the external computing device, allowing the clinician to review the data over an extended monitoring period and potentially detect more AF events during the extended monitoring period without overusing the on-board resources and battery power of the ICM.

[0058] Although the discussion in this invention regarding intermittent data segment collection focuses on segments of cardiac signals for AF monitoring, the systems, devices, and techniques described herein can be applied to the intermittent data segment collection of other physiological information and used to detect other physiological events or conditions.

[0059] Figure 3Generally shown is an example of an arrhythmia detection system 300 configured to monitor a patient at risk of arrhythmia, such as atrial fibrillation (AF). Portions of system 300 may be included in a patient management system 100. System 300 may include a wearable monitoring device 310 and an arrhythmia analysis device 320. Wearable monitoring device 310 may be an example of IMD 102 or WMD 103. In an example, wearable monitoring device 310 may include a microprocessor circuit, which may be a dedicated processor, such as a digital signal processor, an application specific integrated circuit (ASIC), a microprocessor, or other type of processor for processing information including body activity information. Alternatively, the microprocessor circuit may be a general purpose processor that may receive and implement an instruction set for performing the functions, methods, or techniques described herein. In some examples, wearable monitoring device 310 may include a set of circuits that includes one or more other circuits or sub - circuits. These circuits may perform the functions, methods, or techniques described herein either individually or in combination. In an example, the hardware of the set of circuits may be fixedly designed to perform a particular operation (e.g., hard - wired). In an example, the hardware of the set of circuits may include physically - modifiable computer - readable media (e.g., magnetic, electrical, movable placement of immobile aggregating particles, etc.) with physically - connected components (e.g., execution units, transistors, simple circuits, etc.) that are variably connected to encode instructions for a particular operation. When connecting the physical components, the underlying electrical properties of the hardware components change, e.g., from insulator to conductor and vice versa. The instructions enable the embedded hardware (e.g., execution unit or loading mechanism) to create components of the set of circuits in the hardware via variable connections to perform a portion of a particular operation during operation. Thus, when the device operates, the computer - readable media is communicatively coupled to other components of the components of the set of circuits. In an example, any physical component may be used in more than one component of more than one set of circuits. For example, in operation, an execution unit may be used in a first circuit of a first set of circuits at one point in time and reused by a second circuit in the first set of circuits, or reused by a third circuit in a second set of circuits at a different time.

[0060] As Figure 3As shown, the ambulatory monitoring device 310 can include a sensor circuit 311, a data collection circuit 312, a memory 316, and a transceiver circuit 318. The sensor circuit 311 can include circuitry configured to sense physiological signals from a patient, such as via one or more implantable, wearable, or otherwise ambulatory sensors or electrodes associated with the patient. The sensors can be incorporated into or otherwise associated with an ambulatory medical device such as the IMD 102 or the WMD 103. In some examples, the sensors can be incorporated into an implantable cardiac monitor (ICM) device configured for subcutaneous implantation. Examples of physiological signals can include cardiac signals, including, for example, surface electrocardiogram (ECG), subcutaneous ECG, or intracardiac electrogram (EGM), thoracic or cardiac impedance signals, arterial pressure signals, pulmonary artery pressure signals, left atrial pressure signals, RV pressure signals, LV coronary pressure signals, coronary blood temperature signals, oxygen saturation signals, heart sound signals sensed by an ambulatory accelerometer or acoustic sensor, physiological responses to activity, apnea hypopnea index, one or more respiratory signals such as respiratory rate signals or tidal volume signals, brain natriuretic peptide (BNP), blood platelets, sodium and potassium levels, glucose levels, and other biomarker and biochemical markers, and the like. The sensor circuit 310 can include one or more sub-circuits to digitize, filter, or perform other signal conditioning operations on the received physiological signals.

[0061] The data collection circuit 312 can collect data segments of the physiological signals received from the sensor circuit 311. As described above with reference to FIG. 2, a data segment is a short segment of a sensed signal having a specific data length, also referred to as the segment size 313. The segment size 313 can be a programmable value that a user can set via a programmer device. The segment size 313 can be within a specific range, such as between approximately 10 and 60 seconds. By way of example and not limitation, the segment size can be set to 10 seconds, as Figures 2A - 2C shown.

[0062] In some examples, the data collection circuit 312 can determine the segment size 313 based on the ability of the arrhythmia analysis device 320 to detect arrhythmia events (e.g., AF events) from the data segments with sufficient accuracy. The detection performance of the arrhythmia analysis device 320 can be evaluated to determine whether a particular data segment size is sufficient. Examples of detection performance can include sensitivity, specificity, false positive rate, or false negative rate. If the detection performance of the arrhythmia analysis device 320 meets the criteria (e.g., the sensitivity and / or specificity exceeds the corresponding threshold, or the false positive rate and / or false negative rate is lower than the corresponding threshold), the segment size 313 can be decreased; otherwise, the segment size 313 can be maintained at its current value or increased to improve the arrhythmia detection performance of the arrhythmia analysis device 320.

[0063] In some examples, the data collection circuit 312 may adjust the segment size 313 based on the confidence in the arrhythmia detection performed by the arrhythmia analysis device 320. Generally, a larger segment size results in a higher detection confidence, and vice versa. On the other hand, a shorter segment size is generally preferred over a longer segment size, at least because, due to the engineering trade - offs for a given battery power, a longer segment size may correspond to a smaller number of segments being collected during the monitoring period, as described above. As such, the "optimal" segment size can be short but not significantly affect the confidence in arrhythmia detection. In examples where the arrhythmia detection is based on a comparison of a signal metric with a threshold, the detection confidence can be based on the deviation of the signal metric from the threshold. If the detection confidence is below the confidence threshold, the segment size 313 can be increased (e.g., from 10 seconds to 20 seconds). If the detection confidence exceeds the confidence threshold, the segment size 313 can be maintained at its current value or decreased.

[0064] In addition to or as an alternative to the arrhythmia detection performance or confidence described above, in some examples, the segment size 313 can also be adjusted based on the instability of the arrhythmia detected by the arrhythmia analysis device 320. This instability indicates the degree or rate of change of certain properties of the detected arrhythmia. If the arrhythmia instability exceeds the instability threshold, the segment size 313 can be increased (e.g., from 10 seconds to 20 seconds); if the arrhythmia instability drops below the instability threshold, the current segment size 313 can be maintained or decreased. For example, a substantial change (e.g., exceeding a threshold) in heart rate variability during AF may indicate an unstable AF or a new AF episode, and then the segment size can be extended to collect more data, which can ultimately be provided to the user (for reviewing the data segment) or the arrhythmia analysis device for further analysis of the data segment.

[0065] The data collection circuit 312 can intermittently collect data segments during a monitoring period such as the 24 - hour period shown in Figures 2A - 2C . The data collection circuit 312 can include a segment collection scheduler 314 that determines a schedule for collecting data segments from the sensed physiological signal. The segment collection schedule can be represented by the timing for initiating the collection of data segments. In an example of periodic segment collection, the segment collection schedule can be represented by the segment collection rate (e.g., N segments per unit time) or an equivalent segment collection period (e.g., one segment every X minutes). The segment collection rate or segment collection period can be a programmable value that can be set by the user via a programmer device. Figures 2A - 2CAn example is shown where the segment collection period X is set to 60, 30, or 20 minutes. By way of example and not limitation, the segment collection period X can range from about 2 minutes to 60 minutes. To ensure intermittent rather than continuous data collection, the time interval between at least two adjacent data segments, or the data collection period in the case of periodic segment collection, can be set to be longer than the segment size 313.

[0066] In various examples, the segment collection scheduler 314 can establish a variable data segment collection rate or variable timing schedule for collecting data segments during a monitoring period such that during a first monitoring period, data segments are intermittently collected at a first collection rate or schedule, and during a second monitoring period after the first monitoring period, data segments are intermittently collected at a second collection rate or schedule different from the first collection rate or schedule. In an example, the segment collection scheduler 314 can adjust the data segment collection rate or timing schedule based on characteristics of the arrhythmia detected by the arrhythmia analysis device 320. Examples of such characteristics can include heart rate or arrhythmia duration. In an example, the segment collection scheduler 314 can adjust the data segment collection rate or timing schedule based on a determination of an arrhythmia event (e.g., an AF event) detected by the arrhythmia analysis device 320. For example, a user (e.g., a clinician) can review the segment data and determine the AF detection as a true positive (TP) or false positive (FP) detection, and when determined as a TP detection, the segment collection scheduler 314 can increase the segment collection rate (or equivalently shorten the segment collection period). In another example, the segment collection scheduler 314 can adjust the data segment collection rate or timing schedule based on the risk that a patient has an arrhythmia. The arrhythmia risk can be determined using the patient's arrhythmia history, time of day, or physiological information such as sensed from a physiological sensor. The physiological sensor for determining the arrhythmia risk can be different from the sensor used to generate the cardiac signal from which the data segments are generated. The arrhythmia risk can be determined by the wearable monitoring device 310. Alternatively, the arrhythmia risk can be determined by the arrhythmia analysis device 320 and provided to the wearable monitoring device 310 for adjusting the collection rate or timing schedule.

[0067] Data segments collected by the data collection circuit 312 can be stored in the memory 316. The ambulatory monitoring device 310 can include a transceiver circuit 318 configured to send at least a portion of the stored data segments to the arrhythmia analysis device 320 via a communication link 111. The arrhythmia analysis device 320 can be an example of an external device 106 of the external system 105 or a remote device 108. In some examples, the transceiver circuit 318 can send data segments according to a transmission schedule (e.g., automatic periodic transmission) or in response to a trigger event (e.g., command transmission in response to a user command). By way of example and not limitation, the transmission schedule can include a daily transmission of all or at least a portion of the data segments collected during a 24-hour period. For example, the transceiver circuit 318 can send up to 24 segments collected according to the schedule shown in Figure 2A per day (i.e., one transmission session per day), up to 48 segments collected according to the schedule shown in Figure 2B per day, or up to 72 segments collected according to the schedule shown in Figure 2C per day, or other quantities of segments according to the corresponding segment collection rate or schedule (e.g., 360 segments corresponding to a rate of one segment every four minutes, or 720 segments corresponding to a rate of one segment every two minutes). In some examples, the transceiver circuit 318 can adjust the transmission schedule for sending data segments to the arrhythmia analysis device 320. For example, depending on the amount of segments collected daily, the transceiver circuit 318 can increase the transmission schedule from one transmission session per day to two transmission sessions per day, or decrease the transmission schedule from one transmission session per day to one transmission session every other day. In some examples, the adjustment of the transmission schedule can include a change in the time of day for data transmission.

[0068] The arrhythmia analysis device 320 includes an arrhythmia detector circuit 322 and a user interface 324. The arrhythmia detector circuit 320 can detect arrhythmias (such as AF) based on intermittently collected data segments received from the ambulatory monitoring device 310 using a reconfigurable, computationally intensive, and high-performance arrhythmia detection algorithm implemented therein. In an example, the arrhythmia detector circuit 320 can detect the presence of AF in each intermittently collected data segment. In an example, ventricular rate stability can be used to detect AF events. If the ventricular rate variability exceeds a threshold or is within a value range, the arrhythmia detector circuit 320 can detect an AF event. In some examples, the AF detector 234 can use the morphology of the cardiac signal to detect AF events. In another example, the arrhythmia detector circuit 320 can use a ventricular rate pattern of continuously decreasing ventricular rate to detect AF events. The ventricular rate pattern includes a pair of consecutive ventricular rate changes. Both ventricular rate changes are negative, referred to as a "doubledecrement" ventricular rate pattern. The doubledecrement ratio (which represents the prevalence of the doubledecrement ventricular rate pattern over a specified time period or multiple ventricular beats) can be calculated and used to detect AF. U.S. Patent Application No. 14 / 825669, entitled "ATRIAL FIBRILLATION DETECTION USING VENTRICULAR RATE VARIABILITY" by Krueger et al., refers to the doubledecrement pattern of ventricular heart rate and its use in the detection of atrial arrhythmias, the entire content of which is incorporated herein by reference. In yet another example, the arrhythmia detector circuit 320 can use ventricular rate clusters to detect AF events, where the ventricular rate clusters are represented by the statistical distribution or histogram of the ventricular rate or cycle length over multiple cardiac cycles. The ventricular rate cluster indicates the regularity of the ventricular rate of the cardiac cycle length. Patients with AF typically exhibit irregular ventricular contractions. However, premature atrial contractions (PACs) may occur at irregular intervals. When PACs conduct to the ventricles, they may produce an irregular ventricular rate, resulting in a ventricular cluster different from AF. As such, ventricular rate clusters can be used to distinguish frequent PACs and AF events. U.S. Patent Application No. 15 / 864953, entitled "ATRIAL FIBRILLATION DISCRIMINATION USING HEART RATE CLUSTERING" by Perschbacher et al., refers to the ventricular rate clusters of the histogram and their use in distinguishing AF and non-AF events, the entire content of which is incorporated herein by reference. In yet another example, the arrhythmia detector circuit 320 can use a metric representing the occurrence of various beat patterns representing cycle length or heart rate to detect AF events.In an example, the statistical measure includes an atrioventricular (AV) conduction block metric indicative of the presence or degree of conduction abnormality during sinus rhythm, such as a Wenkebach score representing the prevalence of Wenckebach block over a period of time. Examples of Wenkebach detectors can be based on repeatability metrics of various beat patterns of cycle length or heart rate, such as those discussed by Perschbacher et al. in U.S. Patent Application No. 15 / 786,824, titled "SYSTEMS AND METHODS FOR ARRHYTHMIA DETECTION", the entire content of which is incorporated herein by reference.

[0069] In some examples, the arrhythmia detector circuit 320 can determine a confidence level for arrhythmia detection, such as an AF confidence level. The confidence level can have a categorical value or a numerical value. In an example, the AF confidence score can be determined as a function of the atrial peak intensity within an atrial detection window. In an example, the AF confidence score is inversely proportional to the atrial peak intensity, such that an AF event with a lower atrial peak intensity in an ensemble-averaged cardiac electrical signal can be assigned a higher AF confidence score. In another example, the AF confidence score can be determined as a function of the signal power of a portion of the ensemble-averaged cardiac electrical signal within the atrial detection window.

[0070] As described above, the performance of AF detection (e.g., sensitivity and specificity, or false positive rate and false negative rate) and / or the AF confidence indication can be provided as feedback via the communication link 111 to the mobile monitoring device 10 to adjust the segment size and / or the timing schedule or data segment collection rate for collecting data segments.

[0071] The user interface 324 may include an input device and an output device. The input device may receive user programming inputs to the ambulatory monitoring device 310, such as segment sizes and parameters related to a segment collection schedule, and / or user programming inputs for arrhythmia detection at the arrhythmia analysis device 320. The input device may include a keyboard, a screen keyboard, a mouse, a trackball, a touchpad, a touch screen, or other pointing or navigation devices. The input device may enable a system user to program parameters for sensing physiological signals, detecting arrhythmias, generating alerts, and the like. The output device may generate a human-perceivable presentation of detected arrhythmia events, such as AF events. The output device may include a display for displaying sensed physiological information, intermediate measurements or calculations, detected AF events, or AF confidence indicators for AF events, and the like. The output unit may include a printer for printing a hard copy of the detection information. The information may be presented in a table, chart, schematic, or any other type of textual, tabular, or graphical presentation format. The presentation of the output information may include an audio or other media format to alert the system user of the detected arrhythmia event. In an example, the output device may generate an alert, alarm, emergency call, or other form of warning to signal the system user of the detected arrhythmia event.

[0072] In some examples, the system 300 may include a therapy unit that may deliver an anti-arrhythmia therapy to a patient in response to the detection of an arrhythmia. The therapy unit may be included in the ambulatory monitoring device 310; or alternatively in a separate therapy system. Examples of therapies may include electrostimulation therapy, cardioversion therapy, defibrillation therapy delivered to the heart, nerve tissue, other target tissues, or drug therapy including delivering a drug to a tissue or organ. In some examples, an existing therapy or treatment plan may be modified to treat the detected arrhythmia, such as modifying a patient follow-up schedule, or adjusting stimulation parameters or drug dosages.

[0073] Figure 4 is a flowchart illustrating an example of a method 400 for monitoring a patient at risk of arrhythmia, such as atrial fibrillation (AF), using data segments (or data fragments) intermittently collected from the patient. The method 400 may be implemented and executed in an ambulatory medical device such as the IMD 102 or the WMD 103, or in an external system 105. In an example, the method 400 may be implemented and executed in the arrhythmia detection system 300.

[0074] Method 400 begins at 410, where one or more implantable, wearable, or otherwise mobile sensors or electrodes included in or associated with IMD 102, WMD 103, or mobile monitoring device 310 may sense physiological signals from a patient. Alternatively, physiological signals may be retrieved from a storage device (e.g., an electronic medical record system) that stores physiological signals recorded from the patient. In an example, the physiological signals may include cardiac electrical signals such as an ECG or an intracardiac EGM. In some examples, the physiological signals may include thoracic or cardiac impedance signals, arterial pressure signals, pulmonary artery pressure signals, left atrial pressure signals, RV pressure signals, LV coronary pressure signals, heart sounds or endocardial acceleration signals, physiological responses to activity, apnea hypopnea index, one or more respiratory signals such as a respiratory rate signal or a tidal volume signal, and so on. The sensed physiological signals may be preprocessed, including one or more of signal amplification, digitization, filtering, or other signal conditioning operations.

[0075] At 420, data segments of the sensed cardiac signals may be intermittently collected during a monitoring period according to a data segment collection rate or schedule, such as using data collection circuit 312. The intermittently collected data may be in the form of data segments or fragments having a specific data length (also referred to as a segment size). The segment size may be a programmable value that may be set by a user via a programmer device. As described above with reference to Figure 3 described, the segment size may be determined based on how accurately an arrhythmia analysis device (such as arrhythmia analysis device 320 or an offline arrhythmia analyzer in external system 105) may detect the presence or absence of a target arrhythmia event (e.g., an AF event). Additionally or alternatively, the segment size may be determined based on the confidence of arrhythmia detection. In some examples, the segment size may be determined based on the instability of the arrhythmia, which represents the degree or rate of change of certain properties of the detected arrhythmia.

[0076] In an example, the intermittent collection of data segments may include periodic segment collection at a specific data (segment) collection rate, which is represented by the number of segments to be collected per unit time. The periodic segment collection may alternatively be represented by a segment collection period (i.e., one segment every X minutes). Figures 2A - 2C Examples are shown where the segment collection period is set to 60, 30, or 20 minutes. In some examples, the intermittent collection may include an aperiodic collection schedule. To ensure intermittent rather than continuous data collection, the time interval between at least two adjacent data segments or the data collection period in the case of periodic segment collection may be set to be longer than the segment size.

[0077] In various examples, intermittent data segment collection can be performed at a variable data segment collection rate or variable timing schedule for collecting data segments during a monitoring period, such that data segments are collected during a first monitoring period according to a first data segment collection rate or schedule; and during a second monitoring period different from the first monitoring period, data segments are collected according to a second data segment collection rate or schedule different from the first data segment collection rate or schedule. Adjustment of the segment collection rate or timing schedule for collecting data segments can be based on characteristics of the arrhythmia, such as heart rate, arrhythmia duration, etc. In an example, the data segment collection rate or timing schedule can be adjusted based on an arrhythmia determination of an arrhythmia episode, such that, for example, the segment collection rate can be increased in response to a true positive (TP) determination of an arrhythmia. In another example, the data segment collection rate or timing schedule can be adjusted based on a patient's risk of having an arrhythmia, which can be determined using one or more of the patient's arrhythmia history, time of day, or physiological information sensed from a physiological sensor.

[0078] At 430, the intermittently collected data segments can be sent to an arrhythmia analysis device for further analysis and / or expert review or determination. The transmission of the data segments can follow a transmission schedule or in response to a trigger event.

[0079] At 440, the arrhythmia analysis device can analyze the intermittently collected data segments to detect a specific type of arrhythmia, such as AF. Reconfigurable, computationally intensive, and high-performance arrhythmia detection algorithms can be used to detect the presence of an arrhythmia (e.g., AF) from each intermittently collected data segment. In some examples, a confidence indicator can be determined for the detected arrhythmia episode.

[0080] Based on the arrhythmia detection performance at 440, one or more of the segment size, data segment collection rate, or timing schedule can be adjusted. For example, the performance of AF detection at 440 can be calculated, including sensitivity and specificity, or false positive rate and false negative rate. Such performance metrics and / or the confidence indicator associated with the detected arrhythmia episode can be used as feedback for adjusting the segment size and / or the timing schedule or data segment collection rate for collecting data segments.

[0081] At 450, information about the detected arrhythmia can be presented to a user or process. An alert notification about the detected arrhythmia can be presented to the user on a user interface. In an example, an arrhythmia determination can be received from the user. In some examples, a treatment can be delivered to the patient in response to the detection of an arrhythmia, or an existing therapy or treatment plan can be modified to treat the detected arrhythmia.

[0082] Figure 5 FIG. 500 generally shows a block diagram of an example machine on which any one or more of the techniques (e.g., methods) discussed herein may be performed. Portions of this description may apply to the computing framework of various parts of patient management system 100 or arrhythmia detection system 300.

[0083] In alternative embodiments, machine 500 may operate as a stand-alone device or may be connected (e.g., networked) to other machines. In a networked deployment, machine 500 may operate in a server-client network environment as a server machine, a client machine, or both. In an example, machine 500 may act as a peer machine in a peer-to-peer (P2P) (or other distributed) network environment. Machine 500 may be a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), mobile phone, network device, network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) specifying actions to be taken by that machine. Further, although only a single machine is shown, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute one (or more) sets of instructions to perform any one or more of the methods discussed herein, such as cloud computing, software as a service (SaaS), other computer cluster configurations.

[0084] As described herein, an example may include logic or a number of components or mechanisms, or may be operated thereby. A circuit set is a collection of circuits implemented in a tangible entity that includes hardware (e.g., simple circuits, gates, logic, etc.). Circuit set components may be flexible over time and underlying hardware variability. A circuit set includes components that may individually or in combination perform specified operations when operating. In an example, the hardware of a circuit set may be immutably designed to perform a particular operation (e.g., hard-wired). In an example, the hardware of a circuit set may include physically modifiable computer-readable media (e.g., magnetic, electrical, movable placement of invariable aggregating particles, etc.) including physically modifiable components (e.g., execution units, transistors, simple circuits, etc.) to encode instructions for a particular operation. When connecting the physical components, the underlying electrical properties of the hardware composition change, e.g., from insulator to conductor and vice versa. Instructions enable the embedded hardware (e.g., execution unit or loading mechanism) to create components of a circuit set in the hardware via variable connections to perform a part of a particular operation when operating. Thus, when the device operates, the computer-readable media is communicatively coupled to other components of the circuit set components. In an example, any physical component may be used in more than one component of more than one circuit set. For example, under operation, an execution unit may be used in a first circuit of a first circuit set at one point in time and reused by a second circuit in the first circuit set, or reused by a third circuit in a second circuit set at a different time.

[0085] A machine (e.g., a computer system) 500 can include a hardware processor 502 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 504, and a static memory 506, some or all of which may communicate with each other via an interconnect (e.g., a bus) 508. The machine 500 can also include a display unit 510 (e.g., a raster display, a vector display, a holographic display, etc.), an alphanumeric input device 512 (e.g., a keyboard), and a user interface (UI) navigation device 514 (e.g., a mouse). In an example, the display unit 510, the input device 512, and the UI navigation device 514 can be a touchscreen display. The machine 500 can additionally include a storage device (e.g., a drive unit) 516, a signal generation device 518 (e.g., a speaker), a network interface device 520, and one or more sensors 521, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or other sensors. The machine 500 can include an output controller 528, such as a serial (e.g., universal serial bus (USB)), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate with or control one or more peripheral devices (e.g., a printer, a card reader, etc.).

[0086] The storage device 516 can include a machine-readable medium 522 having stored thereon a set or sets of data structures or instructions 524 (e.g., software) embodying any one or more of the techniques or functions described herein or used thereby. During execution of the instructions 524 by the machine 500, the instructions 524 can also reside, completely or at least partially, within the main memory 504, the static memory 506, or the hardware processor 502. In an example, one or any combination of the hardware processor 502, the main memory 504, the static memory 506, or the storage device 516 can constitute a machine-readable medium.

[0087] Although the machine-readable medium 522 is shown as a single medium, the term "machine-readable medium" can include a single medium or multiple media (e.g., a centralized or distributed database and / or associated caches and servers) configured to store one or more instructions 524.

[0088] The term "machine-readable medium" can include any medium that can store, encode, or carry instructions executable by a machine 500 and cause the machine 500 to perform any one or more of the techniques of the present disclosure, or any medium that can store, encode, or carry data structures used by or associated with these instructions. Non-limiting examples of machine-readable media can include solid-state memory and optical and magnetic media. In an example, an aggregated machine-readable medium includes a machine-readable medium having a plurality of particles with invariant (e.g., stationary) mass. Thus, an aggregated computer-readable medium is not a transient propagated signal. Specific examples of aggregated machine-readable media may include: non-volatile memory such as semiconductor storage devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROM disks.

[0089] Instructions 524 can also be sent or received over a communication network 526 via a network interface device 520 using any one of a variety of transmission protocols (e.g., Frame Relay, Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), etc.). Example communication networks can include local area networks (LANs), wide area networks (WANs), packet data networks (e.g., the Internet), mobile telephone networks (e.g., cellular networks), plain old telephone (POTS) networks, and wireless data networks (e.g., the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standard family known as Wi-Fi, the IEEE 802.16 standard family known as WiMAX), the IEEE 802.15.4 standard family, peer-to-peer (P2P) networks, and so on. In an example, the network interface device 520 can include one or more physical jacks (e.g., Ethernet, coaxial cable, or phone jacks) or one or more antennas to connect to the communication network 526. In an example, the network interface device 520 can include multiple antennas to perform wireless communication using at least one of single-input multiple-output (SIMO), multiple-input multiple-input (MIMO), or multiple-input single-output (MISO) techniques. The term "transmission medium" shall be considered to include any intangible medium that can store, encode, or carry instructions executable by a machine 500 and includes digital or analog communication signals or other intangible media to facilitate the communication of such software.

[0090] Various embodiments are shown in the above figures. One or more features from one or more of these embodiments can be combined to form other embodiments.

[0091] The method examples described herein can be at least partially machine or computer-implemented. Some examples can include a computer-readable medium or machine-readable medium encoded with instructions that are operable to configure an electronic device or system to perform the methods described in the above examples. Implementations of such methods can include code, such as microcode, assembly language code, high-level language code, or the like. Such code can include computer-readable instructions for performing various methods. The code can form part of a computer program product. Additionally, during execution or at other times, the code can be tangibly stored on one or more volatile or non-volatile computer-readable media.

[0092] The foregoing detailed description is intended to be illustrative and not restrictive. Accordingly, the scope of the present disclosure should be determined with reference to the appended claims and the full scope of equivalents to which those claims are entitled.

Claims

1. A system for monitoring a patient at risk of arrhythmia, the system comprising: A wearable monitoring device configured to: Sense cardiac signals from the patient via a cardiac sensor; And Intermittently collect data segments of the sensed cardiac signals according to a data segment collection rate or schedule during a monitoring period, each of the data segments having a segment duration; And An arrhythmia analysis device communicatively coupled to the wearable monitoring device, the arrhythmia analysis device being configured to detect arrhythmia using the intermittently collected data segments received from the wearable monitoring device, Wherein the wearable monitoring device is configured to send the intermittently collected data segments to the arrhythmia analysis device according to a transmission schedule or in response to a trigger event.

2. The system according to claim 1, wherein: The arrhythmia analysis device is configured to determine a confidence level of the detection of the arrhythmia; And The wearable monitoring device is configured to adjust the segment duration to include: increasing the segment duration in response to the determined confidence level dropping below a confidence threshold, and maintaining or reducing the segment duration in response to the determined confidence level exceeding the confidence threshold.

3. The system according to any one of claims 1-2, wherein: The arrhythmia analysis device is configured to determine the instability of the detected arrhythmia; And The wearable monitoring device is configured to adjust the segment duration to include: increasing the segment duration in response to the determined instability exceeding an instability threshold, and maintaining or reducing the segment duration in response to the determined instability dropping below the instability threshold.

4. The system according to any one of claims 1-3, wherein the wearable monitoring device is configured to: periodically collect the data segments in a data collection period longer than the segment duration.

5. The system according to any one of claims 1-4, wherein the wearable monitoring device is configured to: intermittently collect the data segments according to a data collection schedule such that the time interval between at least two adjacent collections is longer than the segment duration.

6. The system according to any one of claims 1-5, wherein the wearable monitoring device is configured to adjust the data segment collection rate or schedule to include: Intermittently collect the data segments according to a first data segment collection rate or schedule during a first monitoring period; And Intermittently collect the data segments according to a second data segment collection rate or schedule different from the first data segment collection rate or schedule during a second monitoring period.

7. The system according to claim 6, wherein the arrhythmia analysis device is configured to determine the characteristics of the detected arrhythmia, Wherein the wearable monitoring device is configured to: adjust the data segment collection rate or schedule based on the determined characteristics of the detected arrhythmia.

8. The system according to claim 7, wherein the characteristics of the detected arrhythmia include: One or more of the duration or heart rate of the detected arrhythmia.

9. The system according to any one of claims 6 - 8, wherein: the arrhythmia analysis device is configured to: determine the arrhythmia risk of the patient; and the mobile monitoring device is configured to: adjust the data segment collection rate or schedule based on the arrhythmia risk.

10. The system according to claim 9, wherein the arrhythmia analysis device is configured to: use a physiological signal different from the cardiac signal sensed from the patient to determine the arrhythmia risk.

11. The system according to any one of claims 9 - 10, wherein the arrhythmia analysis device is configured to: determine the arrhythmia risk based on the time of day during the monitoring period.

12. The system according to any one of claims 9 - 11, wherein the arrhythmia analysis device is configured to: use the patient's arrhythmia history to determine the arrhythmia risk.

13. The system according to any one of claims 1 - 12, wherein the arrhythmia analysis device is configured to: detect an arrhythmia including an atrial fibrillation (AF) episode from each of the intermittently collected data segments.

14. The system according to any one of claims 1 - 13, comprising a user interface coupled to the arrhythmia analysis device, the arrhythmia analysis device being configured to provide a notification to the user on the user interface regarding the detected arrhythmia.

15. The system according to claim 14, wherein the user interface is configured to: receive a user determination as to whether the detected arrhythmia is a true positive detection or a false positive detection, wherein the mobile monitoring device is configured to: adjust one or more of the segment duration or the data segment collection rate or schedule in response to the true positive detection.

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