阅读说明：本技术 用于呈现生理数据的系统和方法 (System and method for presenting physiological data ) 是由 大卫·L·佩什巴赫 苏尼帕·萨哈 迪帕·马哈詹 德里克·D·博恩 于 2019-07-23 设计创作，主要内容包括：讨论了用于向用户呈现生理数据的系统和方法。一种示例性系统包括呈现控制电路,该呈现控制电路被配置为根据对应于设备检测的生理事件的存在的生理信号的数据子集生成信号度量。信号度量表示生理事件的特性。呈现控制电路可以从多个数据子集确定具有临床意义的目标子集,该目标子集具有满足特定条件的相对应的信号度量。呈现控制电路可以优先于生理数据的其他子集呈现识别的目标子集。用户可以判断设备检测的生理事件,或者调整设备参数。(Systems and methods for presenting physiological data to a user are discussed. An exemplary system includes a presentation control circuit configured to generate a signal metric from a data subset of a physiological signal corresponding to a presence of a physiological event detected by a device. The signal metric represents a characteristic of the physiological event. The presentation control circuitry may determine a clinically significant target subset from the plurality of data subsets, the target subset having a corresponding signal metric that satisfies a particular condition. The presentation control circuitry may present the identified target subset in preference to other subsets of the physiological data. The user may determine a physiological event detected by the device or adjust a parameter of the device.)

1. A system for presenting physiological data to a user, comprising:

a presentation control circuit configured to:

generating signal metrics for data subsets of physiological signals corresponding to the presence of physiological events detected by a device, respectively, the signal metrics being representative of characteristics of the physiological events;

determining from the data subset of physiological signals a target subset having corresponding generated signal metrics that satisfy a particular condition; and

presenting the determined target subset of the physiological signals to the user.

2. The system of claim 1, comprising a user interface configured to:

displaying the target subset before other data subsets of the physiological signal; and

a determination is received of a presence of a physiological event detected by the device by a user.

3. The system of any of claims 1-2, wherein the physiological signal is recorded in response to a device-detected presence of an arrhythmia episode, and the signal metric represents a cardiac timing or signal morphology of the recorded physiological signal.

4. The system of any of claims 3, wherein the physiological signal is recorded in response to a device detecting the presence of an atrial tachyarrhythmia episode, and the signal metric comprises ventricular Heart Rate Variability (HRV).

5. The system of any of claims 1-2, wherein the physiological signal is recorded in response to a device detecting a syncope or a pre-syncope presence, and the signal metric comprises a cardiac arrest metric.

6. The system of claim 4, wherein the presentation control circuitry is configured to:

detecting an unstable heartbeat from each of the data subsets of physiological signals based on the HRV;

determining a respective count of the detected unstable heartbeats for the subset of data; and

determining the target subset using the count of unstable heartbeats determined for the data subset.

7. The system of claim 6, wherein the presentation control circuitry is configured to detect unstable heartbeats when beat-to-beat changes in ventricular heart rate exceed a beat stability threshold or fall within a beat stability range.

8. The system of claim 7, wherein the presentation control circuitry is configured to detect an unstable heartbeat further when a heart rate of the subset of data is within a range of heart rates.

9. The system of claim 6, wherein the data subset of the physiological signal includes a beat window containing a specified number of heartbeats, and the presentation control circuit is configured to:

detecting one or more unstable windows from the beat windows in which corresponding counts of unstable beats exceed an unstable beat count threshold, and

determining the subset of targets using the detected one or more instability windows.

10. The system according to claim 9, wherein the presentation control circuitry is configured to determine a target subset of unstable windows corresponding to unstable beats from among the unstable beat windows that contain a maximum count of unstable beats.

11. The system according to any one of claims 9 to 10, wherein the presentation control circuitry is configured to determine a target subset corresponding to an unstable window detected first in time among the unstable beat windows.

12. The system of any of claims 9 to 11, wherein the presentation control circuitry is configured to determine a target subset corresponding to a threshold number of instability windows that cause the sequence of consecutive instability windows to exceed the instability window.

13. The system of claim 12, wherein the presentation control circuitry is configured to detect a longest sequence of consecutive instability windows from the two or more sequences of consecutive instability windows and determine a target subset of instability windows that corresponds to the longest sequence that resulted in consecutive instability windows.

14. The system of any of claims 6 to 13, wherein the presentation control circuitry is further configured to:

smoothing a trend of unstable heartbeat counts over the beat window, an

Detecting one or more unstable windows each having a corresponding unstable heartbeat count that exceeds the unstable beat count threshold, as determined from the smoothed trend.

15. The system according to claim 14, wherein the presentation control circuitry is configured to smooth a trend of counts of unstable heartbeats using a moving average of the trend of unstable heartbeat counts over the beat window.

Technical Field

This document relates generally to medical devices and, more particularly, to systems, devices and methods for presenting physiological data to a user.

Background

Implantable Medical Devices (IMDs) have been used to monitor patient health or disease states and deliver therapy. For example, implantable cardioverter-defibrillator (ICD) devices are used to monitor certain abnormal heart rhythms. Some IMDs may be used to monitor the progression of chronic diseases, such as deterioration of cardiac performance due to Congestive Heart Failure (CHF). In addition to diagnostic capabilities, IMDs may also provide therapies for treating or alleviating certain medical conditions, such as electrical cardiac stimulation therapies for treating cardiac arrhythmias or correcting cardiac dyssynchrony in CHF patients.

The IMD may record medical data associated with the detected physiological event, such as an arrhythmia or Worsening Heart Failure (WHF). Some IMDs may register patient-triggered episodes of physiological events and record physiological data in response to patient triggers. The IMD may be interconnected with the patient management system via a data communication network. Device data (such as medical data associated with the detected physiological event) may be transmitted to a patient management system, through which a healthcare professional may remotely track the patient or periodically assess the functionality of the IMD. For example, the healthcare provider may review recorded medical data associated with the onset of a physiological event, determine the existence or likely cause of the physiological event, or assess whether a prescribed therapy has produced a desired therapeutic outcome.

Disclosure of Invention

The patient management system may manage the onset of physiological events from multiple AMDs simultaneously. Manual review (e.g., by a clinician) and annotation of physiological event data (such as arrhythmic episodes) recorded by a large number of devices may require a large amount of clinical, technical, and human resources, which may be expensive or time-consuming for a healthcare facility. For example, the episodes of physiological events recorded by some devices may contain large amounts of data, including multiple data channels such as data recorded from various electrodes or sensors. The data recorded in some data channels may be as long as 10 to 30 minutes. It is generally desirable to reduce the amount of data to be presented to a clinician without missing or distorting important information that may be necessary for the clinician to make his/her clinical judgment about the patient's condition, the efficacy of a diagnostic or therapeutic outcome, or an assessment of system or device function.

The physiological event episode is typically recorded in response to some triggering event, such as a physiological trigger (e.g., when a physiological parameter satisfies a predetermined condition), a patient trigger (e.g., the patient voluntarily activates the data recording), or a time trigger (e.g., data recorded periodically or at a particular time of day). Depending on the nature of the triggering event, some device-recorded episodes may not contain an "event of interest". For example, a patient-triggered or physiologic sensor-triggered episode may be a False Positive (FP) event, meaning that a target event (e.g., a particular type of arrhythmia) has not occurred during the recording time. On the other hand, for episodes recorded by some devices that contain an event of interest, the portion of data corresponding to the event of interest may not be readily identified. For example, for arrhythmic events, the data portions of interest may include the beginning, termination, or segments (e.g., changes in heart rate or characteristic signal morphology) that have significant diagnostic value. Conventionally, to identify events of interest and portions of data of interest from episode data recorded by a device, a user would scroll through a large number of episodes recorded by the device. This can be time consuming and requires a great deal of effort and resources. For at least the reasons set forth above, the present inventors have recognized an unmet need for systems and methods for more efficiently presenting clinically meaningful physiological data so as to save time, effort, resources, and costs associated with data review.

This document discusses, among other things, systems, devices, and methods for presenting physiological data to a user. An embodiment of the system includes a presentation control circuit configured to evaluate signal metrics of a plurality of data subsets of the physiological signal corresponding to the presence of the physiological event detected by the device, respectively. The signal metric represents a characteristic of a physiological event, such as an arrhythmia episode. The presentation control circuitry may identify a clinically significant target subset from the plurality of data subsets, the target subset having a corresponding signal metric that satisfies a particular condition. The presentation control circuitry may present the identified subset of targets to a user for determining a physiological event detected by the device or adjusting a parameter of the device. In some examples, the presentation control circuitry may present the identified target subset to the user in preference to other subsets of the physiological data.

Example 1 is a system for presenting physiological data to a user. The system includes a presentation control circuit configured to: generating signal metrics for the subsets of data of the physiological signal corresponding to the presence of the physiological event detected by the device, respectively, the signal metrics being representative of characteristics of the physiological event; determining from the data subsets of physiological signals a target subset having corresponding generated signal metrics that satisfy a particular condition; and presenting the determined target subset of physiological signals to the user.

In example 2, the subject matter of example 1 optionally includes a user interface configured to display the target subset before the other data subsets of the physiological signal and receive a user determination of the presence of a device-detected physiological event.

In example 3, the subject matter of any one or more of examples 1-2 optionally includes a recorded physiological signal in response to the device detecting the presence of an arrhythmia episode, and a signal metric representing a cardiac timing or signal morphology of the recorded physiological signal.

In example 4, the subject matter of example 3 optionally includes the physiological signal recorded in response to the device detecting the presence of an atrial tachyarrhythmia episode, and the signal metric may include ventricular Heart Rate Variability (HRV).

In example 5, the subject matter of any one or more of examples 1-2 optionally includes a physiological signal recorded in response to a device detecting a syncope or a pre-syncope presence, and a signal metric that may include a cardiac arrest metric.

In example 6, the subject matter of example 4 optionally includes the presentation control circuitry configured to detect unstable heartbeats from each of the data subsets of the physiological signal based on the HRV, determine respective counts of the detected unstable heartbeats for the data subsets, and determine the target subset using the counts of the unstable heartbeats determined for the data subsets.

In example 7, the subject matter of example 6 optionally includes the presentation control circuitry configured to detect an unstable heartbeat when the beat-to-beat change in ventricular heart rate exceeds a beat stability threshold or falls within a beat stability range.

In example 8, the subject matter of example 7 optionally includes the presentation control circuitry configured to detect an unstable heartbeat further when the heart rate of the subset of data is within the heart rate range.

In example 9, the subject matter of example 6 optionally includes a data subset of the physiological signal, which may include a beat window including a specified number of heartbeats. The rendering control circuit is configured to detect one or more unstable windows from the beat windows having corresponding counts of unstable beats that exceed the unstable beat count threshold, and to determine the target subset using the detected one or more unstable windows.

In example 10, the subject matter of example 9 optionally includes the presentation control circuitry configured to determine a target subset corresponding to an unstable window containing a maximum count of unstable beats from among the unstable beat windows.

In example 11, the subject matter of any one or more of examples 9 to 10 optionally includes the presentation control circuitry configured to determine the target subset corresponding to the unstable window detected first in time among the unstable beat windows.

In example 12, the subject matter of any one or more of examples 9 to 11 optionally includes the presentation control circuitry configured to determine the target subset corresponding to the number of instability windows that caused the sequence of consecutive instability windows to exceed the threshold number of instability windows.

In example 13, the subject matter of example 12 optionally includes the presentation control circuitry configured to detect a longest sequence of consecutive unstable windows from the two or more sequences of consecutive unstable windows and determine a target subset of unstable windows corresponding to the longest sequence that resulted in the consecutive unstable windows.

In example 14, the subject matter of any one or more of examples 6-13 optionally includes the presentation control circuitry being further configured to smooth a trend of the unstable heartbeat counts over the beat window, and detect the one or more unstable windows each having a corresponding unstable heartbeat count exceeding the unstable beat count threshold determined from the smoothed trend.

In example 15, the subject matter of example 14 optionally includes the presentation control circuitry configured to smooth the trend of the count of unstable heartbeats using a moving average of the trend of the unstable heartbeat count over the beat window.

Example 16 is a method for presenting physiological data to a user on a user interface. The method comprises the following steps: generating signal metrics for the subsets of data of the physiological signal corresponding to the presence of the physiological event detected by the device, respectively, the signal metrics being representative of characteristics of the physiological event; determining from the data subsets of physiological signals a target subset having corresponding generated signal metrics that satisfy a particular condition; and presenting the determined target subset of physiological signals to the user.

In example 17, the subject matter of example 16 optionally includes displaying the target subset on a user interface before other data subsets of the physiological signal, and receiving a user determination of the presence of the device-detected physiological event.

In example 18, the subject matter of any one or more of examples 16 to 17 optionally includes the data subset may include a beat window having a particular number of beats and taken from a physiological signal recorded in response to the device detecting the presence of an atrial tachyarrhythmia episode. The step of determining the subset of targets may comprise: detecting an unstable heartbeat from each of the beat windows based on a signal metric of ventricular Heart Rate Variability (HRV); detecting one or more unstable windows from the beat window based on the unstable beat counts in the beat window; and determining a subset of targets using the detected one or more instability windows.

In example 19, the subject matter of example 18 can optionally include determining a target subset, which can include identifying an unstable window containing a least stable heartbeat from among the unstable beat windows.

In example 20, the subject matter of any one or more of examples 16 to 19 optionally includes determining a target subset, which may include identifying an unstable window detected first in time among the unstable beat windows.

In example 21, the subject matter of any one or more of examples 16 to 20 optionally includes determining a subset of targets, which may include identifying a threshold number of instability windows that cause a sequence of consecutive instability windows to exceed the instability window.

In example 22, the subject matter of any one or more of examples 16 to 21 optionally includes determining a subset of targets, which may include detecting a longest sequence of consecutive unstable windows from among two or more sequences of consecutive unstable windows, and identifying an unstable window that results in the longest sequence of consecutive unstable windows.

The systems, devices, and methods discussed in this document can improve the performance of medical systems for managing episodes of events detected by the devices, such as episodes of arrhythmia, heart failure exacerbation events, and the like. As discussed previously, a challenge in patient management is the manual time and effort required to review episode data recorded by a large number of devices. This document provides a solution for automatically identifying and prioritizing presentation of clinically significant portions of data from recorded episodes based on event-specific signal metrics. The automatic identification, presentation, and prioritization of data subsets discussed herein may reduce the time and burden on clinicians for seizure evaluation and judgment with improved efficiency as compared to conventional data presentation techniques. With improved data presentation, clinicians may more quickly and efficiently identify FP detections at the expense of less time and effort, such that future inappropriate treatment or medical intervention on patients may be avoided or reduced. Thus, the subject matter discussed herein may better tailor medical resources to serve those patients with critical medical conditions.

The automatic identification and presentation of clinically significant portions of data discussed herein may also improve the functionality of the patient management system. The identification and prioritization of the data portions of interest may be performed in a communicator, mobile monitor, programmer, or remote patient management system in communication with the AMDs. Thus, in certain instances, improved alarm management may be achieved without modifying existing patient AMD or physiologic event detectors. In some cases, the identified clinically significant portions of data may also be prioritized for storage in memory. More efficient memory usage than conventional medical systems can be achieved. With fewer alarms and diagnostic events, the complexity and operating costs of the patient management system may be reduced. Additionally, with reduced FP detection, fewer unnecessary device treatments, medications, and procedures may be scheduled, prescribed, or provided, battery life and longevity of the AMD may be extended, and overall system cost savings may be realized.

This summary is an overview of some of the teachings of the present application and is not intended to be an exclusive or exhaustive treatment of the present subject matter. Further details on the present subject matter may be found in the detailed description and the appended claims. Other aspects of the disclosure will become 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 is not to be taken in a limiting sense. The scope of the present disclosure is defined by the appended claims and their legal equivalents.

Drawings

Various embodiments are shown by way of example in the drawings. These embodiments are illustrative and are not intended to be exhaustive or exclusive embodiments of the present subject matter.

Fig. 1 shows an example of a Cardiac Rhythm Management (CRM) system and portions of an environment in which the CRM system may operate.

Fig. 2 generally illustrates an example of a data presentation system for selectively presenting physiological data to a user.

FIG. 3 generally illustrates an example of a data arbitration circuit that can prioritize a subset of data acquired from a physiological signal corresponding to an atrial fibrillation episode detected by a device.

Fig. 4 is a graph illustrating an example of selecting a portion of a heart rate time series for presentation to a user to determine the onset of AF detected by the device.

Fig. 5 generally illustrates an example of a method for presenting physiological data to a user on a user interface.

Fig. 6 generally illustrates an example of a method for prioritizing subsets of data acquired from physiological signals corresponding to device-detected AF episodes.

Fig. 7 generally illustrates a block diagram of an example machine on which any one or more of the techniques (e.g., methods) discussed herein may execute.

Detailed Description

Systems, devices, and methods for presenting physiological data to a user are disclosed herein. An exemplary system includes a presentation control circuit that can generate a signal metric from various data subsets of a physiological signal corresponding to a physiological event of a presence detected by a device, and determine a target subset from the plurality of data subsets. The target set has corresponding signal metrics that satisfy certain conditions. The presentation control circuitry may present the identified target subset to the user in preference to the other data subsets. The user may determine a physiological event detected by the device or adjust one or more device parameters.

Fig. 1 generally illustrates an example of a Cardiac Rhythm Management (CRM) system 100 and portions of an environment in which the system 100 may operate. CRM system 100 may include a mobile system 105 associated with a patient 102, an external system 125, and a telemetry link 115 that provides communication between mobile system 105 and external system 125.

The ambulatory system 105 may include an Ambulatory Medical Device (AMD) 110. In an example, the AMDs 110 may be implantable devices that are implanted subcutaneously in the chest, abdomen, or other location of the patient 102. Examples of implantable devices may include, but are not limited to, pacemakers/defibrillators, Cardiac Resynchronization Therapy (CRT) devices, cardiac Remodeling Control Therapy (RCT) devices, neuromodulators, drug delivery devices, biological therapy devices, diagnostic devices (such as cardiac monitors or loop recorders), or patient monitors, among others. The AMD110 alternatively or additionally includes a subcutaneously implanted device, a wearable device, or other external monitoring or treatment medical device or equipment.

The AMDs 110 may be coupled to the lead wire system 108. The lead system 108 may include one or more transvenous, subcutaneous or non-invasively placed leads or catheters. Each lead or catheter may include one or more electrodes. The placement and use of the lead system 108 and associated electrodes may be determined based on the patient's needs and the capabilities of the AMDs 110. Associated electrodes on the lead system 108 may be positioned in the patient's chest or abdomen to sense physiological signals indicative of cardiac activity, or physiological responses to diagnostic or therapeutic stimuli of the target tissue. By way of example and not limitation, and as shown in fig. 1, lead system 108 may be configured to be surgically inserted into heart 101 or positioned on a surface of heart 101. The electrodes on the lead system 108 may be positioned on a portion of the heart 101, such as the Right Atrium (RA), Right Ventricle (RV), Left Atrium (LA), or Left Ventricle (LV), or any tissue between or near portions of the heart. In some examples, lead system 108 and associated electrodes are alternatively positioned on other parts of the body to sense physiological signals containing information about the patient's ventricular heart rate or pulse rate. In an example, the ambulatory system 105 may include one or more leadless sensors that are not tethered to the AMDs 110 by the lead system 108. The leadless mobile sensor may be configured to sense physiological signals and wirelessly communicate with the AMDs 110.

The AMDs 110 may be configured as monitoring and diagnostic devices. The AMDs 110 may include hermetically sealed cans that house one or more of sensing circuitry, control circuitry, communication circuitry, and batteries, among other components. The sensing circuit may sense a physiological signal, such as by using a physiological sensor or an electrode associated with the lead system 108. Examples of physiological signals may include one or more of an electrocardiogram, intracardiac electrogram, arrhythmia, heart rate variability, intrathoracic impedance, intracardiac impedance, arterial pressure, pulmonary arterial pressure, left atrial pressure, RV pressure, LV coronary pressure, coronary blood temperature, blood oxygen saturation, one or more heart sounds, intracardiac acceleration, physical activity or motion level, physiological response to activity, posture, respiration rate, tidal volume, respiration sounds, body weight, or body temperature.

In an example, the AMD110 can include a cardiac event detector circuit 160 configured to detect physiological events, such as arrhythmic episodes, Worsening Heart Failure (WHF) events from the patient 102. Examples of arrhythmias include atrial tachyarrhythmias (such as an AFL or AF episode), or ventricular tachyarrhythmias (such as a ventricular tachycardia or ventricular fibrillation episode). The AMDs 110 may sense physiological signals, detect cardiac events, and store physiological data in memory. The AMDs 110 may output detected cardiac events to a user, such as a patient or clinician, or to a process including a computer program instance that may be executed in a microprocessor, for example. In an example, the process may include automatically generating a recommendation for delivering a therapy (such as an anti-arrhythmic therapy), or a recommendation for further diagnostic testing or therapy.

The AMDs 110 are alternatively configured as therapy devices configured to treat cardiac arrhythmias or other cardiac conditions. The AMD110 can additionally include a therapy unit that can generate and deliver one or more therapies. Therapy may be delivered to the patient 102 through the lead system 108 and associated electrodes. The treatment may include electrical, magnetic or other types of treatment. The treatment may include an anti-arrhythmic treatment to treat the arrhythmia or to treat or control one or more complications from the arrhythmia, such as syncope, congestive heart failure, or stroke, and the like. Examples of anti-arrhythmic therapies may include pacing, cardioversion, defibrillation, neuromodulation, drug therapy, or biologic therapy, among other types of therapy. In an example, these therapies may include Cardiac Resynchronization Therapy (CRT) for correcting desynchronization and improving cardiac function in a CHF patient. In some examples, the AMD110 may include a drug delivery system (such as a drug infusion pump) to deliver drugs to a patient for managing arrhythmias or complications from arrhythmias.

Although the discussion herein regarding AMDs 110 focuses on implantable systems, this is by way of example only and not by way of limitation. It is within the contemplation of the inventors and the scope of this document that the systems, devices, and methods discussed herein may also be implemented in and performed by subcutaneous medical devices, such as subcutaneous monitors or diagnostic devices, wearable devices (e.g., watch-like devices, patch-based devices, or other accessories), or other ambulatory medical devices.

The external system 125 may communicate with the AMDs 110 over the communication link 115. The external system 125 may comprise 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 125 may be used to control the operation of the AMDs 110. The external system 125 may additionally receive information acquired by the AMDs 110, such as one or more physiological signals, over the communication link 115.

By way of example and not limitation, the external system 125 may include an external device 120 in proximity to the AMDs 110, a remote device 124 in a location relatively remote from the AMDs 110, and a telecommunications network 122 linking the external device 120 and the remote device 124. Telemetry link 115 may be an inductive telemetry link, a capacitive telemetry link, or a Radio Frequency (RF) telemetry link. The telemetry link 115 may provide data transmission from the AMDs 110 to the external system 125. This may include, for example, transmitting real-time physiological data acquired by the AMDs 110, extracting physiological data acquired by the AMDs 110 and stored in the AMDs 110, extracting patient history data (such as data recorded in the AMDs 110 indicating the occurrence of arrhythmias, the occurrence of decompensation, and therapy delivery), and extracting data indicating the operational status of the AMDs 110 (e.g., battery status and lead impedance). The telemetry link 115 may also provide data transmission from the external system 125 to the AMDs 110. This may include, for example, programming the AMDs 110 to perform one or more of acquiring physiological data, performing at least one self-diagnostic test (such as for device operating status), analyzing the physiological data to detect arrhythmias, or optionally delivering or adjusting therapy to the patient 102.

One or more of external device 120 or remote device 124 may include a display for displaying physiological or functional signals, or alarms, alerts, emergency calls, or other forms of alerts to signal detection of an arrhythmia. In some examples, the external system 125 may include an external data processor configured to analyze physiological or functional signals received by the AMD110 and confirm or reject detection of an arrhythmia. A computationally intensive algorithm, such as a machine learning algorithm, may be implemented in the external data processor to retrospectively process the data to detect arrhythmias.

In an example, the external data processor may process the physiological signal received from the AMD110 by evaluating signal metrics on a plurality of subsets of data, such as different signal portions obtained from the received physiological signal. The external data processor may select a signal portion (i.e., the target subset) that is more representative of the characteristics of the detected cardiac event than other subsets or signal portions (e.g., a portion of an ECG or EGM showing a high variable ventricular heart rate, characteristics of an AF episode). The external data processor may present the selected signal portion to the user in preference to other subsets of the physiological data, such as displaying the selected signal portion before displaying other portions of the physiological signal. By reviewing selected signal portions of the physiological signal, the user may use the external device 120 or the remote device 124 to determine the presence of a device-detected physiological event (e.g., an arrhythmia), or to adjust one or more parameters used by the AMDs 110 to detect the physiological event, or to adjust the therapy delivered to the patient.

Portions of the AMDs 110 or external systems 125 may be implemented using hardware, software, firmware, or a combination thereof. Portions of the AMDs 110 or external systems 125 may be implemented using dedicated circuitry that may be constructed or configured to perform one or more functions, or may be implemented using general-purpose circuitry that may be programmed or otherwise configured to perform one or more specific functions. Such general purpose circuitry may include a microprocessor or a portion thereof, a microcontroller or a portion thereof, or programmable logic circuitry or a portion thereof. For example, a "comparator" may particularly comprise an electronic circuit comparator which may be configured to perform a specific function of a comparison between two signals, or which may be implemented as a part of a general purpose circuit which may be driven by code instructing a part of the general purpose circuit to perform a comparison between two signals.

Fig. 2 generally illustrates an example of a data presentation system 200 configured to selectively present physiological data to a user. Portions of the data presentation system 200 may be included in an external system 125 (such as the external device 120 or the remote device 124) or distributed between the external system 125 and the AMDs 110. The data presentation system 200 may include one or more of a data receiver 210, a presentation control circuit 220, and a user interface unit 230. The data presentation system 200 may be configured as a cardiac monitor or diagnostic device for monitoring the health status of a patient. In some examples, data presentation system 200 may additionally include optional therapy circuitry 240.

The data receiver 210 may receive physiological data acquired during a physiological event, such as a physiological signal recorded by the AMDs 110. AMD can sense physiological signals using implantable, wearable, or otherwise mobile sensors or electrodes associated with a patient. Examples of physiological signals may include surface Electrocardiograms (ECGs) such as sensed from electrodes on the body surface, subcutaneous ECGs such as sensed from electrodes placed under the skin, intracardiac Electrograms (EGMs) sensed from one or more electrodes on lead system 108, 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, blood oxygen saturation signals, heart sound signals such as sensed by a ambulatory accelerometer or acoustic sensor, physiological response to activity, apnea hypopnea index, one or more respiratory signals (such as a respiration rate signal or a tidal volume signal), Brain Natriuretic Peptide (BNP), blood panels, sodium and potassium levels, glucose levels, and other biomarkers and biochemical markers, and the like. In an example, when the AMD110 detects a particular physiological event, such as an arrhythmic episode or WHF event, it may record the physiological signal. In another example, the AMDs 110 may record physiological signals in response to patient triggers (also referred to as patient-triggered episodes). In yet another example, the AMD110 may record a physiological signal when it detects a syncope or a pre-syncope event.

The recorded physiological signals may be stored in device memory within the AMDs 110 or in an external storage device such as an electronic medical recording system. The data receiver 210 may retrieve the stored physiological signals from the device memory or an external storage device. Data retrieval may be in response to a user command, or in response to a particular event. The received data may be processed by data processed by presentation control circuitry 220 prior to presentation to a user.

The presentation control circuit 220 may be implemented as part of 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 physical activity information. Alternatively, the microprocessor circuit may be a general purpose processor that can receive and execute a set of instructions to perform the functions, methods, or techniques described herein.

The presentation control circuit 220 may include a circuit group that includes one or more other circuits or sub-circuits, such as a signal divider circuit 222, a signal metric circuit 224, and a data arbitration circuit 240. These circuits may perform the functions, methods, or techniques described herein, either alone or in combination. In an example, the hardware of the circuit group may be non-alterably designed to carry out a particular operation (e.g., hardwired). In an example, the hardware of the circuit set may include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.) including a computer-readable medium physically modified (e.g., to magnetically, electrically, movably placing of invariant aggregate particles, etc.) to encode instructions for a particular operation. When physical components are connected, the basic electrical characteristics of the hardware components are caused to change, for example, from an insulator to a conductor, and vice versa. The instructions enable embedded hardware (e.g., execution units or loading mechanisms) to create members of a circuit group in the hardware through variable connections to perform portions of a particular operation when operating. Thus, when the device is operating, the computer readable medium is communicatively coupled to other components of the circuit group member. In an example, any of the physical components may be used in more than one member of more than one circuit group. For example, in operation, the execution unit may be used for a first circuit of a first circuit group at one point in time and reused by a second circuit of the first circuit group, or reused by a third circuit of a second circuit group at a different time.

The signal divider circuit 222 may divide the physiological data received from the sensor circuit 210 into a plurality of data subsets { X1, X2, …, XN }. In an example, the signal divider circuit 222 divides the received physiological signal recorded during the cardiac event detected by the device into different signal portions. In an example, the signal portion is a plurality of beat windows each having a particular length (represented by a particular number of heartbeats or duration). The length of the beat window may be programmable, such as adjustable by a user via the user interface 230. In an example, the beat window is about 2 to 5 minutes. In another example, the beat window has a length of 10 to 20 heartbeats. The beat windows can be consecutive in time without overlapping each other. In some examples, two or more beat windows may partially overlap each other. For example, the beat window may be a rolling window of fixed length (e.g., a fixed number of heartbeats) that moves through the stored physiological signal. Examples of data partitioning and beat windows for scheduling presentation of various portions of a physiological signal are discussed below, such as discussed with reference to fig. 4.

The signal metric circuit 224 may evaluate a signal metric for each of the subsets of data generated by the signal divider circuit 222. The signal metric may represent a characteristic of a physiological event associated with the physiological signal. The signal metric may depend on the type of physiological event, such as detected by the AMDs 110. In examples where physiological data associated with an atrial tachyarrhythmia event, such as AF, is presented, the signal metric circuit 224 may evaluate ventricular Heart Rate Variability (HRV) on a subset of data acquired from the physiological data recorded during the AF episode. This is partly because AF typically has a variable ventricular heart rate. In another example of presenting physiological data associated with syncope or a pre-syncope event, such as a cardiac syncope episode, the signal metric circuit 224 can evaluate a cardiac arrest metric (e.g., a duration or pattern of cardiac arrest) on a subset of data obtained from physiological data recorded during a detected syncope or pre-syncope event. This is due in part to the long cardiac arrest that usually occurs with cardiac syncope. In another example, for a patient-triggered episode, the signal metric circuit 224 may evaluate one or more signal metrics of the data subset including, for example, a heart rate metric, an ectopic beat metric such as a count of ectopic beats, the presence of asystole or arrhythmia, an Atrioventricular (AV) conduction metric such as the presence of AV block, or a signal morphology metric.

The data arbitration circuit 226 may select a particular signal portion (target subset or target data window, X) that satisfies a particular condition, such as being more representative of a characteristic of the detected cardiac event than other subsets or signal portions. For example, in selecting a subset of data from the physiological signal corresponding to the onset of AF, the selected target subset X has a higher HRV value than the other subsets of data. The target subset or beat window X thus selected is more representative of the nature of the detected episode. The data arbitration circuit 226 may present the target subset or beat window X to the user in preference to the other data subsets. This allows the user to more quickly and efficiently identify the type of arrhythmia, or to determine whether the detection decision made by the AMD110 is a true positive detection (indicating that the user agrees with the device detection) or a false positive detection (indicating that the user does not agree with the device detection). Thus, the prioritized presentation of data by the presentation control circuitry 220 saves time, effort, and cost of data review and judgment, and allows for more timely intervention or adjustment, such as event detection by the AMDs 110.

The user interface 230 may include an input device and an output device. In an example, at least a portion of the user interface 230 may be implemented in the external system 130. The input device may receive user programming input, such as parameters for determining and comparing signal metrics on the plurality of data subsets. The input device may include a keyboard, on-screen keyboard, mouse, trackball, touch pad, touch screen, or other pointing or navigation device. The input device may enable a system user to program parameters for sensing physiological signals, calculating signal metrics for the data subsets, generating alarms, and the like.

The output device may present a physiological signal corresponding to the physiological event to the user, such as displaying the physiological signal on a display. The user can review the presented physiological signal and provide a determination (e.g., an annotation for a true positive or false positive detection) via the input device. In an example, the output device may present multiple subsets of data (e.g., different signal portions or beat windows) according to the prioritization of data determined by the data arbitration circuit 226. For example, in displaying the physiological signal corresponding to the device detected AF episode, the output device may display a target subset or beat window X to the user before the other data subsets. As discussed above, X is a portion of data that is more likely to reveal characteristics of AF (e.g., a larger HRV) than other subsets of data. By presenting the target subset or beat window X first, the user may spend less time and effort in reviewing the presented physiological signals in order to make decision decisions or adjust device parameters for detecting physiological events or delivering therapy. The selective and prioritized presentation of data discussed herein is a more efficient and cost-effective method of judgment, particularly for users who hold large amounts of data for judgment, than the conventional non-selective presentation of stored physiological data in a chronological order.

The output device may additionally present intermediate measurements or calculations to the user, such as values of signal metrics for the subset of data. The information may be presented in a table, chart, graph, or any other type of text, table, or graphical presentation format. The presentation of the output information may include audio or other media formats for alerting a system user to a device detected physiological event associated with the physiological signal being displayed. The output unit may include a printer for printing a hard copy of the detection information. In an example, the output device may generate an alarm, alert, emergency call, or other form of alert to signal to a system user about the detected arrhythmia event.

The optional therapy circuit 240 may be configured to deliver therapy to the patient, such as based on user determination of a device detected physiological event. Examples of therapy may include electrical stimulation therapy delivered to the heart, neural tissue, other target tissue, cardioversion therapy, defibrillation therapy, or drug therapy. In some examples, the therapy circuitry 240 may modify an existing therapy, such as adjusting a stimulation parameter or a drug dose. For example, if there are a large number of FP detections from the decision, one or more treatment parameters may be adjusted. The user may accept, reject or modify the treatment adjustment, for example, via the input device.

FIG. 3 illustrates an example of a data arbitration circuit 300 configured to prioritize subsets of data acquired from physiological signals corresponding to AF episodes detected by AMDs 110. Data arbitration circuit 300, which is an embodiment of data arbitration circuit 226, includes unstable beat detector circuit 310, unstable window detector circuit 320, and target beat window detector 330.

The unstable-beat detector circuit 310 may identify the heartbeat as a stable beat or an unstable beat using a comparison of the cardiac Cycle Length (CL) of the current beat (CL (n)) and a reference CL. CL may be represented by an R-wave-to-R-wave interval (RR interval), which may be measured from an ECG or intracardiac electrogram. In an example, beat-to-beat changes in cycle length may be used to identify stable or unstable beats, i.e., Δ CL (n) -CL (n-1), where CL (n-1) is the cycle length immediately preceding the current heartbeat. The unstable beat detector circuit 310 may identify beat "n" as a stable beat if Δ cl (n) is less than the beat stability threshold, or as an unstable beat if Δ cl (n) is greater than the beat stability threshold. An exemplary beat stability threshold is approximately 5 to 10 beats per minute (bpm). In another example, the unstable beat detector circuit 310 may identify beat "n" as a stable beat if Δ cl (n) falls within a first heart rate range, or as an unstable beat if Δ cl (n) falls within a second heart rate range. In an example, the first heart rate range is 0 to 5bpm and the second heart rate range is 5 to 100 bpm. In some examples, the unstable beat detector circuit 310 may identify the beat as an unstable beat only if the heart rate is within a range of heart rates. For example, if Δ cl (n) falls within a specified range or exceeds a threshold, beat "n" is identified as an unstable beat, and the heart rate around beat "n" (such as an average heart rate calculated as 3 to 5 cardiac cycles around beat "n") falls within a range of about 30 to 250 bpm.

The unstable window detector 320 is configured to detect one or more unstable beat windows from a plurality of beat windows, such as a subset of data provided by the signal divider circuit 222. Each beat window contains a specified number of heartbeats. The unstable window detector 320 can identify the beat window as an unstable window if there is a sufficient number of unstable beats in the beat window (as identified by the unstable beat detector circuit 310), such as exceeding an unstable beat count threshold, which is expressed as an absolute count number or a relative number (e.g., a fraction or percentage). In an example, each beat window includes 10 ventricular beats, and if the beat window includes at least 50% of the unstable beats (or five or more), the beat window is determined to be an unstable window.

In some examples, the unstable window detector 320 may trend the counts of unstable heartbeats over the beat window and smooth the trend, such as by using a low pass filter or calculating a moving average of the unstable heartbeat counts. Based on the smoothed trend, the unstable window detector 320 may re-determine the count of unstable heartbeats in each of the beat windows and identify an unstable window if the re-determined count of unstable heartbeats contained therein exceeds an unstable heartbeat count threshold.

If the unstable window detector circuit 320 identifies only one unstable window from the multiple data windows examined, the target beat window detector 330 may determine the identified unstable window as the target beat window X, which may then be prioritized for presentation to the user, such as through the user interface 230, before other beat windows or subsets. However, if the unstable window detector circuit 320 identifies two or more unstable windows from the plurality of data windows examined, the target beat window detector 330 may select at least one target beat window target subset X using one of the unstable window comparator 332 or the unstable window sequence detector 334. In an example, the unstable window comparator 332 may determine the target beat window X based on the timing of the identified unstable window. In an example, the target beat window X may be determined as the first detected unstable window in time. In an example, upon detecting the first unstable window, the unstable window is determined as the target beat window X; and the unstable window detector circuit 320 may stop the unstable window evaluation for the remaining beat windows. In another example, the unstable window comparator 332 determines the target beat window X based on the number of unstable beats in the identified unstable window. In an example, the unstable window comparator 332 may compare the number of unstable beats in the identified unstable window and determine the target beat window X as the unstable window with the least stable beat among the beat windows examined. In yet another example, the unstable window comparator 332 may identify a target beat window X based on the pattern of unstable beats in the identified unstable window. An example of an unstable beat pattern includes a beat sequence of consecutive numbers of unstable beats. In an example, the unstable window comparator 332 may determine the target beat window X as the unstable window having a sequence of consecutive unstable beats exceeding a threshold number of unstable beats, or the unstable window having the longest sequence of consecutive unstable beats among the beat windows examined.

The unstable window sequence detector 334 may identify a target beat window X based on a pattern of two or more identified beat windows. In an example, the unstable window sequence detector 334 may detect consecutive unstable windows W₁,W₂,…,W_kIn such a way that in any two adjacent unstable windows W_iAnd W_i+1In between, no stable window (i.e., a beat window that does not meet the unstable beat requirement set in the unstable window detector circuit 320) is detected. If the detected sequence of consecutive unstable windows is long enough that the length k of the sequence exceeds the threshold of the unstable window count, the unstable window sequence detector 334 may cause the unstable window to be in questionUnstable window of the mouth sequence (W in this example)₁) Determined as target beat window X. Alternatively, when two or more sequences of consecutive unstable windows are detected (particularly if a smaller threshold of unstable window counts is used, e.g., 3 to 5), the unstable window sequence detector 334 may identify the longest sequence of consecutive unstable windows and determine the unstable window that results in the longest unstable window sequence as the target beat window X. An example of using unstable beats or a sequence of unstable windows in the identified unstable windows to determine a target beat window X is discussed below, such as with reference to fig. 4.

FIG. 4 is a graph illustrating an example of selecting a portion of a heart rate time series 410 for presentation to a user (e.g., a clinician) to determine detection of an AF episode, such as that detected by an AMD110 from a patient. The graph may be presented to a user through a user interface 230, such as displayed on a display unit. Heart rate time series 410 represents the beat-to-beat ventricular heart rate (beats per minute, or bpm, on the left y-axis) as a function of time (the beat index on the x-axis). The sensor circuit 210 may sense ECG or ventricular Electrogram (EGM) signals, such as signals recorded by the AMD110 in response to AF detection, detect ventricular activation or heartbeat from the ECG or EGM, measure beat-to-beat cardiac cycle length, and determine beat-to-beat heart rate. As shown in fig. 4, the portion of heart rate time series 410 shown has an average heart rate of about 75 to 80bpm and fluctuates over time while having a more varying heart rate in a posterior portion (e.g., heartbeats after about a heartbeat index of 60) as compared to an anterior portion (e.g., heartbeats before about a heartbeat index of 60).

Also shown in fig. 4 is an unstable beat count trend 420, representing the number of unstable beats in multiple beat windows (on the right y-axis). The beat windows each capture a subset of the heart rate time series 410. The beat windows may partially overlap. By way of example and not limitation, a beat window contains 10 beats moving through the heart rate time series 410 with 90% overlap with its neighboring beat window (i.e., 9 beats). Unstable beats may be detected from each beat window and the number of unstable beats may be determined, such as using the unstable node beat detector circuit 310. Unstable beat windows may be identified as those beat windows having an unstable beat frequency count (5 in the example of fig. 4) that exceeds the threshold 450, for example using the unstable window detector circuit 320.

A target beat window X may then be determined from the identified unstable windows. In an example, the unstable window comparator 332 may be used to select the target beat window X corresponding to the unstable beat count 461 that is the largest one of the unstable beat counts over the identified unstable window as shown in the unstable beat count trend 420. The target beat window X thus selected has the most unstable beat among the beat windows examined.

Alternatively, the unstable window sequence detector 334 may be used to select a target beat window X corresponding to the unstable beat count 462 that results in a sequence 470 of consecutive unstable windows each having a desired number of unstable beats (such as 8 or more unstable beats within each beat window). If two or more sequences of consecutive unstable windows are detected, the unstable window that results in the longest unstable window sequence is determined as target beat window X. The portion of the recorded ECG or EGM within the target beat window X may then be presented to the user with a higher priority than other portions of the recorded ECG or EGM or beat windows.

Fig. 5 generally illustrates an example of a method 500 for presenting physiological data to a user on a user interface. The physiological data may include physiological signals recorded by a medical device, such as the AMD110, in response to the presence of a device-detected physiological event, such as an arrhythmic episode, a heart failure Worsening (WHF) event, a syncope or pre-syncope event, or a patient-triggered event. The method 500 may be implemented in the external system 125, distributed between the external system 125 and the AMDs 110, or in the data presentation system 200 as previously discussed and executed by the external system 125, distributed between the external system 125 and the AMDs 110, or by the data presentation system 200 as previously discussed.

The method 500 begins at step 510, where a physiological event corresponding to a device detection may be usedThe physiological signal of the presence of the member generates a subset of data. Examples of physiological signals may include surface Electrocardiograms (ECGs), subcutaneous ECGs, intracardiac Electrograms (EGMs), chest or cardiac impedance signals, blood pressure signals, blood oxygen saturation signals, heart sound signals, physiological responses to activity, apnea hypopnea index, one or more respiratory signals such as respiratory rate signals or tidal volume signals, brain natriuretic peptides, blood panels, sodium and potassium levels, glucose levels and other biomarkers and biochemical markers, and the like. The physiological signal may be divided into a plurality of subsets of data { X }₁,X₂,…,X_NSuch as by using signal dividing circuit 222. In an example, the beat window can be generated using a rolling window of a specified window length (e.g., a specified number of heartbeats) that moves through the physiological signal. The beat windows may be consecutive (no overlap), or at least partially overlap each other.

At 520, a signal metric may be generated for the data subset or beat window, such as by using the signal metric circuit 224. The signal metric may depend on the type of physiological event detected by the device. For example, for an ECG or EGM recorded in response to a device detected atrial tachyarrhythmia episode, a ventricular Heart Rate Variability (HRV) metric may be generated for a beat window created from an ECG or EGM signal corresponding to the detected atrial tachyarrhythmia episode. In another example, for physiological signals recorded in response to patient-triggered episodes, one or more metrics may be generated for the beat window, including a heart rate metric, an ectopic beat metric such as a count of ectopic beats, the presence of asystole or arrhythmia, an Atrioventricular (AV) conduction metric such as the presence of AV block, or a signal morphology metric, among others. In yet another example, a cardiac arrest metric (e.g., duration or pattern of cardiac arrest) may be generated for a beat window for a physiological signal recorded in response to a syncope or pre-syncope event detected by a device.

At 530, a target subset may be selected from the plurality of data subsets using the signal metrics generated for the data subsets, respectively. The target subset may be selected such that the corresponding signal metric generated therefrom may be more representative of a characteristic of the physiological event detected by the device than other data subsets or beat windows. In an example of an AF episode detected by a presentation device, the target beat window may be selected as one of a plurality of beat windows having a Heart Rate Variability (HRV) value greater than the other data subsets. The subset of targets thus selected is more representative of the nature of the AF episode detected by the device. At 540, the data arbitration circuit 226 may present the target subset or beat window X to the user in preference to the other data subsets. The determination of the target subset at 530 and the prioritized presentation of the target subset at 540 may be performed using the data arbitration circuit 226. Examples of selecting a target subset or target beat window are discussed below, such as with reference to fig. 6.

The target subset of physiological signals may be provided to one or more processes 552, 554, or 556. At 552, a physiological signal can be output to the user according to the prioritization of the subset of data determined at 540. In an example, the target subset of physiological signals may be displayed on a display of the user interface 230. Other data subsets of the physiological signal having a lower priority than the target subset may be displayed after the target subset has been displayed, such as via user commands via user input through the user interface 230. In various examples, the signal metrics of the target subset and other data subsets may also be presented to a user, such as being displayed on a display unit or printed in a hard copy. In various examples, an alarm, alert, emergency call, or other form of alert may be generated to notify the user of a physiological event detected by the device associated with the physiological signal being displayed.

At 554, a determination of the presence of a device-detected physiological event by a user (e.g., detection of an arrhythmic episode generated by the AMD 110) may be received. The determination may include an indication of whether the detection by the device (e.g., the AMD 110) is a true positive detection (indicating that the user agrees with the device detection) or a false positive detection (indicating that the user does not agree with the device detection). Because the target subset may be more likely to reveal characteristics of the physiological event detected by the device than other data subsets, the preferential presentation of the target subset or beat window may allow a user to more quickly and efficiently identify the presence or absence of a physiological event, thereby saving the user time and effort in reviewing physiological signals to make decision-making decisions.

At 556, one or more device parameters for physiologic event detection or therapy delivery can be adjusted, such as based on a determination by a user of the presence of a physiologic event detected by the device. For example, if there are a large number of false positive detections from the determination, one or more treatment parameters may be adjusted. The user may accept, reject, or modify the treatment adjustment, for example, via the user interface 230. The parameters may be adjusted automatically or manually by the user. In an example, recommendations may be generated and provided to the user, including further diagnostic tests to be performed, implantation of the device, adjustment of one or more physiological event detection parameters or treatment parameters (e.g., electrical stimulation parameters or drug dose). The therapy may be delivered according to the adjusted therapy parameters. Examples of therapy may include electrical stimulation therapy delivered to the heart, neural tissue, other target tissue, cardioversion therapy, defibrillation therapy, or drug therapy.

Fig. 6 is a flow chart illustrating an example of a method 600 for prioritizing a subset of data acquired from a physiological signal corresponding to a device detected AF episode. Method 600 may be an embodiment of step 530 of determining a subset of targets as shown in fig. 5. The method 600 begins at 610 by detecting an unstable heartbeat from each of a plurality of beat windows, such as by using the unstable beat detector circuit 310. The beat window is a subset of data acquired from an ECG or EGM recorded in response to the presence of an AF episode detected by the device. In an example, each beat window includes 10 to 20 ventricular heartbeats. Unstable beats may be detected using beat-to-beat changes in cycle length (denoted as Δ cl (n) at heart beat "n"), e.g., R-wave to R-wave intervals in an ECG or EGM. If the difference between the cycle length of the current heartbeat and the cycle length immediately before the current heartbeat exceeds a threshold value, or falls within a certain range, the heartbeat is detected as an unstable heartbeat. In some examples, identification of unstable beats may also require the heart rate to be within a heart rate range, such as between about 30 to 250 bpm.

At 620, an unstable window can be detected based at least on the count of unstable beats in the beat window. A beat window may be identified as an unstable window if there is a sufficient number of unstable beats in the beat window, e.g., exceeding an unstable beat count threshold. In an example, a beat window is considered unstable if 50% or more of the beats are unstable beats.

At 630, the instability window detected at step 620 is analyzed to identify at least one target beat window. If at 630, only one unstable window is detected from the multiple data windows examined, the only detected unstable window may be identified as the target beat window, and the presentation of such target beat window may be prioritized at step 540 of fig. 5. If two or more unstable windows are detected from the examined data window at 630, one of alternative methods 632, 634, or 636 may be performed to determine the target beat window.

At 632, a target beat window can be detected based on the number of unstable beats in the identified unstable window. The unstable window with the most (i.e., the largest number) of unstable heartbeats may be identified and determined as the target beat window. At 634, a target beat window may be detected based on the timing of the identified unstable window. The first temporally detected unstable window may be identified and determined as the target beat window. At 636, a target beat window can be detected based on the pattern of the two or more identified beat windows. Successive unstable windows W may be identified, such as using unstable window sequence detector 334₁,W₂,…,W_kThe sequence of (c). If the detected sequence of consecutive unstable windows is long enough such that the length k of the sequence exceeds the threshold of unstable window counts, the unstable window that results in the unstable window sequence may be determined to be the target beat window. Alternatively, when two or more sequences of consecutive unstable windows are detected, the longest sequence of consecutive unstable windows may be identified, and the unstable window that results in the longest unstable window sequence may be determined as the target beat window. At one of steps 632, 634, or 636The determined target beat window can be provided to step 540 to prioritize presentation of the beat window to the user.

Fig. 7 generally illustrates a block diagram of an example machine 700 on which any one or more of the techniques (e.g., methods) discussed herein may execute. Portions of the present description may be applied to the computing framework of various portions of an LCP device, IMD, or external programmer.

In alternative embodiments, the machine 700 may operate as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine 700 may operate in the capacity of a server machine, a client machine, or both, in server-client network environments. In an example, the machine 700 may operate as a peer machine in a peer-to-peer (P2P) (or other distributed) network environment. The machine 700 may be a Personal Computer (PC), a tablet PC, a set-top box (STB), a Personal Digital Assistant (PDA), a mobile telephone, a web appliance, a network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein, such as cloud computing, software as a service (SaaS), other computer cluster configurations.

Examples as described herein may include or be operated by logic or multiple components or mechanisms. A circuit group is a collection of circuits implemented in a tangible entity that includes hardware (e.g., simple circuits, gates, logic, etc.). Circuit group membership may be flexible over time and variability in underlying hardware. The circuit group includes components that perform particular operations when operated alone or in combination. In an example, the hardware of the circuit group may be non-alterably designed to carry out a particular operation (e.g., hardwired). In an example, the hardware of the circuit set may include variably connected physical components (e.g., execution units, transistors, simple circuits, etc.) including a computer-readable medium physically modified (e.g., to magnetically, electrically, movably placing of invariant aggregate particles, etc.) to encode instructions for a particular operation. When physical components are connected, the basic electrical characteristics of the hardware components are caused to change, for example, from an insulator to a conductor, and vice versa. The instructions enable embedded hardware (e.g., execution units or loading mechanisms) to create members of a circuit group in the hardware through variable connections to perform portions of a particular operation when operating. Thus, when the device is operating, the computer readable medium is communicatively coupled to other components of the circuit group member. In an example, any of the physical components may be used in more than one member of more than one circuit group. For example, in operation, the execution unit may be used for a first circuit of a first circuit group at one point in time and reused by a second circuit of the first circuit group, or reused by a third circuit of a second circuit group at a different time.

A machine (e.g., a computer system) 700 may include a hardware processor 702 (e.g., a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), a hardware processor core, or any combination thereof), a main memory 704 and a static memory 706, some or all of which may communicate with each other via an interconnect (e.g., bus) 708. The machine 700 may also include a display unit 710 (e.g., a raster display, a vector display, a holographic display, etc.), an alphanumeric input device 712 (e.g., a keyboard), and a User Interface (UI) navigation device 714 (e.g., a mouse). In an example, the display unit 710, the input device 712, and the UI navigation device 714 may be a touch screen display. The machine 700 may additionally include a storage device (e.g., drive unit) 716, a signal generation device 718 (e.g., a speaker), a network interface device 720, and one or more sensors 721, such as a Global Positioning System (GPS) sensor, compass, accelerometer, or other sensor. The machine 700 may include an output controller 728 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 or control one or more peripheral devices (e.g., a printer, card reader, etc.).

The storage 716 may include a machine-readable medium 722 on which is stored one or more sets of data structures or instructions 724 (e.g., software) that implement or are utilized by any one or more of the techniques or functions described herein. The instructions 724 may also reside, completely or at least partially, within the main memory 704, within static memory 706, or within the hardware processor 702 during execution thereof by the machine 700. In an example, one or any combination of the hardware processor 702, the main memory 704, the static memory 706, or the storage device 716 may constitute machine-readable media.

While the machine-readable medium 722 is shown to be 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) that are configured to store the one or more instructions 724.

The term "machine-readable medium" may include any medium that is capable of storing, encoding or carrying instructions for execution by the machine 700 and that cause the machine 700 to perform any one or more of the techniques of this disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Non-limiting examples of machine-readable media may include solid-state memory and optical and magnetic media. In an example, a high capacity machine readable medium includes a machine readable medium with a plurality of particles having a constant (e.g., static) mass. Thus, a mass machine-readable medium is not a transitory propagating signal. Specific examples of the mass machine-readable medium may include: nonvolatile memories such as semiconductor Memory devices (e.g., Electrically Programmable Read-Only memories (EPROMs), Electrically Erasable Programmable Read-Only memories (EEPROMs)) and flash Memory devices, magnetic disks (e.g., internal hard disks and removable magnetic disks), magneto-optical disks, and CD-ROMs, and DVD-ROM disks.

The instructions 724 may also be transmitted or received over the communication network 726 using a transmission medium via the network interface device 720 using any one of a number of transfer 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 a Local Area Network (LAN), a Wide Area Network (WAN), a packet data network (e.g., the internet), a mobile Telephone network (e.g., a cellular network), a Plain Old Telephone (POTS) network, and a wireless data network (e.g., known as a "local area network")Of the Institute of Electrical and Electronics Engineers (IEEE)802.11 series of standards, known asIEEE 802.16 series of standards), IEEE 802.15.4 series of standards, peer-to-peer (P2P) networks, and the like. In an example, the network interface device 720 can include one or more physical jacks (e.g., ethernet jacks, coaxial cable jacks, or telephone jacks) or one or more antennas to connect to the communication network 726. In an example, the network interface device 720 may include multiple antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) technologies. The term "transmission medium" shall be taken to include any intangible medium that is capable of storing, encoding or carrying instructions for execution by the machine 700 and that includes digital or analog communications signals, or other intangible medium to facilitate communication of such software.

Various embodiments are shown in the above figures. One or more features from one or more of these embodiments may be combined to form further embodiments.

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

The above detailed description is intended to be illustrative, and not restrictive. The scope of the disclosure should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled.