Detection and monitoring of sleep apnea conditions

Abstract

The present disclosure provides a method of detecting sleep apnea, the method comprising generating a cardiac signal indicative of patient cardiac activity. The method further comprises determining a short-term average heart rate and a long-term average heart rate. The method further comprises determining a start and an end of a heart rate cycle based on the short-term average heart rate and the long-term average heart rate. The method further comprises determining physiological parameter values generated during the heart rate cycle. The method further comprises determining whether the patient has experienced or has not experienced a sleep apnea episode based on whether one or more parameter values for one or more heart rate cycles satisfy one or more conditions, and responsively generating an indication that the patient has experienced or has not experienced a sleep apnea episode.

Description

Technical Field

[0001] This disclosure relates in general to techniques for monitoring the physiological condition of a patient, and more specifically to techniques for detecting sleep apnea episodes. Background Technology

[0002] When functioning normally, the heart maintains its own intrinsic rhythm and pumps enough blood throughout the circulatory system. This intrinsic rhythm is a function of an intrinsic signal generated by the sinoatrial (SA) node located in the upper right atrium. The SA node periodically depolarizes, which in turn causes depolarization of the atrial tissue, resulting in contraction of the right and left atria as depolarization travels through the atrial tissue. The atrial depolarization signal is also received by the atrioventricular (AV) node, which in turn triggers a subsequent ventricular depolarization signal that travels through and depolarizes the ventricular tissue, causing contraction of the right and left ventricles.

[0003] A condition known as sleep apnea can reduce cardiac output and pose various risks to patients, especially those sensitive to heart failure. Sleep apnea is a sleep disorder involving the temporary cessation of airflow during sleep. In various cases, sleep apnea can be characterized by one or both of the following: cessation of breathing during sleep or shallow breathing cycles.

[0004] Sleep apnea is generally considered a medical syndrome that arises in various forms. One recognized form is central sleep apnea, which is associated with the central nervous system's inability to automatically initiate and control breathing. Another recognized form is obstructive sleep apnea, which is associated with airway obstruction due to airway collapse. A further recognized form is the mixed form, which may include both the central nervous system's inability to drive ventilatory effort and obstructive apnea.

[0005] The potential consequences of sleep apnea include daytime sleepiness, impaired alertness, and various associated cardiovascular diseases, which in turn can significantly impair a patient's lifestyle and increase their risk of illness. In some cases, obstructive sleep apnea can lead to death due to lack of oxygen to vital organs. Various methods have been employed to detect and treat sleep apnea. Summary of the Invention

[0006] Some existing monitoring systems rely on respiratory measurement data to detect sleep apnea episodes. For example, algorithms based on cardiac signals have been used to detect sleep apnea episodes. However, some sleep apnea algorithms have significant false positive error rates. Therefore, there is a need for technologies to accurately detect sleep apnea episodes.

[0007] In one example, this disclosure provides a method for detecting sleep apnea. The method includes: sensing cardiac signals indicative of a patient's cardiac activity via a sensing circuitry system. The method further includes: determining, via the processing circuitry system and based on the cardiac signals, a short-term average of the patient's heart rate and a long-term average of the patient's heart rate, wherein the short-term average of the patient's heart rate is based on fewer heartbeats than the long-term average of the patient's heart rate. The method further includes: determining, via the processing circuitry system, the commencement of a heart rate cycle based on a first time when the short-term average of the patient's heart rate changes from less than the long-term average of the patient's heart rate to greater than the long-term average of the patient's heart rate. The method further includes: determining, via the processing circuitry system, a peak-to-trough time interval, which is the time interval between the maximum short-term average of the heart rate during the heart rate cycle and the minimum short-term average of the heart rate during the heart rate cycle. The method further includes: determining, via a processing circuitry system, whether one or more of a plurality of conditions for a heart rate cycle are met, including a peak-to-trough time interval condition, wherein the peak-to-trough time interval is greater than a lower threshold and less than an upper threshold. The method further includes: determining, via the processing circuitry system, that the patient has experienced a sleep apnea episode based at least in part on the satisfaction of one or more conditions for a heart rate cycle. The method further includes: generating, via the processing circuitry system, an indication that the patient has experienced a sleep apnea episode.

[0008] In another example, this disclosure provides a system for detecting sleep apnea. The system includes a sensing circuitry system and a processing circuitry system. The sensing circuitry system is configured to sense cardiac signals indicative of a patient's cardiac activity; the processing circuitry system is configured to: determine a short-term average and a long-term average of the patient's heart rate based on the cardiac signals, the short-term average of the patient's heart rate being based on fewer heartbeats than the long-term average of the patient's heart rate. The processing circuitry system is further configured to: determine the commencement of a heart rate cycle based on a first time when the short-term average of the patient's heart rate changes from less than the long-term average of the patient's heart rate to greater than the long-term average of the patient's heart rate. The processing circuitry system is further configured to: determine the termination of a heart rate cycle based on a second time when the short-term average of the patient's heart rate changes from less than the long-term average of the patient's heart rate to greater than the long-term average of the patient's heart rate. The processing circuitry system is further configured to: determine a peak-to-trough time interval, which is the time interval between the maximum short-term average of the heart rate during the heart rate cycle and the minimum short-term average of the heart rate during the heart rate cycle. The processing circuitry is further configured to: determine whether one or more of a plurality of conditions for the heart rate cycle are met, wherein one of the plurality of conditions is that the peak-to-trough time interval is greater than a lower threshold and less than an upper threshold. The processing circuitry is further configured to: determine, at least in part, that the patient has experienced a sleep apnea episode based on the satisfaction of one or more conditions for the heart rate cycle. The processing circuitry is further configured to: generate an indication that the patient has experienced a sleep apnea episode.

[0009] In another example, the present invention provides a non-transitory computer-readable storage medium encoded with instructions. When executed, the instructions cause the processing circuitry of a medical device system to receive cardiac signals indicative of a patient's cardiac activity. The instructions further cause the processing circuitry to determine, based on the cardiac signals, a short-term average and a long-term average of the patient's heart rate, the short-term average being based on fewer heartbeats than the long-term average. The instructions further cause the processing circuitry to determine, based on a first time when the short-term average of the patient's heart rate changes from less than the long-term average to greater than the long-term average, that a heart rate cycle has begun. The instructions further cause the processing circuitry to determine, based on a second time when the short-term average of the patient's heart rate changes from less than the long-term average to greater than the long-term average, that a heart rate cycle has ended. The instructions further cause the processing circuitry to determine a peak-to-trough time interval, which is the time interval between the maximum short-term average of the heart rate during the heart rate cycle and the minimum short-term average of the heart rate during the heart rate cycle. The instructions further cause the processing circuitry to determine whether one or more of a plurality of conditions for the heart rate cycle are met, wherein one of the conditions is that the peak-to-trough time interval is greater than a lower threshold and less than an upper threshold. The instructions further cause the processing circuitry to determine, at least in part, that the patient has experienced a sleep apnea episode based on the satisfaction of one or more conditions for the heart rate cycle. The instructions further cause the processing circuitry to generate an indication that the patient has experienced a sleep apnea episode.

[0010] Details of one or more examples of this disclosure are set forth in the following figures and description. Other features, objectives, and advantages will be apparent from the specification, figures, and claims. Attached Figure Description

[0011] Figure 1 The diagram illustrates a conceptual illustration of an example medical device system integrated with a patient, wherein the medical device system is configured to implement various sleep apnea detection and communication functions disclosed herein.

[0012] Figure 2 This is a conceptual diagram illustrating another example of a medical device system for a patient, wherein the medical device system is configured to detect sleep apnea episodes in a patient according to the technology of this disclosure.

[0013] Figure 3 This is a functional block diagram illustrating an example configuration of a medical device configured to sense and record a patient's electrocardiogram signals.

[0014] Figure 4 It is a line graph that displays the patient's sleep disturbance heart rate (SDHR) information.

[0015] Figure 5 This is a flowchart illustrating a method by which a medical device system configured according to aspects of this disclosure can detect sleep apnea episodes. Detailed Implementation

[0016] Generally, this disclosure relates to detecting individual episodes of sleep apnea. Sleep apnea is a breathing disorder that cuts off the oxygen supply to various systems and organs of the body. In response to the reduced oxygenation levels, the body's organs and systems may trigger one or more compensatory mechanisms. In the case of the cardiovascular system, compensatory mechanisms cause the heart to increase its blood output for a period of time. Thus, cardiac compensatory mechanisms lead to increased cardiac exertion. Furthermore, at the end of a sleep apnea episode and during the recovery period after a sleep apnea episode, the patient's heart rate may increase significantly due to alveolar hyperventilation caused by compensatory mechanisms of the pulmonary system. The peak heart rate after a sleep apnea episode may be larger in magnitude than the naturally occurring increase in heart rate observed in the normal phenomenon of cyclic heart rate variability (CVHR). Therefore, both the reduced oxygen supply during a sleep apnea episode and the hyperventilation after sleep apnea can lead to cardiac exertion levels exceeding normal levels.

[0017] Abnormal oxygenation associated with sleep apnea can have adverse effects on various systems and vital organs. Frequent compensatory blood output to combat chronic sleep apnea, and increased heart rate to accommodate subsequent hyperventilation, can lead to increased cardiac expenditure, thereby increasing the likelihood of heart disease or potential heart failure. Some existing monitoring systems rely on respiratory measurement data to detect sleep apnea episodes. For example, algorithms based on cardiac signals have been used to detect sleep apnea episodes. However, some sleep apnea algorithms may be inaccurate (e.g., with significant false positive error rates), which can lead to inadequate treatment of sleep apnea.

[0018] The techniques described in this disclosure enable medical monitoring systems or therapy delivery systems to accurately detect and potentially combat sleep apnea episodes using heart-related data collection. For example, in one example, the sensing circuitry of the medical monitoring system can sense cardiac signals indicative of a patient's cardiac activity. Additionally, the processing circuitry of the medical monitoring system can determine a short-term average and a long-term average of the patient's heart rate based on the cardiac signals, wherein the short-term average is based on fewer heartbeats than the long-term average.

[0019] The processing circuitry can determine the start of a heart rate cycle based on the first moment when the short-term average of the patient's heart rate changes from below the long-term average to above the long-term average. Furthermore, the processing circuitry can determine the end of a heart rate cycle based on a second moment when the short-term average of the patient's heart rate changes from below the long-term average to above the long-term average.

[0020] Additionally, the processing circuitry can determine a series of heart rate variability (HRV) values. For example, the processing circuitry can determine the peak-to-trough heart rate variability value (e.g., the maximum difference between the maximum short-term average heart rate and the minimum short-term average heart rate during the heart rate cycle) based on heartbeats detected during the heart rate cycle. That is, the peak-to-trough heart rate variability value can be the maximum HRV value among a series of HRV values ​​for the heart rate cycle. The processing circuitry can also determine the peak-to-trough time interval, which is the time interval between the maximum short-term average heart rate and the minimum short-term average heart rate during the heart rate cycle.

[0021] The processing circuitry system can also determine whether one or more of a plurality of conditions are met for one or more heart rate cycles. These conditions may include a peak-to-trough time interval condition, i.e., a peak-to-trough time interval greater than a lower threshold and less than an upper threshold. The processing circuitry system can determine, at least in part, that a patient has experienced a sleep apnea episode based on the satisfaction of one or more of these conditions for one or more heart rate cycles. Furthermore, the processing circuitry system can generate an indication that a patient has experienced a sleep apnea episode. Therefore, by utilizing cardiac-related data collection to determine the satisfaction of one or more of these conditions, medical monitoring systems or therapy delivery systems can accurately detect and potentially combat sleep apnea episodes.

[0022] For example, Figure 1 This is a conceptual diagram illustrating an example medical device system 100A in conjunction with a patient 102A. Medical device system 100A is an example of medical device system 100, configured to implement the techniques described herein for detecting sleep apnea episodes and for responsively providing an indication that a sleep apnea episode has been detected. Figure 1 In the example, medical device system 100A may include medical device 106A and external device 108A. Medical device 106A may be an implantable medical device (IMD), such as an insertable cardiac monitor (ICM). Figure 2In the example, medical device system 100B includes medical device 106B. Medical device 106B can be another implantable medical device, such as an implantable pacemaker. This disclosure may refer to medical device 106A or medical device 106B as "medical device 106". In some examples, medical device 106 is an external medical device.

[0023] Medical device 106A is capable of sensing and recording ventricular electrocardiography (EGM) signals from a location external to the heart 104A. In some examples, medical device 106A may include or be coupled to one or more additional sensors that generate one or more other physiological signals, such as signals that vary based on cardiac motion and / or sound, blood pressure, blood flow, blood oxygenation, or respiration. Medical device 106A may be implanted outside the patient's chest cavity, such as subcutaneously or under muscle, for example... Figure 2 The chest location is shown. In some examples, the medical device 106A may employ Reveal LINQ. TM In the form of ICM.

[0024] External device 108A may be a computing device that communicates with medical device 106A via wireless telemetry (e.g., for use in a home, outpatient, clinic, or hospital environment). External device 108A may be coupled to a remote patient monitoring system, such as... External device 108A may be, for example, a programmer, an external monitor, or a consumer device (e.g., a smartphone). External device 108A (e.g., when configured as a programmer for medical device 106A) can be used to program commands or operating parameters into medical device 106A to control its operation. External device 108A can be used to query medical device 106A to retrieve data, including device operating data and physiological data accumulated in memory. Querying can be automatic, such as according to a schedule, or in response to commands from a remote or local user. Programmers, external monitors, and consumer devices are examples of external devices 30A that can be used to query medical device 106A. Examples of communication technologies used by medical device 106A and external device 108A may include tissue conductance communication (TCC) or radio frequency (RF) telemetry (which may be via… (or an RF link established by a Medical Implant Communication Service (MICS)). The medical device system 100A may also include an implantable sensing device 110, also known as a sensor device 110.

[0025] External device 108A can communicate wirelessly with medical device 106A, for example, to program the functions of the ICM and to retrieve recorded physiological signals and / or patient parameter values ​​or other data derived from such signals from the ICM. Both medical device 106A and external device 108A may include processing circuitry systems, and the processing circuitry systems of any one, both, or any other device included in medical device system 100A can perform the techniques described herein, such as determining patient parameter values ​​over a period of time and determining whether one or more patient parameter values ​​indicate a sleep apnea episode.

[0026] Based on the analysis of a patient's cardiac activity, the processing circuitry of one or more devices can also be configured, for example, via an external device 108A to provide an indication that a possible sleep apnea episode has been detected to a user (e.g., to a clinician and / or patient 102A). For example, the medical device system 100A can store (e.g., record) data associated with the occurrence of a possible sleep apnea episode in a memory included in the medical device system 100A (e.g., such as...). Figure 3 The data may be included in (e.g., the memory of medical device 106A or external device 108A, or any other memory included in medical device system 100A, such as memory included in a server). Medical device system 100A may then automatically (e.g., at a predetermined time of day) present data (e.g., as part of a report or history of patient 102A) in response to input from patient 102A or another person. For example, patient 102A or another person may press a button to cause medical device system 102A to present data in a visual form (e.g., displaying data via a monitor), an audio form (e.g., emitting a sound), a tactile form (e.g., vibration or vibration pattern), and / or any other form suitable for conveying information to patient 102A. Patient 102A, a clinician, or another implanted or external medical device may use the data to deliver or take preventative measures to prevent additional sleep apnea episodes.

[0027] Medical device 106A can monitor physiological parameters indicating a patient's status, such as heart rate, HRV value, peak-to-trough time interval, activity count, peak-to-trough heart rate variation, cycle length, etc. Medical device 106A can measure physiological parameter values ​​continuously or at specific times during the day and / or night. In some examples, sensor device 110 may be part of sensor assembly 106A. Each of sensor device 110 and medical device 106A may include a timer and processing circuitry configured to determine the time of day based on timer values. If sensor device 110 determines that the current time is within a predetermined window that can be stored in the memory of sensor device 110, sensor device 110 can measure the patient's physiological parameter values ​​and transmit them to medical device 106A.

[0028] In some examples, sensor device 110 may include a wireless communication circuitry system configured to receive a trigger signal from medical device 106A. The pressure sensing circuitry of sensor device 110 may be configured to measure physiological parameter values ​​of a patient in response to receiving the trigger signal. In this way, medical device 106A may specify that sensor device 110 measures physiological parameter values, and that sensor device 110 may enter a low-power mode, such as a sleep mode, until the wireless communication circuitry of sensor device 110 receives the trigger signal. Medical device 106A may transmit the physiological parameter data acquired by medical device 106A (e.g., heart rate, HRV value, peak-to-trough time interval, activity count, peak-to-trough heart rate change value, cycle length, etc.) to external device 108A. Medical device 106A may also transmit the physiological parameter measurement results received from sensor 110 to external device 108A.

[0029] Although not in Figure 1 The examples shown herein, but medical device systems configured to implement the techniques described in this disclosure may include one or more implanted or external medical devices in addition to or in place of medical device 106A. For example, the medical device system may include a vascular ICD, an extravascular ICD, a pacemaker implanted outside the heart 104A but coupled to an intracardiac or epicardial lead, or an intracardiac pacing device. One or more such devices may generate signals and include processing circuitry configured to perform, in whole or in part, the techniques described herein to detect sleep apnea episodes. The implanted devices may communicate with each other and / or with external device 108A, and one of the implanted or external devices may ultimately determine whether sleep apnea has been detected based on information received from the other device.

[0030] In various examples, the implantable medical device (IMD) component may be connected to a lead extending into the heart 104A or may be fully implanted into the heart 104A. In some examples, components of the medical device system 100A may be external devices. Components of the medical device system 100A may be configured to detect electrocardiographic signals, such as ECG. In various examples, the processing circuitry of the medical device system 100A (e.g., the processing circuitry of the medical device 106A and / or the external device 108A) may use various types of sensing circuitry (e.g., the sensing circuitry of the medical device 106A and / or the sensing circuitry of the sensor device 110 capable of detecting cardiac depolarization or contraction timing) to perform the techniques of this disclosure. Therefore, aspects of the medical device system 200A may use various types of measurements to detect sleep apnea episodes, such as cardiac cycle measurements sensed by the medical device 106A and / or pressure-based readings sensed by the sensor device 110.

[0031] Figure 2 This is a conceptual diagram illustrating an example medical device system 100B in conjunction with a patient 102B. Medical device systems 100A and 100B (either of which may be referred to as "medical device system 100") are examples of medical device systems configured to implement the techniques described herein for detecting sleep apnea attacks based on whether one or more conditions (e.g., peak-to-trough time interval, activity count, peak-to-trough heart rate variability, cycle length, etc.) are met. In some examples, upon identifying a possible sleep apnea attack, components of medical device system 100B may also responsively provide an indication that a sleep apnea attack may be occurring, and / or deliver a therapy configured to prevent, mitigate, or remedy the consequences of a sleep apnea attack. In the illustrated example, medical device system 100B includes a medical device 106B coupled to ventricular leads 202 and atrial leads 204.

[0032] Ventricular lead 202 and atrial lead 204 can be electrically coupled to medical device 106B and extend into the patient's heart 104B. Ventricular lead 202 may include electrodes 206 and 208, shown as leads positioned in the patient's right ventricle (RV) for sensing EGM signals and pacing in the RV. Atrial lead 204 may include electrodes 210 and 212, positioned as leads in the patient's right atrium (RA) for sensing atrial EGM signals and pacing in the RA.

[0033] Medical device 106B can use both ventricular lead 202 and atrial lead 204 to acquire electrocardiographic (EGM) signals from the heart 104B of patient 102B. Medical device system 100B is shown as having a dual-chamber IMD configuration; however, other examples may include one or more additional leads, such as a coronary sinus lead extending into the right atrium, through the coronary sinus, and into a cardiac vein, to position electrodes along the left ventricle (LV) for sensing LV EGM signals and delivering pacing pulses to the LV. In other examples, the medical device system may be a single-chamber system or may otherwise exclude atrial lead 204.

[0034] Processing circuitry, sensing circuitry, and other circuitry configured to perform the techniques described herein may be housed within a sealed housing 200 of the medical device 106B. The housing 200 (or a portion thereof) may be conductive to serve as electrodes for pacing or sensing. The medical device 106B may acquire signal data (e.g., EGM signal data) and cardiac rhythm occurrence data, and transmit this data to an external device 108B. The external device 108B may be a computing device, such as one used in a home, outpatient, clinic, or hospital setting, including processing circuitry and / or communication interface circuitry configured to communicate with the medical device 106B via wireless telemetry. The external device 108B may be coupled to a remote patient monitoring system, such as… In many examples, the external device 108B may include, may be a programmer, an external monitor, or a consumer device (e.g., a smartphone), or may be part of them.

[0035] External device 108B can be implemented and operated in a manner similar to external device 108A. For example, external device 108B (e.g., when configured as a programmer for medical device 106B) can be used to program commands or operating parameters into medical device 106B to control its functions, and can be used to query medical device 106B to retrieve data, including device operating data and physiological data accumulated in the memory of medical device 106B. Medical device system 100B is an example of a medical device system operable to detect sleep apnea episodes using physiological parameter values. For example, medical device system 100B can be configured to monitor physiological parameter values ​​and determine whether one or more physiological parameter values ​​meet one or more conditions. In some examples, if medical device system 100B determines that a sleep apnea episode may have occurred, medical device system 100B can responsively provide an indication that an event has been detected, and optionally trigger the delivery of a therapy configured to remedy the consequences of the event or prevent the development of such consequences. Such technology can be performed individually or jointly by the processing system of the medical device system 100B (such as the processing system of one or both of the medical device 106B and the external device 108B).

[0036] The processing circuitry of external device 108B and / or medical device 106B can determine the values ​​of at least some patient parameters based on signals generated by the sensing circuitry of medical device 106B. In some examples, medical device 106B may include or be coupled to one or more additional sensors that generate one or more other physiological signals, such as signals that vary based on blood flow or respiration. The processing circuitry of external device 108B and / or medical device 106B can determine patient parameters based on therapies delivered by various components of medical device system 2B (such as CPAP machines). For ease of illustration, from... Figure 2 Various components of the medical device system 2B are omitted. For example, the processing circuitry of the external device 108B and / or the medical device 106B can analyze cardiac activity information to determine whether a sleep apnea episode has been effectively remedied by a therapy delivered by the positive airway pressure machine or the medical device 106B.

[0037] Figure 3 This is a functional block diagram illustrating an example configuration of medical device 106. Medical device 106 can correspond to... Figure 1 Medical device 106A in Figure 2 The medical device 106B shown in the disclosure, or another medical device configured to implement the technology described in this disclosure. Similarly, although Figure 3 Not shown in the image, but external device 108 can correspond to... Figure 1 External device 108A in Figure 2The external device 108B shown in the disclosure may be another external device configured to implement the technology as described in this disclosure.

[0038] In the illustrated example, medical device 106 includes a processing circuitry 302, a memory 314, a sensing circuitry 304, a therapy delivery circuitry 306, one or more sensors 308 (e.g., an accelerometer 310), a communication circuitry 312, and a timer 316. However, in some examples, medical device 106 may not include all of these components, or in some examples, medical device 106 may include additional components. For example, in some examples, medical device 106 may not include the therapy delivery circuitry 306.

[0039] Memory 314 may include computer-readable instructions that, when executed by the processing circuitry system, cause the medical device 106 and the processing circuitry system to perform various functions attributable to the medical device 106 and the processing circuitry system herein (e.g., determining the time of day, comparing the time of day with a predetermined window, causing the communication circuitry system 312 to transmit physiological parameter values ​​to an external device, etc.). Memory 314 may include any volatile, non-volatile, magnetic, optical, or electrical medium, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other digital or analog medium. Memory 314 may store thresholds for peak-to-trough time interval status, activity count status, peak-to-trough heart rate variability status, cycle length status, etc. Memory 314 may also store data indicating the measurement results of physiological parameter values ​​received from sensing device 110.

[0040] Processing circuitry system 302 may include fixed functional circuitry systems and / or programmable processing circuitry systems. Processing circuitry system 302 may include any or more of a microprocessor, controller, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or equivalent discrete or analog logic circuitry systems. In some examples, processing circuitry system 302 may include multiple components (such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs) and other discrete or integrated logic circuitry systems. The functionality attributed herein to processing circuitry system 302 may be embodied in software, firmware, hardware, or any combination thereof. For example, processing circuitry system 302 may be the processing circuitry system of medical device 106 or external device 108, or any other processing circuitry system included in medical device system 100 may be configured to perform techniques according to this disclosure, such as determining patient parameter values ​​over a period of time and determining whether one or more patient parameter values ​​indicate a sleep apnea episode.

[0041] The sensing circuit system 304 and the therapy delivery circuit system 306 are coupled to the electrode 300. Figure 3 The electrode 300 shown in the image can correspond to, for example, a medical device system 200B. Figure 2 Electrodes carried on leads 202 and / or 204 of the electrodes 300. The sensing circuitry 304 can monitor signals from selected two or more electrodes 300 to monitor the electrical activity, impedance, or other electrical phenomena of the heart. Sensing of cardiac electrical signals can be performed to determine heart rate or HRV or to detect arrhythmias (e.g., tachyarrhythmias or bradycardia) or other electrical signals. In some examples, the sensing circuitry 304 may include one or more filters and amplifiers for filtering and amplifying signals received from the electrodes 300. In some examples, the sensing circuitry 304 can sense or detect physiological parameters such as heart rate, blood pressure, respiration, etc.

[0042] The obtained cardiac electrical signal can be transmitted to a cardiac event detection circuit system, which detects a cardiac event when the cardiac electrical signal crosses a sensing threshold. The cardiac event detection circuit system may include a rectifier, a filter and / or an amplifier, a sensing amplifier, a comparator and / or an analog-to-digital converter.

[0043] The sensing circuitry 304 may also include a switching module to select which of the available electrodes 300 (or electrode polarities) is used to sense cardiac activity. In an example having several electrodes 300, the processing circuitry 302 may select the electrode that acts as the sensing electrode via the switching module within the sensing circuitry 304, i.e., select the sensing configuration. The sensing circuitry 304 may also transmit one or more digitized EGM signals to the processing circuitry 302 for analysis, such as for cardiac activity differentiation (e.g., heart rhythm differentiation).

[0044] exist Figure 3 In the example, medical device 106 includes one or more sensors 308 coupled to sensing circuitry 304. Although in Figure 3 The sensor 308 is shown as being included within the medical device 106, but one or more sensors 308 may be external to the medical device 106, for example, coupled to the medical device 106 via one or more leads, or configured to communicate wirelessly with the medical device 106. In some examples, the sensor 308 converts signals indicative of patient parameters, which may be amplified, filtered, or otherwise processed by the sensing circuitry system 304. In such examples, the processing circuitry system 302 determines physiological parameter values ​​based on the signals. In some examples, the sensor 308 determines physiological parameter values ​​and transmits them to the processing circuitry system, for example, via a wired or wireless connection.

[0045] In some examples, sensor 308 includes one or more accelerometers 310, such as one or more triaxial accelerometers. Signals generated by the one or more accelerometers 310 may indicate, for example, heart sounds or other vibrations or movements associated with the heartbeat, or coughing, rales, or other respiratory abnormalities. Accelerometers 310 may generate signals and transmit these signals to processing circuitry 302 to make a determination that the heart 104 has contracted. In some examples, sensor 308 may include one or more microphones configured to detect heart sounds or respiratory abnormalities. In some examples, sensor 308 may include sensors configured to convert signals indicating blood flow, blood oxygen saturation, or patient temperature, and processing circuitry may determine patient parameter values ​​based on these signals.

[0046] Therapy delivery circuitry 306 is configured to generate and deliver electrical therapy to the heart. Therapy delivery circuitry 306 may include one or more pulse generators, capacitors, and / or other components capable of generating and / or storing energy to deliver pacing therapy, defibrillation therapy, cardioversion therapy, other therapies, or combinations thereof. In some cases, therapy delivery circuitry 306 may include a first set of components configured to provide pacing therapy and a second set of components configured to provide anti-tachyarrhythmic shock therapy. In other cases, therapy delivery circuitry 306 may utilize the same set of components to provide both pacing and anti-tachyarrhythmic shock therapy. In still other cases, therapy delivery circuitry 306 may share some pacing and shock therapy components, while using only other components for pacing or shock delivery.

[0047] The therapy delivery circuitry 306 may include a charging circuitry, one or more charge storage devices (such as one or more capacitors), and a switching circuitry to control when the one or more capacitors are discharged to the electrodes 300 and the pulse width. The therapy delivery circuitry 306 may perform charging the capacitors to a programmed pulse amplitude and discharging the capacitors to a programmed pulse width based on control signals received from a processing circuitry, which provides the control signals based on parameters stored in a memory 314. The processing circuitry can control the therapy delivery circuitry 306 to deliver the generated therapy to the heart, for example, via one or more combinations of electrodes 300, based on parameters stored in the memory 314. The therapy delivery circuitry 306 may include a switching circuitry to select, for example, which available electrode of the available electrodes 300 is used to deliver the therapy when controlled by the processing circuitry.

[0048] The communication circuitry 312 may include any suitable hardware, firmware, software, or any combination thereof for communicating with another device (such as external device 108) or another IMD or sensor. Under the control of the processing circuitry, the communication circuitry 312 can receive downlink telemetry from external device 108 or another device and transmit uplink telemetry to external device 108 or another device via an antenna, which may be internal and / or external. In some examples, the communication circuitry 312 may communicate with a local external device, and the processing circuitry may communicate via a local external device and a computer network (such as Medtronic). (Network) communicates with networked computing devices.

[0049] Clinicians or other users can retrieve data from medical device 106 using external device 108A or another local or networked computing device configured to communicate with processing circuitry via communication circuitry 312. Clinicians can also use external device 108A or another local or networked computing device to program parameters of medical device 106.

[0050] The communication circuitry 312 can also be configured to communicate with the implantable pressure sensing device 110. The processing circuitry can receive measured physiological parameter values ​​from the sensing device 110 via the communication circuitry 312. In some examples, the processing circuitry can send a trigger signal to the sensing device 110 via the communication circuitry 312 to control the sensing device to measure physiological parameter values ​​in response to the trigger signal.

[0051] Although Figure 3 Not shown, however, the communication circuitry 312 can be coupled to the electrode 300 for tissue conductance communication (TCC) via the electrode. In some examples, communication with the medical device 106 and the external device 108 can be via RF telemetry or TCC. In one example, the communication circuitry 312 can be configured for RF telemetry communication with the external device 108 and TCC communication with the sensing device 110.

[0052] Medical device 106 and / or external device 108 may include a timer 316. Timer 316 may be configured to provide a timer value that the processing circuitry system can use to measure and determine various physiological parameters relating to duration (e.g., HRV, cycle length, etc.). Alternatively, processing circuitry system 302 may determine the time of day based on the timer value. For example, processing circuitry system 302 may be configured to determine the time of day based on the timer value. Processing circuitry system 302 may correlate a patient's cardiac activity with the time of day in which cardiac activity occurs. For example, processing circuitry system may correlate one or more sleep apnea episodes with the time of day in which the sleep apnea episode occurred and store this information in memory 314, which may be retrieved, for example, during interrogation by external device 108.

[0053] Figure 4 It is a line graph 400 showing the patient's sleep disturbance heart rate (SDHR) information. Figure 4 In this context, the RR interval corresponds to the interval between two consecutive R waves. In the example of line graph 400, the patient's SDHR exhibits characteristics of different physiological states at different times. For example, the processing circuitry of medical device system 100A or 100B (collectively referred to as "medical device system 100") (such as the processing circuitry of medical device 106 and / or external device 108) can analyze the trends shown by the clusters of maximum and minimum values ​​(e.g., "peaks" and "troughs") in line graph 400 to determine whether the patient is exhibiting symptoms of sleep apnea episodes. The processing circuitry of medical device system 100 can analyze these peak-trough clusters to detect possible sleep apnea episodes in the patient. Figure 4 It was identified in the middle.

[0054] For example, even during normal breathing, the heart rate information in line graph 400 shows a type of periodic variable heart rate (CVHR) known as respiratory sinus arrhythmia (RSA). During RSA, the patient's heart rate increases with inspiration (or inhalation) and decreases with exhalation (or exhalation). RSA is associated with a single respiratory cycle; for example, a single heart rate increases with inspiration and a single heart rate decreases with exhalation. RSA in Figure 4 The curve is shown by variations of 408 with higher frequency and lower amplitude.

[0055] on the other hand, Figure 4 The lower frequency and higher amplitude variations in the graphs illustrate a form of CVHR associated with sleep apnea, referred to herein as SDHR. For example, each of regions 402 and 404 illustrates an apnea-awakening cycle and the corresponding decrease and increase in heart rate. In most patients, a decrease in heart rate is observed during each apnea event, followed by an increase in heart rate near the end of the apnea event. The heart rate further increases during subsequent hyperventilation syndrome. In the example of an individual sleep apnea episode associated with region 404 of line graph 400, the increase in heart rate due to voluntary awakening is indicated by peak 406. For example, peak 406 could indicate a reduction experienced by heart 104 when the patient's autonomic nervous system attempts to compensate for the increased oxygen saturation during recovery from a sleep apnea episode. Thus, troughs between such peaks could indicate periods of overexertion of heart 104, resulting from compensatory mechanisms implemented by the patient's autonomic nervous system to counteract the decreased oxygen saturation caused by the sleep apnea episode. Figure 4 As shown, heart rate can increase near the end of a sleep apnea episode (e.g., after 410 pm).

[0056] Furthermore, the processing circuitry of the medical device system 100 can collect and analyze physiological parameter values ​​determined in any way, based on... Figure 4 The line graph 400 shows the characteristics used to identify sleep breathing events.

[0057] Figure 5This is a flowchart illustrating a method by which a medical device system 100 configured to perform the techniques described in this disclosure can detect sleep apnea episodes. According to the techniques of this disclosure, the medical device system 100 can generate a cardiac signal indicative of cardiac activity of a patient 102 via a processing circuitry system 302 in response to a signal sensed by a sensing circuitry system 304 of a medical device 106. As discussed above, the processing circuitry system 302 may be included in the medical device 106, an external device 108, or elsewhere in the medical device system 100. The medical device 106 may include one or more sensors 308 configured to sense signals generated by cardiac activity. The sensing circuitry system 304 may deliver (e.g., send, transmit, etc.) the detected signal to the processing circuitry system 302. The processing circuitry system 302 can then generate (e.g., algorithmically based) a cardiac signal indicative of cardiac activity of the patient 102. In some examples, the cardiac signal may be associated with characteristics of the patient 102's cardiac activity. For example, the cardiac signal may be associated with the wave, interval, duration, and rhythm of the patient 102's cardiac activity.

[0058] The medical device system 100 can further determine a short-term average heart rate and a long-term average heart rate via a processing circuit system 302, the start and end of a heart rate cycle being based on these short-term and long-term average heart rates (502). The short-term average heart rate of patient 102 can be based on a first number of heartbeats, and the long-term average heart rate of patient 102 can be based on a first number of heartbeats, where the first number is less than a second number. For example, the first number of heartbeats can be equal to 3 heartbeats, and the second number of heartbeats can be equal to 120 heartbeats. In such examples, the short-term average heart rate of patient 102 can be based on 3 heartbeats, and the long-term average heart rate of patient 102 can be based on 120 heartbeats. In other examples, the first number of heartbeats can be equal to a value other than 3 (e.g., 4, 5, etc.) and / or the second number of heartbeats can be equal to a value other than 120 (e.g., 119, 130, etc.).

[0059] The medical device system 100 can further calculate the short-term average of the heart rate via the processing circuit system 302, and the long-term average of the heart rate can vary. For example, the short-term average and the long-term average of the heart rate can be the median of a first heartbeat and the median of a second heartbeat, respectively (e.g., the median of 3 heartbeats and the median of 120 heartbeats, respectively). Alternatively, the short-term average and the long-term average of the heart rate can be the average of the first heartbeat and the average of the second heartbeat, respectively (e.g., the average of 3 heartbeats and the average of 120 heartbeats, respectively). Alternatively, the short-term average and the long-term average of the heart rate can be a pattern of the first heartbeat and the second heartbeat (e.g., a pattern of 3 heartbeats and a pattern of 120 heartbeats, respectively). It should be understood that other methods for calculating the short-term average and the long-term average of the heart rate may be appropriate, depending on the circumstances.

[0060] The processing circuitry system 302 of the medical device system 100 can further determine the start and end of the heart rate cycle (504). The heart rate cycle defines a period of time during which the patient 102's cardiac activity is processed by the processing circuitry system 302 to detect the occurrence of sleep apnea episodes. The start of the heart rate cycle can be based on the first time when the short-term average of the patient 102's heart rate changes from less than the long-term average of the patient 102's heart rate to greater than the long-term average of the patient 102's heart rate. For example, if the long-term average of the patient 102's heart rate is a constant 65 beats per minute (BPM), the start of the heart rate cycle can be determined based on the first time when the short-term average of the patient 102's heart rate exceeds 65 BPM. Similarly, the end of the heart rate cycle can be based on the second time when the short-term average of the patient 102's heart rate changes from less than the long-term average of the patient 102's heart rate to greater than the long-term average of the patient 102's heart rate. For example, if the long-term average heart rate of patient 102 is a constant 65 BPM, the end of a heart rate cycle can be determined based on the second time when the short-term average heart rate of patient 102 exceeds 65 BPM.

[0061] The processing circuitry 302 of the medical device system 100 can further determine parameter values ​​(506) of the patient 102 based on cardiac activity occurring during the heart rate cycle. Determining whether the patient 102 has experienced a sleep apnea episode can be based on one or more parameter values.

[0062] The parameter values ​​determined by the processing circuit system 302 may include the peak-to-trough time interval. The peak-to-trough time interval is the time interval between the maximum short-term average heart rate during a heart rate cycle and the minimum short-term average heart rate during the same cycle. For example, if the maximum short-term average heart rate of 75 BPM occurs 30 seconds from a reference point (e.g., the start of a heart rate cycle recording, the start of a heart rate cycle, etc.) and during the heart rate cycle, and the minimum short-term average heart rate of 60 BPM occurs 45 seconds from the reference point and during the heart rate cycle, then the peak-to-trough time interval is equal to 15 seconds.

[0063] The parameter values ​​determined by the processing circuitry system 302 may also include an activity count. The activity count per heart rate cycle indicates the number of time intervals during a heart rate cycle in which the patient 102's movement exceeds a minimum movement threshold. One or more sensors 308 (e.g., an accelerometer 310) can be used to determine the patient 102's movement. For example, the accelerometer 310 can measure the patient's body acceleration, and if the patient's body acceleration exceeds an acceleration threshold corresponding to the minimum movement threshold, the activity count per heart rate cycle can increment by one. Therefore, if, in a single heart rate cycle, the patient's body acceleration exceeds the acceleration threshold by eight times, the minimum movement threshold can also exceed eight times, resulting in an activity count of 8 for the patient 102.

[0064] The parameter values ​​determined by the processing circuitry system may also include the peak-to-trough heart rate variation. The peak-to-trough heart rate variation during a heart rate cycle is the difference between the maximum short-term average heart rate and the minimum short-term average heart rate during the same heart rate cycle. For example, if the maximum short-term average heart rate during a heart rate cycle is 75 BPM and the minimum short-term average heart rate during the same heart rate cycle is 60 BPM, then the peak-to-trough heart rate variation for that heart rate cycle is 15 BPM.

[0065] The parameter values ​​determined by the processing circuit system 302 may also include the cycle length, where the cycle length of the heart rate cycle indicates the length of the heart rate cycle. For example, if the heart rate cycle begins at a first time of 0 seconds (e.g., a first time based on the short-term average of the patient 102's heart rate exceeding the long-term average of the patient 102's heart rate), and the heart rate cycle ends at a second time of 70 seconds (e.g., a second time based on the short-term average of the patient 102's heart rate exceeding the long-term average of the patient 102's heart rate), then the cycle length is 70 seconds.

[0066] In some examples, the processing circuitry 302 can use one parameter value to determine another. For instance, the processing circuitry 302 can use a series of heart rate values ​​to determine the peak-to-trough time interval, which can then be used to determine whether the patient 102 has experienced a sleep apnea episode. For example, if the short-term average heart rate of the patient 102 fluctuates from 70 BPM to 60 BPM, from 60 BPM to 85 BPM, from 85 BPM to 65 BPM, etc., during the heart cycle, then the heart rate values ​​could include 10 BPM (70 BPM minus 60 BPM), 25 BPM (85 BPM minus 60 BPM), 5 BPM (70 BPM minus 65 BPM), etc. In this example, the peak-to-trough variation is 25 BPM because 85 BPM is the maximum short-term average heart rate during the heart cycle, and 60 BPM is the minimum short-term average heart rate during the heart cycle.

[0067] The processing circuit system 302 can then determine the peak-to-trough time interval using the corresponding times when the maximum short-term average of the 85 BPM heart rate value and the minimum short-term average of the 60 BPM heart rate value occur. For example, if the maximum short-term average of the 85 BPM heart rate value occurs 30 seconds from the start of the heart rate cycle, and the minimum short-term average of the 60 BPM heart rate value occurs 60 seconds from the start of the heart rate cycle, then the peak-to-trough time interval is equal to 30 seconds.

[0068] The processing circuitry system 302 of the medical device system 100 can further determine whether one or more parameter values ​​for the patient 102 regarding the heart rate cycle meet one or more of a plurality of conditions. These plurality of conditions may include, but are not limited to, peak-to-trough time interval conditions, activity count conditions, peak-to-trough heart rate variation conditions, cycle length conditions, etc.

[0069] These multiple conditions may include peak-to-valley time interval conditions. A peak-to-valley time interval condition can be one where the peak-to-valley time interval is greater than a lower threshold and less than an upper threshold. For example, if the lower threshold is 5 seconds and the upper threshold is 30 seconds, then if the peak-to-valley time interval is greater than 5 seconds and less than 30 seconds, the processing circuitry 302 can determine that the peak-to-valley time interval condition is met. Therefore, a peak-to-valley time interval of, for example, 10 seconds would meet the peak-to-valley time interval condition. Alternatively, if the peak-to-valley time interval is not greater than 5 seconds and less than 30 seconds, the processing circuitry 302 can determine that the peak-to-valley time interval condition is not met. Therefore, a peak-to-valley time interval of, for example, 3 seconds would not meet the peak-to-valley time interval condition.

[0070] Alternatively, these multiple conditions may include an activity count condition. An activity count condition may be a condition where the activity count for a heart rate cycle is less than an activity count threshold. For example, if the activity count threshold is equal to 8, then if the activity count is less than 8, the processing circuitry 302 can determine that the activity count condition is met. Therefore, an activity count of, for example, 5 would meet the activity count condition. Alternatively, if the activity count is not less than 8, the processing circuitry 302 can determine that the activity count condition is not met. Therefore, an activity count of, for example, 10 would not meet the activity count condition.

[0071] Alternatively, these multiple conditions may include peak-to-trough HRV conditions. A peak-to-trough HRV condition can be a condition where the peak-to-trough HRV value for a heart rate cycle is greater than a lower threshold for peak-to-trough heart rate variation and less than an upper threshold for peak-to-trough heart rate variation. For example, if the lower threshold for peak-to-trough heart rate variation is equal to 6 BPM and the upper threshold for peak-to-trough heart rate variation is equal to 50 BPM, then if the peak-to-trough HRV value is greater than 6 and less than 50, the processing circuitry 302 can determine that a peak-to-trough HRV condition is met. Therefore, a peak-to-trough HRV value of, for example, 25 would meet the peak-to-trough HRV condition. Alternatively, if the peak-to-trough HRV value is not less than 50, the processing circuitry 302 can determine that a peak-to-trough HRV condition is not met. Therefore, a peak-to-trough HRV value of, for example, 60 would not meet the peak-to-trough HRV condition.

[0072] Alternatively, these multiple conditions may include a cycle length condition. Cycle length is the length of a heart rate cycle (e.g., the length of time during which the patient 102's cardiac activity is processed by the processing circuitry system 302 to detect sleep apnea episodes). A cycle length condition can be a condition where the heart rate cycle length is greater than a lower cycle length threshold and less than an upper cycle length threshold. For example, if the lower cycle length threshold is equal to 25 seconds and the upper cycle length threshold is equal to 100 seconds, then if the cycle length is greater than 25 seconds and less than 100 seconds, the processing circuitry system 302 can determine that a cycle length condition is met. Therefore, a cycle length of, for example, 50 seconds would meet the cycle length condition. Alternatively, if the cycle length is not greater than 25 seconds and less than 100 seconds, the processing circuitry system 302 determines that a cycle length condition is not met. Therefore, a cycle length of, for example, 125 seconds would not meet the cycle length condition.

[0073] In some examples, the multiple conditions may include peak-to-trough time interval conditions, activity count conditions, peak-to-trough heart rate variability conditions, and cycle length conditions. In such examples, determining that a patient has experienced a sleep apnea episode may be based at least in part on meeting each of the multiple conditions related to the heart rate cycle.

[0074] In some examples, the medical device system 100 may further determine whether the patient 102 has experienced a sleep apnea episode based on whether one or more of the plurality of conditions are met (508). For example, if each of the plurality of conditions is met (e.g., the peak-to-trough time interval condition is met) (the "yes" branch of 508), the processing circuitry system 302 may determine that the patient 102 has experienced a sleep apnea episode or that the probability that the patient 102 has experienced a sleep apnea episode is not low (510). The processing circuitry system 302 may then cause an indication that the patient 102 has experienced a sleep apnea episode to be generated (512). For example, the processing circuitry system 302 may cause the medical device and / or an external device to generate an indication that the patient 102 has experienced a sleep apnea episode.

[0075] Alternatively, if one or more conditions are not met (the "No" branch of 508), the processing circuitry 302 may determine that the probability that the patient 102 has not experienced a sleep apnea episode or has experienced a sleep apnea episode is low (514). The processing circuitry 302 may then cause an indication that the patient 102 has not experienced a sleep apnea episode to be generated (516). For example, the processing circuitry 302 may cause a medical device and / or an external device to generate an indication that the patient 102 has not experienced a sleep apnea episode. Alternatively, in some examples, if the patient 102 has not experienced a sleep apnea episode, the processing circuitry 302 does not generate an indication that the patient 102 has experienced a sleep apnea episode.

[0076] The processing circuitry system 302 can generate an indication that the patient 102 has experienced a sleep apnea episode. For example, the processing circuitry system 302 can cause a medical device or external device to output an indication that the patient 102 has experienced a sleep apnea episode. External devices may include one or more cellular phones, smartphones, satellite phones, laptops, tablets, wearable devices, computer workstations, personal digital assistants, handheld computing devices, virtual reality headsets, or any other device capable of outputting an indication that the patient 102 has experienced a sleep apnea episode. External devices may automatically (e.g., at a predetermined time of day) output the indication and / or output the indication in response to input from the patient 102 (as part of a patient report or history), and may output the indication in the form of audible notifications, visual notifications, tactile notifications (e.g., vibration or vibration patterns), text prompts, button prompts, and / or any other notification that may indicate to the patient 102 that the patient 102 has experienced a sleep apnea episode.

[0077] Although Figure 5Not shown, but the processing circuitry 302 can determine a confidence level, such as probability, that the patient 102 has experienced a sleep apnea episode based on the number of conditions among multiple conditions that are met. For example, if only one condition is met, the processing circuitry 302 can determine that the confidence level of the patient 102 having experienced a sleep apnea episode is low. In another example, if two conditions are met, the processing circuitry 302 can determine that the confidence level of the patient 102 having experienced a sleep apnea episode is moderate. In yet another example, if three or more conditions are met, the processing circuitry 302 can determine that the confidence level of the patient 102 having experienced a sleep apnea episode is high.

[0078] Although Figure 5 Not shown, but the medical device system 100 can determine that the patient 102 has experienced a sleep apnea episode based on one or more conditions satisfied by at least a minimum non-zero number of additional heart rate cycles other than the current heart rate cycle occurring within a predetermined time interval (e.g., time duration) of the current heart rate cycle. For example, if the minimum number of additional heart rate cycles is equal to 2 and the predetermined time interval is equal to 240 seconds, the processing circuit system 302 can determine that the patient 102 has experienced a sleep apnea episode based on one or more conditions satisfied by two additional heart rate cycles other than the current heart rate cycle occurring within 240 seconds of the current heart rate cycle (e.g., peak-to-trough time interval condition, activity count condition, peak-to-trough heart rate change condition, cycle length condition, etc.). Therefore, based on the activity count condition satisfied by a heart rate cycle with a time interval of 220 seconds of the current heart rate cycle and the peak-to-trough heart rate change condition satisfied by a heart rate cycle with a time interval of 75 seconds of the current heart rate cycle, the processing circuit system 302 can determine that the patient 102 has experienced a sleep apnea episode. Alternatively, based on the fact that a heart rate cycle with a time interval of 300 seconds satisfies the activity count condition and a heart rate cycle with a time interval of 75 seconds satisfies the peak-to-trough heart rate change condition, the processing circuitry 302 can determine that the patient 102 has not experienced a sleep apnea episode because no heart rate cycles with a time interval of 300 seconds have occurred within the predetermined time interval of 240 seconds. Additionally or alternatively, in the same example, the processing circuitry 302 can determine that the patient 102 has not experienced a sleep apnea episode because one or more conditions are not satisfied by two or more additional heart rate cycles (e.g., a condition is satisfied by only one additional heart rate cycle because only the activity count condition is satisfied by only one heart rate cycle with a time interval of 75 seconds from the current heart rate cycle).

[0079] The reference point used to determine the time interval can vary. For example, the reference point can be the start of a heart rate cycle, the end of a heart rate cycle, or any point between the start and end of a heart rate cycle. In some examples, the direction of the time interval can vary. That is, the direction of the time interval can be retrospective to allow assessment of cardiac activity that has already occurred, prospective to allow assessment of cardiac activity that will occur, or a combination of both.

[0080] For example, if the reference point used to determine the time interval is the start of a heart rate cycle and the predetermined time interval is 240 seconds, the processing circuitry 302 can determine that the patient 102 has experienced a sleep apnea episode based on cardiac activity occurring within any heart rate cycle prior to the start of the current heart rate cycle with a time interval of 240 seconds or less. In another example, the processing circuitry 302 can determine that the patient 102 has experienced a sleep apnea episode based on cardiac activity occurring within any heart rate cycle after the start of the current heart rate cycle with a time interval of 240 seconds or less. In yet another example, the processing circuitry 302 can determine that the patient 102 has experienced a sleep apnea episode based on cardiac activity occurring both before and after the start of the current heart rate cycle, provided that cardiac activity meeting one or more conditions occurs within a time interval of 240 seconds or less.

[0081] In another example, if the reference point used to determine the time interval is the end of a heart rate cycle and the predetermined time interval is 240 seconds, the processing circuitry 302 can determine that the patient 102 has experienced a sleep apnea episode based on cardiac activity occurring within any heart rate cycle prior to the end of the current heart rate cycle, with a time interval of 240 seconds or less. Alternatively, the processing circuitry 302 can determine that the patient 102 has experienced a sleep apnea episode based on cardiac activity occurring within any heart rate cycle after the end of the current heart rate cycle, with a time interval of 240 seconds or less. Alternatively, the processing circuitry 302 can determine that the patient 102 has experienced a sleep apnea episode based on cardiac activity occurring within any heart rate cycle prior to the end of the current heart rate cycle with a time interval of 120 seconds or less, and cardiac activity occurring within any heart rate cycle after the end of the current heart rate cycle with a time interval of 120 seconds or less.

[0082] In another example, if the reference point used to determine the time distance is the midpoint of the heart rate cycle and the predetermined time distance is 240 seconds, the processing circuitry 302 can determine that the patient 102 has experienced a sleep apnea episode based on cardiac activity occurring within any heart rate cycle preceding the midpoint of the current heart rate cycle with a time distance of 240 seconds or less. Alternatively, the processing circuitry 302 can determine that the patient 102 has experienced a sleep apnea episode based on cardiac activity occurring within any heart rate cycle following the midpoint of the current heart rate cycle with a time distance of 240 seconds or less. Alternatively, the processing circuitry 302 can determine that the patient 102 has experienced a sleep apnea episode based on cardiac activity occurring within any heart rate cycle preceding the midpoint of the current heart rate cycle with a time distance of 120 seconds or less, and within any heart rate cycle following the midpoint of the current heart rate cycle with a time distance of 120 seconds or less.

[0083] As used herein, implantable medical devices (IMDs) can include, be part of, various devices or integrated systems, such as, but not limited to, implantable cardiac monitors (ICMs), implantable pacemakers (including those for delivering biventricular resynchronization therapy (CRTs)), implantable cardioverter defibrillators (ICDs), diagnostic devices, cardiac devices, etc. Various examples, including the use of cardiac cycle length measurements to detect sleep apnea episodes, have been described. Additionally, pulmonary therapies can be provided to reduce the severity of sleep apnea episodes or counteract their consequences. Any combination of sleep apnea episode detection and therapy is considered.

[0084] Various aspects of these technologies can be implemented in one or more processing circuitry systems, including one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic circuitry systems, and any combination of such components, embodied in external devices such as physician or patient external devices, electrical stimulators, or other devices. The term "processing circuitry system" can generally refer to any circuitry system, either alone or in combination with other logic circuitry systems or any other equivalent circuitry system.

[0085] In one or more examples, the functionality described in this disclosure may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functionality may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. The computer-readable medium may include a computer-readable storage medium forming a tangible, non-transitory medium. The instructions may be executed by one or more processing circuitry systems, such as one or more DSPs, ASICs, FPGAs, general-purpose microprocessors, or other equivalent integrated or discrete logic circuitry systems. Therefore, the term "processing circuitry system" as used herein may refer to one or more of any of the foregoing structures or any other structure suitable for implementing the techniques described herein.

[0086] Additionally, in some aspects, the functions described herein can be housed within dedicated hardware and / or software modules. Describing different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be implemented by separate hardware or software components. Rather, the functions associated with one or more modules or units may be performed by separate hardware or software components, or integrated within common or separate hardware or software components. Furthermore, this technology can be fully implemented in one or more circuit or logic elements. The technology disclosed herein can be implemented in a wide variety of devices or apparatuses, including IMDs, external devices, combinations of IMDs and external devices, integrated circuits (ICs), or a set of ICs and / or discrete circuit systems located within IMDs and / or external devices.

Claims

1. A method for detecting sleep apnea episodes, the method comprising: The system uses a sensing circuitry to detect cardiac signals that indicate the patient's heart activity. The short-term average and long-term average of the patient's heart rate are determined by a processing circuit system based on the cardiac signals, wherein the short-term average of the patient's heart rate is based on fewer heartbeats than the long-term average of the patient's heart rate; The processing circuit system determines the start of a heart rate cycle based on the first moment when the short-term average of the patient's heart rate changes from less than the long-term average of the patient's heart rate to greater than the long-term average of the patient's heart rate; The processing circuit system determines that the heart rate cycle has ended based on a second time when the short-term average of the patient's heart rate changes from less than the long-term average of the patient's heart rate to greater than the long-term average of the patient's heart rate; The peak-to-trough time interval is determined by the processing circuit system. The peak-to-trough time interval is the time interval between the maximum short-term average of the heart rate during the heart rate cycle and the minimum short-term average of the heart rate during the heart rate cycle. The processing circuit system determines whether one or more of a plurality of conditions for the heart rate cycle are met, the plurality of conditions including the peak-to-trough time interval condition, wherein the peak-to-trough time interval is greater than a lower threshold threshold and less than an upper threshold threshold threshold. The processing circuitry determines, at least in part, that the patient has experienced a sleep apnea episode based on one or more conditions related to the heart rate cycle. as well as The processing circuitry system generates an indication that the patient has experienced a sleep apnea episode.

2. The method according to claim 1, wherein, The plurality of conditions also includes an activity count condition where the activity count for the heart rate cycle is less than an activity count threshold, wherein the activity count for the heart rate cycle indicates the number of time intervals during which the patient's exercise intensity is greater than a minimum exercise threshold during the heart rate cycle, and the method further includes: The processing circuitry system determines the activity count for the heart rate cycle; and The processing circuit system determines whether the activity count for the heart rate cycle is less than the activity count threshold.

3. The method according to claim 2, wherein, The activity count threshold is equal to 8.

4. The method according to claim 1, wherein, The heart rate cycle is the current heart rate cycle, and determining that the patient has experienced the sleep apnea episode further includes: determining that the patient has experienced the sleep apnea episode by means of the processing circuitry based on at least a minimum number of non-zero additional heart rate cycles occurring within a predetermined time interval of the current heart rate cycle, in addition to the current heart rate cycle, satisfying one or more conditions.

5. The method according to claim 4, wherein, At least one of the predetermined time intervals is 240 seconds, or the minimum number of additional heart rate cycles is equal to 2.

6. The method according to claim 1, wherein, The plurality of conditions also includes peak-to-trough heart rate variation, wherein the peak-to-trough heart rate variation value for the heart rate cycle is greater than a lower threshold for peak-to-trough heart rate variation and less than an upper threshold for peak-to-trough heart rate variation, wherein the peak-to-trough heart rate variation value for the heart rate cycle indicates the difference between the maximum short-term average of the heart rate during the heart rate cycle and the minimum short-term average of the heart rate during the heart rate cycle, and the method further includes: The processing circuit system determines the peak-to-trough heart rate variation value for the heart rate cycle; and The processing circuit system determines whether the peak-to-trough heart rate change value is greater than the lower threshold of the peak-to-trough heart rate change and less than the upper threshold of the peak-to-trough heart rate change.

7. The method according to claim 6, wherein, At least one of the following: the lower threshold for peak-to-trough heart rate variation is equal to 6 heartbeats per minute (BPM); and the upper threshold for peak-to-trough heart rate variation is equal to 50 BPM.

8. The method according to claim 1, wherein, The plurality of conditions also includes a cycle length condition, wherein the cycle length of the heart rate cycle is greater than a lower limit threshold and less than an upper limit threshold, wherein the cycle length of the heart rate cycle indicates the length of the heart rate cycle, and the method further includes: The cycle length is determined by the processing circuit system; and The processing circuit system determines whether the cycle length is greater than the lower limit threshold of the cycle length and less than the upper limit threshold of the cycle length.

9. The method according to claim 1, wherein, The method further includes: Activity count status: The activity count for the heart rate cycle is less than the activity count threshold, wherein the activity count for the heart rate cycle indicates the number of time intervals during which the patient's exercise volume is greater than the minimum exercise threshold during the heart rate cycle; Peak-to-trough heart rate variation: The peak-to-trough heart rate variation value for the heart rate cycle is greater than a lower threshold and less than an upper threshold, wherein the peak-to-trough heart rate variation value for the heart rate cycle indicates the difference between the maximum short-term average of the heart rate and the minimum short-term average of the heart rate during the heart rate cycle; and Cycle length status: The cycle length for the heart rate cycle is greater than the lower limit threshold and less than the upper limit threshold, wherein the cycle length for the heart rate cycle indicates the length of the heart rate cycle.

10. A system comprising: Sensing circuitry system, the sensing circuitry system being configured to: Sensing cardiac signals that indicate the patient's cardiac activity; and Processing circuitry system, the processing circuitry system being configured to: The short-term average and long-term average of the patient's heart rate are determined based on the cardiac signals, wherein the short-term average of the patient's heart rate is based on fewer heartbeats than the long-term average of the patient's heart rate; The heart rate cycle is determined to have started at the first moment when the short-term average of the patient's heart rate changes from less than the long-term average of the patient's heart rate to greater than the long-term average of the patient's heart rate; The heart rate cycle is determined to have ended based on a second time when the short-term average of the patient's heart rate changes from less than the long-term average of the patient's heart rate to greater than the long-term average of the patient's heart rate; Determine the peak-to-trough time interval, which is the time interval between the maximum short-term average of the heart rate during the heart rate cycle and the minimum short-term average of the heart rate during the heart rate cycle; Determine whether one or more of a plurality of conditions for the heart rate cycle are met, wherein one of the plurality of conditions is that the peak-to-trough time interval is greater than a lower threshold for peak-to-trough time and less than an upper threshold for peak-to-trough time; The determination that the patient has experienced a sleep apnea episode is based at least in part on meeting one or more conditions related to the heart rate cycle. as well as Generate an indication that the patient has experienced a sleep apnea episode.

11. The system according to claim 10, wherein, The sensing circuit system and the processing circuit system are included in the medical device.

12. The system according to claim 10, wherein, The plurality of conditions also includes an activity count condition for the heart rate cycle where the activity count is less than an activity count threshold, wherein the activity count for the heart rate cycle indicates the number of time intervals during which the patient's exercise intensity is greater than a minimum exercise threshold, and wherein the processing circuitry is further configured to: Determine the activity count for the heart rate cycle; and Determine whether the activity count for the heart rate cycle is less than the activity count threshold.

13. The system according to claim 12, wherein, The activity count threshold is equal to 8.

14. The system according to claim 10, wherein, The heart rate cycle is the current heart rate cycle, and the processing circuitry is further configured to determine that the patient has experienced the sleep apnea episode based on one or more conditions satisfied by at least a minimum non-zero number of additional heart rate cycles other than the current heart rate cycle occurring within a predetermined time interval of the current heart rate cycle.

15. A computer-readable medium comprising instructions that, when executed, cause: The sensing circuit system receives cardiac signals that indicate the patient's cardiac activity; and Processing circuit system: The short-term average and long-term average of the patient's heart rate are determined based on the cardiac signals, wherein the short-term average of the patient's heart rate is based on fewer heartbeats than the long-term average of the patient's heart rate; The heart rate cycle is determined to have started at the first moment when the short-term average of the patient's heart rate changes from less than the long-term average of the patient's heart rate to greater than the long-term average of the patient's heart rate; The heart rate cycle is determined to have ended based on a second time when the short-term average of the patient's heart rate changes from less than the long-term average of the patient's heart rate to greater than the long-term average of the patient's heart rate; Determine the peak-to-trough time interval, which is the time interval between the maximum short-term average of the heart rate during the heart rate cycle and the minimum short-term average of the heart rate during the heart rate cycle; Determine whether one or more of a plurality of conditions for the heart rate cycle are met, wherein one of the plurality of conditions is that the peak-to-trough time interval is greater than a lower threshold for peak-to-trough time and less than an upper threshold for peak-to-trough time; The determination that the patient has experienced a sleep apnea episode is based at least in part on meeting one or more conditions related to the heart rate cycle. as well as Generate an indication that the patient has experienced a sleep apnea episode.

Images