Detection of sleep disordered breathing

Abstract

A system includes an implantable medical device with sensing circuitry configured to sense cardiac activity of a patient. The system further includes processing circuitry configured to determine an occurrence of a first sleep disordered heart rate (SDHR) event based on a set of heart rate cycle properties for a first heart rate cycle and an occurrence of a second SDHR event based on the set of heart rate cycle properties for a second heart rate cycle. The processing circuitry is configured to confirm the occurrence of at least one of the first SDHR event or the second SDHR event based on a set of event properties for the first SDHR event and for the second SDHR event. The processing circuitry is configured to output that the patient experiences sleep disordered breathing in response to confirming at least one of the first SDHR event or the second SDHR event.

Description

DETECTION OF SLEEP DISORDERED BREATHING

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 586,892, filed September 29, 2023, the entire content of which is incorporated herein by reference.TECHNICAL FIELD

[0002] This disclosure generally relates to techniques for monitoring physiological conditions of a patient and, more particularly, to techniques for detecting sleep disordered breathing.BACKGROUND

[0003] When functioning properly, a heart maintains its own intrinsic rhythm and is capable of pumping adequate blood throughout a circulatory system. This intrinsic rhythm is a function of intrinsic signals generated by the sinoatrial (SA) node located in the upper right atrium. The SA node periodically depolarizes, which in turn causes the atrial heart tissue to depolarize so that right and left atria contract as the depolarization travels through the atrial heart tissue. The atrial depolarization signal is also received by the atrioventricular node, or AV node, which, in turn, triggers a subsequent ventricular depolarization signal that travels through and depolarizes the ventricular heart tissue causing the right and left ventricles to contract.

[0004] A condition known as sleep disordered breathing (e.g., sleep apnea) can diminish cardiac output and pose various risks to patients, particularly those who are susceptible to heart failure. For example, sleep apnea is a sleep disorder that involves the temporary cessation of respiratory airflow during sleep. In various scenarios, sleep apnea may be characterized by one or both of pauses in breathing or periods of shallow breathing during sleep.

[0005] Sleep apnea is generally considered a medical syndrome that occurs in various forms. One recognized form of sleep apnea is “central sleep apnea,” which is associated with a failure of the central nervous system to automatically initiate and control respiration. Another recognized form of sleep apnea is “obstructive sleep apnea,” which is associated with an obstruction of the airways due to airway collapse. Yet anotherrecognized form of sleep apnea is a mixed form that may include a central nervous system failure to drive ventilatory effort combined with an obstructive apnea.

[0006] Possible effects of sleep apnea include daytime sleepiness, impaired alertness, and various associated cardiovascular diseases, which in turn can significantly impair patient lifestyle and increase morbidity risk. In some cases, obstructive sleep apnea can lead to death due to lack of oxygen to vital organs of the body. Various approaches have been taken to detect and treat sleep apnea.SUMMARY

[0007] In general, this disclosure is directed toward techniques for detecting sleep disordered breathing episodes based on cardiac signals. In accordance with techniques of this disclosure, an implantable medical device (IMD) system may include processing circuitry configured to detect sleep disordered heart rate (SDHR) events resulting from sleep disordered breathing episodes. Sleep disordered breathing may have observable effects on heart rate. During a sleep disordered breathing episodes, heart rate may very in a cyclical fashion.

[0008] The processing circuitry may detect SDHR events based on a set of heart rate cycle properties for a first heart rate cycle and a second heart rate cycle. The set of heart rate cycle properties may include one or more of a peak-to-valley heart rate variation, a cycle length, a peak-to-valley time interval, or an activity count. To improve accuracy and specificity, the processing circuitry may be configured to confirm the SDHR events based on a set of event properties for detected SDHR events, such as one or more of a difference between a maximum heart rate and a minimum heart rate, a time difference between maximum and minimum heart rate, the maximum heart rate, or the minimum heart rate. In some examples, to confirm a SDHR event, the processing circuitry may compare the set of event properties for multiple detected SDHR events. Responsive to confirmation, the processing circuitry may output that the patient is experiencing sleep disordered breathing.

[0009] By detecting sleep disordered breathing using a set of heart rate cycle properties and a set of event properties, the techniques may advantageously prevent false- positives from being detected as sleep disordered breathing, thereby improving specificity. Additionally, the techniques may ensure that the processing circuitry does not detect SDHR events during cycles when the patient might be active and moving around (as thoseevents would be noisy and treated as false-positives). Thus, for at least these reasons, the techniques of this disclosure may improve detection of SDHR events, and therapy improve detection of sleep disordered breathing episodes.

[0010] In contrast to conventional techniques, an IMD system may be a cost-effective and practicable solution to sleep disordered breathing detection. For example, sleep studies using overnight polysomnography are generally costly because the studies are performed in hospitals settings and require continuous monitoring and scoring of sleep events by a sleep technician. The studies may involve long wait times due to increased demand, and the patient may also experience discomfort and interrupted sleep due to sleeping in a new setting while being restrained by cables. These factors may affect their measured Apnea- Hypopnea Index (AHI), resulting in potentially inaccurate data. Furthermore, monitoring for a single night for sleep disordered breathing may not be ideal because the severity of sleep disordered breathing in an individual patient is not stable over time but exhibits considerable night-to-night variability that cannot be detected by a single overnight sleep study.

[0011] Thus, by utilizing an IMD, the techniques of this disclosure may address the need for continuous and long-term monitoring of sleep disordered breathing and accurate monitoring of night-to-night variability in AHI in patients with sleep disordered breathing. By continuously monitoring the occurrence of sleep disordered breathing episodes, the IMD may collect valuable data for healthcare professionals to assess. Thus, using an IMD in accordance with the techniques may be advantageous in terms of cost, practicality, accuracy, reliability, continuous monitoring, etc., making the data provided by the IMD clinically significant and useful (e.g., in contrast to data collected using more manual methods that are deficient for at least the reasons described above).

[0012] Furthermore, detection of sleep disordered breathing episodes based on SDHR events in accordance with the techniques of this disclosure may advantageously facilitate use of an IMD for continuous and long-term monitoring of sleep disordered breathing. IMDs, including relatively compact, subcutaneously insertable cardiac monitors, may readily be configured to detect cardiac signals and determine sequential cardiac cycle lengths usable to detect SDHR events.

[0013] In some examples, a system comprises: an implantable medical device comprising sensing circuitry configured to sense cardiac activity of a patient; andprocessing circuitry configured to: determine an occurrence of a first sleep disordered heart rate (SDHR) event within a first heart rate cycle based on a set of heart rate cycle properties for the first heart rate cycle; determine an occurrence of a second SDHR event within a second heart rate cycle based on the set of heart rate cycle properties for the second heart rate cycle; confirm the occurrence of at least one of the first SDHR event or the second SDHR event based on a set of event properties for the first SDHR event and for the second SDHR event; and responsive to confirming the occurrence of at least one of the first SDHR event or the second SDHR event, output an indication that the patient experiences sleep disordered breathing.

[0014] In some examples, an implantable medical device comprises: sensing circuitry configured to sense cardiac activity of a patient; and processing circuitry configured to: determine an occurrence of a first sleep disordered heart rate (SDHR) event within a first heart rate cycle based on a set of heart rate cycle properties for the first heart rate cycle; determine an occurrence of a second SDHR event within a second heart rate cycle based on the set of heart rate cycle properties for the second heart rate cycle; confirm the occurrence of at least one of the first SDHR event or the second SDHR event based on a set of event properties for the first SDHR event and for the second SDHR event; and responsive to confirming the occurrence of at least one of the first SDHR event or the second SDHR event, output an indication that the patient experiences sleep disordered breathing.

[0015] In some examples, a method comprises: sensing, by sensing circuitry of an implantable medical device, cardiac activity of a patient; determining, by processing circuitry, an occurrence of a first sleep disordered heart rate (SDHR) event within a first heart rate cycle based on a set of heart rate cycle properties for the first heart rate cycle; determining, by the processing circuitry, an occurrence of a second SDHR event within a second heart rate cycle based on the set of heart rate cycle properties for the second heart rate cycle; confirming, by the processing circuitry, the occurrence of at least one of the first SDHR event or the second SDHR event based on a set of event properties for the first SDHR event and for the second SDHR event; and responsive to confirming the occurrence of at least one of the first SDHR event or the second SDHR event, outputting, by the processing circuitry, an indication that the patient experiences sleep disordered breathing.

[0016] The details of one or more examples of this disclosure are set forth in the accompanying drawings and in the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF DRAWINGS

[0017] FIG. 1 is a conceptual diagram illustrating an example medical device system in conjunction with a patient, where the medical device system is configured to implement various sleep disordered breathing detection and communication functionalities of this disclosure.

[0018] FIG. 2A is a perspective drawing illustrating an example insertable cardiac monitor, in accordance with techniques of this disclosure.

[0019] FIG. 2B is a perspective drawing illustrating another example insertable cardiac monitor, in accordance with techniques of this disclosure.

[0020] FIG. 3 is a block diagram illustrating an example configuration of a medical device configured to sense and record cardiac electrogram signals of a patient.

[0021] FIG. 4 is a chart illustrating the determination of example heart rate cycles based on short-term and long-term average heart rates.

[0022] FIG. 5 is a chart illustrating example heart rate cycle properties for a heart rate cycle.

[0023] FIG. 6 is a chart illustrating example SDHR events.

[0024] FIG. 7 is a block diagram illustrating an example configuration of an external device, in accordance with techniques of this disclosure.

[0025] FIG. 8 is a block diagram illustrating an example system that includes an external device, such as a server, in accordance with techniques of this disclosure.

[0026] FIG. 9 is a flowchart illustrating a method by which a medical device system that is configured according to techniques of this disclosure may detect a sleep disordered breathing episode.DETAILED DESCRIPTION

[0027] In general, this disclosure is directed to detecting individual episodes of sleep disordered breathing. For example, sleep apnea is a breathing disorder that cuts oxygensupply to various systems and organs of the body. To deal with the reduction in oxygenation levels, organs and systems of the body may trigger one or more compensatory mechanisms. With respect to the cardiovascular system, the compensatory mechanism(s) cause the heart to increase blood output for a period of time. As such, the cardiac compensatory mechanisms cause increased exertion of the heart. Moreover, at the end of a sleep apnea episode and during a recovery period that follows a sleep apnea episode, the patient’s heart rate may increase significantly, due to alveolar hyperventilation caused by the pulmonary system’s compensatory mechanisms. The heart rate spike after a sleep apnea episode may be greater in magnitude naturally occurring heart rate increases that are exhibited by the normal phenomenon of cyclical variation of heart rate (CVHR). As such, both the reduced oxygen supply during a sleep apnea episode and the hyperventilation that follows a sleep apnea episode may cause exertion levels in the heart that exceed normal levels of heart exertion.

[0028] As described herein, a medical monitoring system or therapy delivery system may leverage heart-related data collections to accurately and continuously detect episodes of sleep disordered breathing. In accordance with one or more techniques of this disclosure, FIG. 1 illustrates the environment of an example medical system 2 in conjunction with a patient 4. The example techniques may be used with an IMD 10, which may be in wireless communication with at least one of external device 12 and other devices not pictured in FIG. 1. In some examples, IMD 10 is implanted outside of a thoracic cavity of patient 4 (e.g., subcutaneously in the pectoral location illustrated in FIG. 1). IMD 10 may be positioned near the sternum near or just below the level of the heart of patient 4 (e.g., at least partially within the cardiac silhouette). IMD 10 may be positioned on other locations, such as patient 4’s cranium region. IMD 10 includes one or more sensors (not shown in FIG. 1) and is configured to sense data via the one or more sensors. In some examples, IMD 10 takes the form of the Reveal LINQ™ or LINQ II™ ICM. In some examples, the one or more sensors are configured to sense cardiac signals and / or patient motion / activity, e.g., electrodes configured to detect a cardiac electrogram (EGM), one or more accelerometers configured to detect cardiac contractions and / or patient body motion / activity, or one or more optical sensors configured to detect pulsatile blood flow associated with cardiac contractions.

[0029] External device 12 may be a computing device with a display viewable by the user and an interface for receiving user input to external device 12. In some examples, external device 12 may be a notebook computer, tablet computer, workstation, one or more servers, smart phone, personal digital assistant, or another computing device that may run an application that enables the computing device to interact with IMD 10. External device 12 is configured to communicate with IMD 10 and, optionally, another computing device (not illustrated in FIG. 1), via wireless communication. External device 12, for example, may communicate via near-field communication technologies (e.g., inductive coupling, NFC or other communication technologies operable at ranges less than 10-20 cm) and far-field communication technologies (e.g., radiofrequency (RF) telemetry according to the 802.11 or Bluetooth® specification sets, or other communication technologies operable at ranges greater than near-field communication technologies).

[0030] External device 12 may be used to configure operational parameters and / or device settings for IMD 10. External device 12 may be used to retrieve data from IMD 10. The retrieved data may include values of physiological parameters measured by IMD 10, indications of health conditions detected by IMD 10, and physiological signals recorded by IMD 10. As will be discussed in greater detail below, one or more remote computing devices may interact with IMD 10 in a manner similar to external device 12, e.g., to program IMD 10 and / or retrieve data from IMD 10, via a network. External device 12 may be a computing device of patient 4 or a clinician. In some examples, both patient 4 and clinician may interact with respective computing devices to implement the techniques of this disclosure.

[0031] Sleep disordered breathing is a sleep disorder characterized by pauses in breathing or shallow breaths during sleep. A common method for diagnosing sleep disordered breathing is a sleep study, which involves spending a night in a sleep center or hospital where patient 4 is monitored while sleeping. Various sensors may be placed on the body of patient 4 to monitor brain activity, eye movement, heart rate, oxygen levels, and other relevant factors. A sleep specialist may then then analyze the data to determine if patient 4 has sleep disordered breathing and its severity. Although accurate, a sleep study may be expensive due to the test being conducted in a hospital by medical professionals. Furthermore, the study may involve long wait times due to increased demand, and patient 4 may also experience discomfort and interrupted sleep due tosleeping in a new setting while being restrained by cables. Monitoring for a single night for sleep disordered breathing may also not be ideal because the severity of SDB in patient 4 may not be stable over time but exhibit considerable night-to-night variability that cannot be detected by a single overnight sleep study.

[0032] In accordance with techniques of this disclosure, IMD 10 of system 2 may be configured to detect sleep disordered breathing based on a set (e.g., one or more) of heart rate cycle properties and a set of event properties. The set of heart rate cycle properties may include one or more of a peak-to-valley heart rate variation, a cycle length, a peak-to- valley time interval, or an activity count. The set of event properties may include one or more of a difference between maximum and minimum heart rate, a time difference between maximum and minimum heart rate, a maximum heart rate, or a minimum heart rate.

[0033] IMD 10 may determine one or more heart rate cycles within a cardiac signal. To determine the start, end, and length of a heart rate cycle, IMD 10 may use a short-term average heart rate and a long-term average heart rate. To determine the short-term average heart rate, IMD 10 may calculate a moving average based on the respective heart rates associated with M of the most recent heart beats (sometimes referred to as cardiac cycles). To determine the long-term average heart rate, IMD 10 may calculate a moving average based on the respective heart rates associated with N of the most recent heart beats, where M is less than N. A heart rate cycle may include a plurality of heart beats, each of the heart beats associated with a respective heart rate.

[0034] In some examples, IMD 10 may determine that the heart rate cycle starts when the short-term moving average signal crosses from below to above the long-term moving average signal. IMD 10 may determine that the heart rate cycle ends when the short-term moving average signal crosses from below to above the long-term moving average signal a subsequent time. In other examples, IMD 10 may determine that the heart rate cycle starts when the short-term moving average signal crosses from above to below the long-term moving average signal. IMD 10 may determine that the heart rate cycle ends when the short-term moving average signal crosses from above to below the long-term moving average signal a subsequent time. The cycle length may be the time interval from the start of the heart rate cycle to the end of the heart rate cycle.

[0035] IMD 10 may determine a set of heart rate cycle properties to detect SDHR events. SDHR events may be defined as cyclic changes based on properties such as a peak-to-valley heart rate variation greater than 6 BPM and less than 50 BPM, a cycle length between 22 and 100 seconds, a time interval between the peak and valley of the detected heart rate variation being greater than 3 seconds and less than 70 seconds, etc. To prevent noisy events from being detected as sleep disordered breathing and to improve specificity, IMD 10 may discard segments or windows of cardiac data that are determined to be noisy. Additionally, IMD 10 may confirm a detected SDHR event based on other (nearby) detected SDHR events using a set of event properties, such as a difference between maximum and minimum heart rate, a time difference between maximum and minimum heart rate, a maximum heart rate, or a minimum heart rate.

[0036] In this way, IMD 10 of system 2 may address the need for accurate, continuous, and long-term monitoring of sleep disordered breathing, including monitoring of night-to-night variability in AHI in patients with sleep disordered breathing. By continuously detecting the occurrence of sleep disordered breathing episodes, IMD 10 may collect valuable data for healthcare professionals to assess (e.g., which may be used to prescreen for sleep disordered breathing and / or to determine the best candidates for a sleep study). Thus, using IMD may be advantageous in terms of cost, practicality, accuracy, reliability, continuous monitoring, etc., making the data provided by IMD 10 clinically significant and useful.

[0037] FIG. 2A is a perspective drawing illustrating an IMD 10A, which may be an example configuration of IMD 10 of FIG. 1 as an ICM. In the example shown in FIG. 2A, IMD 10A may be embodied as a monitoring device having housing 13, proximal electrode 16A and distal electrode 16B. Housing 13 may further comprise first major surface 14, second major surface 18, proximal end 20, and distal end 22. Housing 13 encloses electronic circuitry located inside the IMD 10A and protects the circuitry contained therein from body fluids. Housing 13 may be hermetically sealed and configured for subcutaneous implantation. Electrical feedthroughs provide electrical connection of electrodes 16A and 16B.

[0038] In the example shown in FIG. 2 A, IMD 10A is defined by a length L, a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D. Inone example, the geometry of the IMD 10A - in particular a width W greater than the depth D - is selected to allow IMD 10A to be inserted under the skin of patient 4 using a minimally invasive procedure and to remain in the desired orientation during insertion. For example, the device shown in FIG. 2A includes radial asymmetries (notably, the rectangular shape) along the longitudinal axis that maintains the device in the proper orientation following insertion. For example, the spacing between proximal electrode 46A and distal electrode 46B may range from 5 millimeters (mm) to 55 mm, 30 mm to 55 mm, 35 mm to 55 mm, and from 40 mm to 55 mm and may be any range or individual spacing from 5 mm to 60 mm. In addition, IMD 10A may have a length L that ranges from 30 mm to about 70 mm. In other examples, the length L may range from 5 mm to 60 mm, 40 mm to 60 mm, 45 mm to 60 mm and may be any length or range of lengths between about 30 mm and about 70 mm. In addition, the width W of major surface 14 may range from 3 mm to 15, mm, from 3 mm to 10 mm, or from 5 mm to 15 mm, and may be any single or range of widths between 3 mm and 15 mm. The thickness of depth D of IMD 10A may range from 2 mm to 15 mm, from 2 mm to 9 mm, from 2 mm to 5 mm, from 5 mm to 15 mm, and may be any single or range of depths between 2 mm and 15 mm. In addition, IMD 10A according to an example of the present disclosure is has a geometry and size designed for ease of implant and patient comfort. Examples of IMD 10A described in this disclosure may have a volume of three cubic centimeters (cm) or less, 1.5 cubic cm or less or any volume between three and 1.5 cubic centimeters.

[0039] In the example shown in FIG. 2A, once inserted within patient 4, the first major surface 14 faces outward, toward the skin of patient 4 while the second major surface 18 is located opposite the first major surface 14. In addition, in the example shown in FIG. 2A, proximal end 20 and distal end 22 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of patient 4. IMD 10A, including instrument and method for inserting IMD 10 is described, for example, in U.S. Patent Publication No. 2014 / 0276928, incorporated herein by reference in its entirety.

[0040] Proximal electrode 16A is at or proximate to proximal end 20, and distal electrode 16B is at or proximate to distal end 22. Proximal electrode 16A and distal electrode 16B are used to sense cardiac EGM signals, e.g., ECG signals, thoracically outside the ribcage, which may be sub-muscularly or subcutaneously. EGM signals may be stored in a memory of IMD 10 A, and data may be transmitted via integrated antenna30A to another device, which may be another implantable device or an external device, such as external device 12. In some example, electrodes 16A and 16B may additionally or alternatively be used for sensing any bio-potential signal of interest, which may be, for example, an EGM, EEG, EMG, or a nerve signal, or for measuring impedance, from any implanted location.

[0041] In the example shown in FIG. 2A, proximal electrode 16A is at or in close proximity to the proximal end 20 and distal electrode 16B is at or in close proximity to distal end 22. In this example, distal electrode 16B is not limited to a flattened, outward facing surface, but may extend from first major surface 14 around rounded edges 24 and / or end surface 26 and onto the second major surface 18 so that the electrode 16B has a three-dimensional curved configuration. In some examples, electrode 16B is an uninsulated portion of a metallic, e.g., titanium, part of housing 13.

[0042] In the example shown in FIG. 2 A, proximal electrode 16A is located on first major surface 14 and is substantially flat, and outward facing. However, in other examples proximal electrode 16A may utilize the three dimensional curved configuration of distal electrode 16B, providing a three dimensional proximal electrode (not shown in this example). Similarly, in other examples distal electrode 16B may utilize a substantially flat, outward facing electrode located on first major surface 14 similar to that shown with respect to proximal electrode 16 A.

[0043] The various electrode configurations allow for configurations in which proximal electrode 16A and distal electrode 16B are located on both first major surface 14 and second major surface 18. In other configurations, such as that shown in FIG. 2A, only one of proximal electrode 16A and distal electrode 16B is located on both major surfaces 14 and 18, and in still other configurations both proximal electrode 16A and distal electrode 16B are located on one of the first major surface 14 or the second major surface 18 (e.g., proximal electrode 16A located on first major surface 14 while distal electrode 16B is located on second major surface 18). In another example, IMD 10A may include electrodes on both major surface 14 and 18 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on IMD 10A. Electrodes 16A and 16B may be formed of a plurality of different types of biocompatible conductive material, e.g. stainless steel, titanium, platinum, iridium, or alloys thereof, and may utilize one or more coatings such as titanium nitride or fractal titanium nitride.

[0044] In the example shown in FIG. 2A, proximal end 20 includes a header assembly 28 that includes one or more of proximal electrode 16A, integrated antenna 30A, antimigration projections 32, and / or suture hole 34. Integrated antenna 30A is located on the same major surface (i.e., first major surface 14) as proximal electrode 16A and is also included as part of header assembly 28. Integrated antenna 30A allows IMD 10A to transmit and / or receive data. In other examples, integrated antenna 30A may be formed on the opposite major surface as proximal electrode 16A, or may be incorporated within the housing 13 of IMD 10A. In the example shown in FIG. 2A, anti-migration projections 32 are located adjacent to integrated antenna 30A and protrude away from first major surface 14 to prevent longitudinal movement of the device. In the example shown in FIG. 2A, anti-migration projections 32 include a plurality (e.g., nine) small bumps or protrusions extending away from first major surface 14. As discussed above, in other examples antimigration projections 32 may be located on the opposite major surface as proximal electrode 16A and / or integrated antenna 30A. In addition, in the example shown in FIG. 2A, header assembly 28 includes suture hole 34, which provides another means of securing IMD 10A to patient 4 to prevent movement following insertion. In the example shown, suture hole 34 is located adjacent to proximal electrode 16A. In one example, header assembly 28 is a molded header assembly made from a polymeric or plastic material, which may be integrated or separable from the main portion of IMD 10A.

[0045] FIG. 2B is a perspective drawing illustrating another IMD 10B, which may be another example configuration of IMD 10 from FIG. 1 as an ICM. IMD 10B of FIG. 2B may be configured substantially similarly to IMD 10A of FIG. 2A, with differences between them discussed herein.

[0046] IMD 10B may include a leadless, subcutaneously-implantable monitoring device, e.g. an ICM. IMD 10B includes housing having a base 40 and an insulative cover 42. Proximal electrode 16C and distal electrode 16D may be formed or placed on an outer surface of cover 42. Various circuitries and components of IMD 10B may be formed or placed on an inner surface of cover 42, or within base 40. In some examples, a battery or other power source of IMD 10B may be included within base 40. In the illustrated example, antenna 30B is formed or placed on the outer surface of cover 42, but may be formed or placed on the inner surface in some examples. In some examples, insulativecover 42 may be positioned over an open base 40 such that base 40 and cover 42 enclose the circuitries and other components and protect them from fluids such as body fluids.

[0047] Circuitries and components may be formed on the inner side of insulative cover 42, such as by using flip-chip technology. Insulative cover 42 may be flipped onto a base 40. When flipped and placed onto base 40, the components of IMD 10B formed on the inner side of insulative cover 42 may be positioned in a gap 44 defined by base 40. Electrodes 16C and 16D and antenna 30B may be electrically connected to circuitry formed on the inner side of insulative cover 42 through one or more vias (not shown) formed through insulative cover 42. Insulative cover 42 may be formed of sapphire (i.e., corundum), glass, parylene, and / or any other suitable insulating material. Base 40 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 16C and 16D may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes 16C and 16D may be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.

[0048] In the example shown in FIG. 2B, the housing of IMD 10B defines a length L, a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D, similar to IMD 10A of FIG. 2A. For example, the spacing between proximal electrode 46C and distal electrode 46D may range from 5 mm to 50 mm, from 30 mm to 50 mm, from 35 mm to 45 mm, and may be any single spacing or range of spacings from 5 mm to 50 mm, such as approximately 40 mm. In addition, IMD 10B may have a length E that ranges from 5 mm to about 70 mm. In other examples, the length L may range from 30 mm to 70 mm, 40 mm to 60 mm, 45 mm to 55 mm, and may be any single length or range of lengths from 5 mm to 50 mm, such as approximately 45 mm. In addition, the width W may range from 3 mm to 15 mm, 5 mm to 15 mm, 5 mm to 10 mm, and may be any single width or range of widths from 3 mm to 15 mm, such as approximately 8 mm. The thickness or depth D of IMD 10B may range from 2 mm to 15 mm, from 5 mm to 15 mm, or from 3 mm to 5 mm, and may be any single depth or range of depths between 2 mm and 15 mm, such as approximately 4 mm. IMD 10B may have a volume of three cubic centimeters (cm) or less, or 1.5 cubic cm or less, such as approximately 1.4 cubic

[0049] In the example shown in FIG. 2B, once inserted subcutaneously within patient 4, outer surface of cover 42 faces outward, toward the skin of patient 4. In addition, as shown in FIG. 2B, proximal end 46 and distal end 48 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the skin of patient 4. In addition, edges of IMD 10B may be rounded.

[0050] FIG. 3 is a functional block diagram illustrating an example configuration of IMD 10 of FIG. 1 in accordance with one or more techniques described herein. In the illustrated example, IMD 10 includes electrodes 16 (e.g., corresponding to any of electrodes 16A-16D), antenna 26, processing circuitry 50, sensing circuitry 52, communication circuitry 54, memory 56, switching circuitry 58, and sensors 62, which includes one or more accelerometers 64. Processing circuitry 50 may be operatively coupled to sensing circuitry 52, communication circuitry 54, memory 56, switching circuitry 58, and sensors 62. Although the illustrated example includes two electrodes 16, IMDs including or coupled to more than two electrodes 16 may implement the techniques of this disclosure in some examples.

[0051] Processing circuitry 50 may be configured to implement functionality and / or execute instructions within IMD 10. For example, processing circuitry 50 may receive and execute instructions that provide the functionality described herein. Processing circuitry 50 may include fixed function circuitry and / or programmable processing circuitry. Processing circuitry 50 may include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or analog logic circuitry. In some examples, processing circuitry 50 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, as well as other discrete or integrated logic circuitry. The functions attributed to processing circuitry 50 herein may be embodied as software, firmware, hardware or any combination thereof.

[0052] Sensing circuitry 52 may be selectively coupled to electrodes 16 via switching circuitry 58, e.g., to sense electrical signals of the heart of patient 4, for example by selecting the electrodes 16 and polarity, referred to as the sensing vector, used to sense a cardiac EGM, e.g., ECG, as controlled by processing circuitry 50. Sensing circuitry 52 may sense the cardiac EGM from electrodes 16 in order to facilitate monitoring theelectrical activity of the heart. In some examples, sensing circuitry 52 may include one or more filters and amplifiers for filtering and amplifying signals received from electrodes 16 and / or sensors 62. Sensing circuitry 52 and processing circuitry 50 may store patient data in memory 56, e.g., digitized samples of electrical signals. Sensing circuitry 52 may also monitor signals from sensors 62, which may include one or more accelerometers, pressure sensors, and / or optical sensors, as examples. Sensing circuitry 52 may capture sensor signals from any one of sensors 62, e.g., to produce other patient data, in order to facilitate monitoring of patient activity and detecting changes in patient health.

[0053] Communication circuitry 54, which may be an example of the communication circuitry described in FIG. 1, may include any suitable hardware, firmware, software or any combination thereof for wirelessly communicating with another device, such as external device 12, another networked computing device, or another IMD or sensor. Under the control of processing circuitry 50, communication circuitry 54 may receive downlink telemetry from, as well as send uplink telemetry to external device 12 or another device with the aid of an internal or external antenna, e.g., antenna 26. In addition, processing circuitry 50 may communicate with a networked computing device via an external device (e.g., external device 12) and a computer network, such as the Medtronic CareLink® Network. Antenna 26 and communication circuitry 54 may be configured to transmit and / or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth®, WiFi, or other proprietary or non-proprietary wireless communication schemes.

[0054] In some examples, processing circuitry 50 may control communication circuitry 54 to transmit data (e.g., patient data) to another device, e.g., external device 12 or a cloud computing system comprising one or more computing devices, for analysis. In this manner, the techniques of this disclosure may advantageously enable improved accuracy in the detection of changes in patient health and, consequently, better evaluation of the condition of patient 4.

[0055] In some examples, memory 56 includes computer-readable instructions that, when executed by processing circuitry 50, cause IMD 10 and processing circuitry 50 to perform various functions attributed to IMD 10 and processing circuitry 50 herein. Memory 56 may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random-access memory (RAM), read-only memory (ROM), non-volatile RAM(NVRAM), electrically-erasable programmable ROM (EEPROM), ferroelectric RAM (FRAM), dynamic random-access memory (DRAM), flash memory, or any other digital media. Memory 56 may store, as examples, programmed values for one or more operational parameters of IMD 10 and / or data collected by IMD 10 for transmission to another device using communication circuitry 54. Data stored by memory 56 and transmitted by communication circuitry 54 to one or more other devices may include various patient data (e.g., patient physiological parameters such as those described herein). Memory 56 may store patient data 65 determined in accordance with techniques of this disclosure. Example patient data 65 includes indications of the presence of sleep disordered breathing episodes, or various metrics of detected sleep disordered breathing episodes, such as number of episodes, number of episodes per period, rate of episodes, or duration of episodes, such as average or total duration of episodes per period.

[0056] In some examples, processing circuitry 50 may be configured to perform noise detection. To perform noise detection, processing circuitry 50 may process a cardiac signal to precisely detect an R-wave peak of a heartbeat. For example, IMD 10 may determine an approximate R-wave peak of a heartbeat. Processing circuitry 50 may then determine a more precise R-wave peak by analyzing a window of samples that include samples around the approximate R-wave peak (e.g., 25 samples before the approximate R-wave peak and 25 samples after the approximate R-wave peak). Processing circuitry 50 may compute the mean of the window of samples and subtract the mean from the cardiac signal. Processing circuitry 50 may determine that the sample from this processed signal having the largest absolute value is the R-wave peak.

[0057] In some examples, processing circuitry 50 may detect noise within a window of samples based on a first difference signal and a second difference signal of an original signal. For example, a 32-sample signal may include the following sample values: [-363, - 344, -324, -308, -308, -353, -472, -652, -804, -805, -589, -205, 207, 515, 667, 696, 661, 583, 448, 250, 26, -163, -280, -333, -364 -404, -451, -488, -501, -487, -461, -442], Accordingly, the first difference signal (31 samples) of the 32-sample signal may be: [19, 20, 16, 0, -45, -119, -180, -152, -1, 216, 384, 412, 308, 152, 29, -35, -78, -135, -198, -224, -189, -117, -53, -31, -40 -47, -37, -13, 14, 26, 19]. Additionally, the second difference signal (30 samples) of the 32-sample signal may be: [1 -4, -16, -45, -74, -61, 28, 151, 217, 168, 28, -104, -156, -123, -64, -43, -57, -63, -26, 35, 72, 64, 22, -9, -7, 10, 24, 27, 12, -7],

[0058] Processing circuitry 50 may detect noise based on whether adjacent samples in the first difference signal changed signs. For example, processing circuitry 50 may not detect a sign change for the first 2 sample values of the first difference signal, 19 and 20. However, processing circuitry 50 may detect a sign change for the fourth and fifth sample values of the first difference signal, 0 and -45.

[0059] In some examples, processing circuitry 50 may further determine whether a sample is noise based on the corresponding second difference signal value. For example, even if processing circuitry 50 detects a sign change in the first difference signal, if the corresponding second difference value is not greater than 40 or less than -40, processing circuitry 50 may not count the event as noise. In the above example, processing circuitry 50 may detect a sign change for the fourth and fifth sample values of the first difference signal, and processing circuitry -50 may determine that the corresponding second difference value, -45, is less than -40. Thus, processing circuitry 50 may count the sample as noise.

[0060] In this fashion, processing circuitry 50 may analyze the window of samples for samples that satisfy the above conditions. If the number of samples exceeds a threshold (e.g., 8 events per 32 samples), processing circuitry 50 may determine that the R-wave (and in turn the heartbeat) is noisy. Processing circuitry 50 may count the number of noisy samples and / or heartbeats within a heart rate cycle. If the number of noisy samples and / or heartbeats exceeds a threshold (e.g., 4 heartbeats), then processing circuitry 50 may determine that the window of samples is noisy and exclude the window from analysis (e.g., to avoid false-positives otherwise indicating detection of a SDHR event within the heart rate cycle).

[0061] This disclosure primarily describes examples in which a cardiac EGM is the cardiac activity or cardiac signal analyzed to determine heart rates for consecutive heart rate cycles and detect SDHR events. However, the techniques described herein may be applicable to any signal that varies based on cardiac activity such that consecutive heart rate cycles and respective heart rates can be determined. Other example signals to which the techniques of this disclosure may be applied include an accelerometer signal generated by accelerometer 64, a pressure signal of a pressure sensor of sensors 62, or an optical signal generated by an optical sensor of sensors 62, each of which may vary with each heart rate cycle based on contractions or other mechanical activity of heart.

[0062] FIG. 4 is a chart illustrating the determination of heart rate cycles based on short-term and long-term average heart rates. The start of a heart rate cycle, such as heart rate cycle 70, may be defined as when a short-term moving average signal 72 crosses from below to above a long-term moving average signal 74 for a first time. For example, a start point 76 may indicate the start of heart rate cycle 70. The definition of the start of a heart rate cycle may also include that short-term moving average signal 72 is increasing during the crossing (i.e., the slope of short-term moving average signal 72 is positive).

[0063] The end of a heart rate cycle, such as heart rate cycle 70, may be defined as when short-term moving average signal 72 crosses from below to above long-term moving average signal 74 for a second time. For example, an end point 78 may indicate the end of heart rate cycle 70. The definition of the end of a heart rate cycle may also include that short-term moving average signal 72 is increasing during the crossing (i.e., the slope of short-term moving average signal 72 is positive). The length of a heart rate cycle (“cycle length”) may be defined as the time interval from the start (e.g., start point 76) of a heart rate cycle (e.g., heart rate cycle 70) to the end (e.g., end point 78) of the heart rate cycle.

[0064] To determine short-term moving average signal 72, processing circuitry 50 may calculate a moving average based on M (e.g., 5) of the most recent heart beats. To determine the long-term average heart rate, processing circuitry 50 may calculate a moving average based on N (e.g., 120) of the most recent heart beats, where M is less than N. Processing circuitry 50 may be configured to determine the start (e.g., start point 76) and end (e.g., end point 78) of a heart rate cycle (e.g., heart rate cycle 70) as described herein.

[0065] It should be understood that the scope of this disclosure is not limited to the examples provided here, and that other definitions for the start and end of a heart rate cycle are possible. For example, processing circuitry 50 may determine that a heart rate cycle starts when short-term moving average signal 72 crosses from above to below longterm moving average signal 74 a first time. Processing circuitry 50 may determine that the heart rate cycle ends when short-term moving average signal 72 crosses from above to below long-term moving average signal 74 a second time. In this example, processing circuitry 50 may also use the slope of short-term average signal 72 during the crossing (e.g., whether the slope is negative) to determine the start and / or end of the heart rate cycle.

[0066] After determining a start and end of one or more heart rate cycles, processing circuitry 50 may determine a set of heart rate cycle properties to detect SDHR events. For example, processing circuitry 50 may determine whether peak-to-valley heart rate variation is greater than a first peak-to-valley heart rate variation threshold (e.g., 6 BPM) and less than a second peak-to-valley heart rate variation threshold (e.g., 50 BPM), whether cycle length is between a first cycle length threshold (e.g., 22 seconds) and a second cycle length threshold (e.g., 100 seconds), whether the time interval between the peak and valley of the detected heart rate variation is greater than a first time interval threshold (e.g., 3 seconds) and less than a second time interval threshold (e.g., 70 seconds), and / or the activity count per 2 seconds (or any other time interval) is less than a ratio (e.g., an activity count of 8 for 2 seconds of activity count data from IMD 10). Processing circuitry 50 may determine activity counts over a time period based on a signal of accelerometer 64 over the time period crossing one or more thresholds.

[0067] FIG. 5 is a chart illustrating example heart rate cycle properties for a heart rate cycle 80. As shown, processing circuitry 50 may determine a peak-to-valley heart rate variation 82 of heart rate cycle 80 (e.g., of the amplitude of short-term moving average signal 72 during heart rate cycle 80). Processing circuitry 50 may determine whether peak- to-valley heart rate variation 82 is greater than a first peak-to-valley heart rate variation threshold (e.g., 6 BPM) and less than a second peak-to-valley heart rate variation threshold (e.g., 50 BPM), which may be part of the definition of a SDHR event.

[0068] Processing circuitry 50 may determine a cycle length 84 of heart rate cycle 80. Processing circuitry 50 may determine cycle length 84 based on a start point 90 and an end point 92 of heart rate cycle 80 (which processing circuitry 50 may determine based on when and / or how short-term moving average signal 72 crosses over long-term moving average signal 74, as described with respect to FIG. 4). Processing circuitry 50 may determine whether cycle length 84 is between a first cycle length threshold (e.g., 22 seconds) and a second cycle length threshold (e.g., 100 seconds), which may be part of the criteria for a SDHR event.

[0069] Processing circuitry 50 may determine a time interval 86 between a peak 94 of heart rate cycle 80 (e.g., a maximum BPM value of short-term moving average signal 72 during heart rate cycle 80) and a valley 96 of heart rate cycle 80 (e.g., a minimum BPM value of short-term moving average signal 72 during heart rate cycle 80). Processingcircuitry 50 may determine whether the time interval between peak 94 and valley 96 of peak-to-valley heart rate variation 82 is greater than a first time interval threshold (e.g., 3 seconds) and less than a second time interval threshold (e.g., 70 seconds), which may be part of the criteria for a SDHR event.

[0070] Processing circuitry 50 may determine whether an activity count frequency 88 during heart rate cycle 80 is less than a ratio (e.g., an activity count of 8 for 2 seconds of activity count data from IMD 10), which may be part of the criteria for a SDHR event. Processing circuitry 50 may determine that a detected event is a SDHR event if one or more of peak-to-valley heart rate variation 82, cycle length 84, time interval 86, or activity count frequency 88 satisfies their respective conditions. In some examples, processing circuitry 50 may only determine that a detected events is a SDHR event if all of the criteria is satisfied (e.g., peak-to-valley heart rate variation 82, cycle length 84, time interval 86, and activity count frequency 88 all satisfy their respective conditions).

[0071] FIG. 6 is a conceptual diagram illustrating a plurality of events, such as events 90A-90C (collectively, “events 90”). Each of events 90 may be a heart rate cycle 80 and, in some examples, a heart rate cycle 80 that satisfies one or more other criteria for qualification as an SDHR event, such as the criteria described with respect to FIG. 5. In some examples, processing circuitry 50 may confirm whether a detected event is a SDHR event based on whether there are one or more events detected within one or more time windows of the detected event (e.g., a first 120 second window beginning 120 seconds before onset of the detected event and a second 120 second window beginning at offset of the detected event). A time window, in the context of data analysis and monitoring, may refer to a specific period or interval of time during which data or events are observed, recorded, or analyzed.

[0072] By requiring multiple event detections within one or more time windows to confirm a detected event, processing circuitry 50 may reduce the likelihood of false positives and increase the reliability of event detection. For example, if processing circuitry 50 detects one or more events within a time window, processing circuitry 50 may confirm a detected event as a SDHR event. Otherwise, processing circuitry 50 may determine that the detected event is a false-positive. In some examples, the events may need to be consecutive.

[0073] In some examples, the time window may be defined in relation to a specific detected event. For example, a time window may be centered around a specific detected event, such as event 90B, and the time window may have a width that allows for detection of nearby, relevant events (e.g., event 90A and 90C). For example, a time window centered around a detected event may have a width of 480 seconds. Other examples are contemplated by this disclosure. In any case, processing circuitry 50 may use the earlier events (e.g., event 90A) and / or later events (e.g., event 90C) to confirm event 90B as a SDHR event, in this way reducing false positives and increasing confidence in the detection of sleep disordered breathing.

[0074] Processing circuitry 50 may determine a set of event properties to improve specificity of SDHR detection. The set of event properties may include the difference between maximum and minimum heart rate, the time difference between maximum and minimum heart rate, the maximum heart rate within each cycle, and the minimum heart rate. In some examples, processing circuitry 50 may compare the event properties of multiple events to confirm whether a detected event is a SDHR event.

[0075] In some examples, processing circuitry 50 may determine whether a SDHR event has occurred based on whether the difference between the maximum and minimum heart rate is within 3 BPM for 3 consecutive events. For example, the maximum heart rate and minimum heart rate for a first event 90A may be 80 BPM and 75 BPM, such that the difference for first event 90A is 5 BPM. The maximum heart rate and minimum heart rate for a second event 90B may be 81 BPM and 74 BPM, such that the difference for second event 90B is 7 BPM. The maximum heart rate and minimum heart rate for a third event 90C may be 78 BPM and 74 BPM, such that the difference for third event 90C is 4 BPM.

[0076] Processing circuitry 50 may compare the difference between the maximum and minimum heart rate for events 90 by determining the absolute value of the difference between the differences of events 90 (in this way comparing an example event property for events 90). For example, the absolute value of 5 BPM (the difference of first event 90A) minus 7 BPM (the difference of second event 90B) is 2 BPM. The absolute value of 5 BPM (the difference of first event 90A) minus 4 BPM (the difference of third event 90C) is 1 BPM. The absolute value of 7 BPM (the difference of second event 90B) minus 4 BPM (the difference of third event 90C) is 3 BPM. As 2 BPM, 1 BPM, and 3 BPM are within (e.g., equal to or less than) 3 BPM of each other, processing circuitry 50 maydetermine that the first criterion is satisfied. Since none of these differences differs by more than 3 BPM, processing circuitry 50 may confirm that event 90A, event 90B, and / or event 90C is a SDHR event(s).

[0077] In some examples, processing circuitry 50 may determine whether a SDHR event has occurred based on whether the time difference between maximum and minimum heart rate for 3 consecutive events is within 6 secs. For example, the maximum heart rate and minimum heart rate for first event 90A may have occurred at timax =246 seconds (secs) and timin =258 secs, such that the time difference for first event 90A is -12 secs. The maximum heart rate and minimum heart rate for second event 90B may have occurred at timax =275 secs and timin =286 secs, such that the time difference for second event 90B is - 11 secs. The maximum heart rate and minimum heart rate for third event 90C may have occurred at t3max =310 secs and isnun =325 secs, such that the time difference for second event 90B is -15 secs.

[0078] Processing circuitry 50 may compare the time difference between the maximum and minimum heart rate for the three heart rate cycles by determining the absolute value of the difference between the time differences of the various heart rate cycles (in this way comparing an example event property for events 90). For example, the absolute value of -12 secs (the time difference of first event 90A) minus -11 secs (the time difference of second event 90B) is 2. The absolute value of -12 secs (the time difference of first event 90A) minus -15 (secs the time difference of third event 90C) is 3 secs. The absolute value of -11 secs (the time difference of second event 90B) minus -15 secs (the time difference of third event 90C) is 4 secs. As 2 secs, 3 secs, and 4 secs are within 6 secs, processing circuitry 50 may determine that the second criterion is satisfied. Since none of these differences differs by more than 6 seconds, processing circuitry 50 may confirm that event 90A, event 90B, and / or event 90C is a SDHR event(s).

[0079] In some examples, processing circuitry 50 may determine whether a SDHR event has occurred based on whether the maximum heart rate within each cycle is within 3 BPM for 3 consecutive events. For example, the maximum heart rate for first event 90A may be 82 BPM. The maximum heart rate for second event 90B may be 83 BPM. The maximum heart rate for third event 90C may be 80 BPM.

[0080] Processing circuitry 50 may compare the maximum heart rate for the three heart rate cycles by determining the absolute value of the difference between themaximum heart rates of the various heart rate cycles (in this way comparing an example event property for events 90). For example, the absolute value of 82 BPM (the maximum heart rate of first event 90A) minus 83 BPM (the maximum heart rate of second event 90B) is 1 BPM. The absolute value of 82 BPM (the maximum heart rate of first event 90A) minus 80 BPM (the maximum heart rate of third event 90C) is 2 BPM. The absolute value of 83 BPM (the maximum heart rate of second event 90B) minus 80 BPM (the maximum heart rate of third event 90C) is 3 BPM. As 1 BPM, 2 BPM, and 3 BPM are within 3 BPM, processing circuitry 50 may determine that the third criterion is satisfied. Since none of these differences differs by more than 3 BPM, processing circuitry 50 may confirm that event 90A, event 90B, and / or event 90C is a SDHR event(s).

[0081] Processing circuitry 50 may determine whether a SDHR event has occurred based on whether the minimum heart rate within each cycle is within 3 BPM for 3 consecutive events. For example, the minimum heart rate for first event 90A may be 72 BPM. The minimum heart rate for second event 90B may be 70 BPM. The minimum heart rate for third event 90C may be 73 BPM.

[0082] Processing circuitry 50 may compare the minimum heart rate for the three heart rate cycles by determining the absolute value of the difference between the minimum heart rates of the various heart rate cycles (in this way comparing an example event property for events 90). For example, the absolute value of 72 BPM (the minimum heart rate of first event 90A) minus 70 BPM (the minimum heart rate of second event 90B) is 2 BPM. The absolute value of 72 BPM (the minimum heart rate of first event 90A) minus 73 BPM (the minimum heart rate of third event 90C) is 1 BPM. The absolute value of 70 BPM (the minimum heart rate of second event 90B) minus 73 BPM (the minimum heart rate of third event 90C) is 3 BPM. As 2 BPM, 1 BPM, and 3 BPM are within 3 BPM, processing circuitry 50 may determine that the fourth criterion is satisfied. Since none of these differences differs by more than 3 BPM, processing circuitry 50 may confirm that event 90A, event 90B, and / or event 90C is a SDHR event(s).

[0083] Processing circuitry 50 may use satisfaction of one or more of the above criteria to confirm the occurrence of a SDHR event. By using the various criteria disclosed herein, system 2 may more accurately and precisely detect sleep disordered breathing. In this way, system 2 may reduce the detection of false-positives while still collecting clinically significant results, thereby improving the medical care of patient 4 (e.g., byreducing visits to the hospital, by continuously monitoring patient 4 for sleep disordered breathing, etc.).

[0084] It should be understood that each threshold value disclosed herein is an example, and other threshold values (e.g., 4 BPM, 5 BPM, 2 consecutive beats, 4 consecutive beats, 10 seconds, 20 seconds, etc.) are contemplated by this disclosure.

[0085] FIG. 7 is functional block diagram of an example external device 12. As shown in FIG. 7, external device 12 includes processing circuitry 100, a memory 102, a user interface 104, telemetry circuitry 106, and a power source 108. External device 12 may be a dedicated hardware device with dedicated software for interacting with IMD 10. Alternatively, external device 12 may be an off-the-shelf computing device running an application that enables external device 12 to interact with IMD 10.

[0086] A user may use external device 12 to select programmable parameters that control the monitoring and delivery of therapy by IMD 10, and to retrieve information collected by IMD regarding the condition of patient 14 or the performance of IMD 10. The user may interact with external device 12 via user interface 104, which may include display to present graphical user interface to a user, and a keypad or another mechanism for receiving input from a user.

[0087] Processing circuitry 100 can take the form one or more microprocessors, DSPs, ASICs, FPGAs, programmable logic circuitry, or the like, and the functions attributed to processing circuitry 102 herein may be embodied as hardware, firmware, software or any combination thereof. Memory 102 may store instructions that cause processing circuitry 100 to provide the functionality ascribed to external device 12 herein, and information used by processing circuitry 100 to provide the functionality ascribed to external device 12 herein. Memory 102 may include any fixed or removable magnetic, optical, or electrical media, such as RAM, ROM, CD-ROM, hard or floppy magnetic disks, EEPROM, or the like. Memory 102 may also include a removable memory portion that may be used to provide memory updates or increases in memory capacities. A removable memory may also allow patient data to be easily transferred to another computing device, or to be removed before external device 12 is used to program therapy for another patient. Memory 102 may also store information that controls therapy delivery by IMD 10, such as stimulation parameter values.

[0088] External device 12 may communicate wirelessly with IMD 10, such as using RF communication or proximal inductive interaction. This wireless communication is possible through the use of telemetry circuitry 106, which may be coupled to an internal antenna or an external antenna. Telemetry circuitry 106 may also be configured to communicate with another computing device via wireless communication techniques, or direct communication through a wired connection. Examples of local wireless communication techniques that may be employed to facilitate communication between external device 12 and another computing device include RF communication according to the 802.11 or Bluetooth specification sets, infrared communication, e.g., according to the IrDA standard, or other standard or proprietary telemetry protocols. In this manner, other external devices may be capable of communicating with external device 12 without needing to establish a secure wireless connection.

[0089] Power source 108 is configured to deliver operating power to the components of external device 12. Power source 108 may include a battery and a power generation circuit to produce the operating power. In some embodiments, the battery may be rechargeable to allow extended operation. Recharging may be accomplished by electrically coupling power source 108 to a cradle or plug that is connected to an alternating current (AC) outlet. In addition or alternatively, recharging may be accomplished through proximal inductive interaction between an external charger and an inductive charging coil within external device 12. In other embodiments, traditional batteries (e.g., nickel cadmium or lithium ion batteries) may be used. In addition, external device 12 may be directly coupled to an alternating current outlet to power external device 12. Power source 108 may include circuitry to monitor power remaining within a battery. In this manner, user interface 104 may provide a current battery level indicator or low battery level indicator when the battery needs to be replaced or recharged. In some cases, power source 108 may be capable of estimating the remaining time of operation using the current battery.

[0090] In some examples, processing circuitry 100 and memory 102 of external device 12 may be configured to provide some or all of the functionality ascribed to processing circuitry 50 and memory 56 of IMD 10. In some examples, processing circuitry 100 may be configured to generate a time window of an EGM signal, determine values of one or more features of the EGM signal during the time window, and adjust a sensitivitythreshold used by IMD 10 to detect near filed depolarizations, as described herein with respect to processing circuitry 50 of IMD 10.

[0091] FIG. 8 is a block diagram illustrating a system 110 that includes an external device 112, such as a server, and one or more computing devices 114A-114N that are coupled to IMD 10 and external device 12 shown in FIG. 1 via a network 120, according to one example. In this example, IMD 10 uses communication circuitry 54 to communicate with external device 12 via a first wireless connection, and to communicate with an access point 122 via a second wireless connection. In the example of FIG. 8, access point 122, external device 12, external device 112, and computing devices 114A- 114N are interconnected, and able to communicate with each other, through network 120. In some cases, one or more of access point 122, external device 12, external device 112, and computing devices 114A-114N may be coupled to network 120 through one or more wireless connections. IMD 10, external device 12, external device 112, and computing devices 114A-114N may each comprise one or more processing circuitries, such as one or more microprocessors, DSPs, ASICs, FPGAs, programmable logic circuitry, or the like, that may perform various functions and operations, such as those described herein.

[0092] Access point 122 may comprise a device that connects to network 120 via any of a variety of connections, such as telephone dial-up, digital subscriber line (DSL), or cable modem connections. In other examples, access point 122 may be coupled to network 120 through different forms of connections, including wired or wireless connections. In some examples, access point 122 may communicate with external device 12 and / or IMD 10. Access point 122 may be co-located with patient 14 (e.g., within the same room or within the same site as patient 14) or may be remotely located from patient 14. For example, access point 122 may be a home monitor that is located in the patient’s home or is portable for carrying with patient 14.

[0093] During operation, IMD 10 may collect, measure, and store various forms of diagnostic data. For example, as described previously, IMD 10 may collect EGM signals, generate a time window of an EGM signal, determine values of one or more features of the EGM signal during the time window, and adjust a sensitivity threshold used to detect R- waves or other near field depolarizations. In certain cases, IMD 10 may directly analyze collected diagnostic data and generate any corresponding reports or alerts. In some cases, however, IMD 10 may send diagnostic data to external device 12, access point 122, and / orexternal device 112, either wirelessly or via access point 122 and network 110, for remote processing and analysis.

[0094] For example, IMD 10 may provide external device 112 with collected EGM data, system integrity indications, and any other relevant physiological or system data via access point 122 and network 120. External device 112 includes one or more processing circuitries 118. In some cases, external device 112 may request such data, and in some cases, IMD 10 may automatically or periodically provide such data to external device 112. Upon receipt of the diagnostic data via input / output device 116, external device 112 is capable of analyzing the data and generating reports or alerts.

[0095] In one example, external device 112 may comprise a secure storage site for information that has been collected from IMD 10 and / or external device 12. In this example, network 120 may comprise an Internet network; and trained professionals, such as clinicians, may use computing devices 114A-114N to securely access stored data on external device 112. For example, the trained professionals may need to enter usernames and passwords to access the stored information on external device 112. In one embodiment, external device 112 may be a CareLink server provided by Medtronic, Inc., of Minneapolis, Minnesota.

[0096] In some examples, processing circuitry and memory of one or more of access point 122, server 112, or computing devices 114, e.g., processing circuitry 118 and memory of server 112, may be configured to provide some or all of the functionality ascribed to processing circuitry 50 and memory 56 of IMD 10. In some examples, processing circuitry 118 may be configured to generate a time window of the EGM signal, determine values of one or more features of an EGM signal during the time window, and adjust a sensitivity threshold as described herein with respect to processing circuitry 50 of IMD 10.

[0097] FIG. 9 is a flowchart illustrating an example operation of the techniques of this disclosure. Sensing circuitry 52 may sense cardiac activity of a patient (900). Based on the cardiac activity, processing circuitry 50 may determine heart rate cycles based on shortterm moving average signal 72 and long-term moving average signal 74 (902). Processing circuitry 50 may determine the start and end of the heart rate cycles based on when shortterm moving average signal 72 crosses long-term moving average signal 74.

[0098] In some examples, processing circuitry 50 may perform noise detection and exclude heart rate cycles that are noisy from further analysis (904). To perform noise detection, processing circuitry 50 may process a cardiac signal to precisely detect an R- wave peak of a heartbeat. For example, IMD 10 may determine an approximate R-wave peak of a heartbeat. Processing circuitry 50 may then determine a more precise R-wave peak by analyzing a window of samples that include samples around the approximate R- wave peak (e.g., 25 samples before the approximate R-wave peak and 25 samples after the approximate R-wave peak). Processing circuitry 50 may compute the mean of the window of samples and subtract the mean from the cardiac signal. Processing circuitry 50 may determine that the sample from this processed signal having the largest absolute value is the R-wave peak.

[0099] In some examples, processing circuitry 50 may detect noise within a window of samples based on a first difference signal and a second difference signal of an original signal. For example, a 32-sample signal may include the following sample values: [-363, - 344, -324, -308, -308, -353, -472, -652, -804, -805, -589, -205, 207, 515, 667, 696, 661, 583, 448, 250, 26, -163, -280, -333, -364 -404, -451, -488, -501, -487, -461, -442], Accordingly, the first difference signal (31 samples) of the 32-sample signal may be: [19, 20, 16, 0, -45, -119, -180, -152, -1, 216, 384, 412, 308, 152, 29, -35, -78, -135, -198, -224, -189, -117, -53, -31, -40 -47, -37, -13, 14, 26, 19]. Additionally, the second difference signal (30 samples) of the 32-sample signal may be: [1 -4, -16, -45, -74, -61, 28, 151, 217, 168, 28, -104, -156, -123, -64, -43, -57, -63, -26, 35, 72, 64, 22, -9, -7, 10, 24, 27, 12, -7],

[0100] Processing circuitry 50 may detect noise based on whether adjacent samples in the first difference signal changed signs. For example, processing circuitry 50 may not detect a sign change for the first 2 sample values of the first difference signal, 19 and 20. However, processing circuitry 50 may detect a sign change for the fourth and fifth sample values of the first difference signal, 0 and -45.

[0101] In some examples, processing circuitry 50 may further determine whether a sample is noise based on the corresponding second difference signal value. For example, even if processing circuitry 50 detects a sign change in the first difference signal, if the corresponding second difference value is not greater than 40 or less than -40, processing circuitry 50 may not count the event as noise. In the above example, processing circuitry 50 may detect a sign change for the fourth and fifth sample values of the first differencesignal, and processing circuitry -50 may determine that the corresponding second difference value, -45, is less than -40. Thus, processing circuitry 50 may count the sample as noise.

[0102] In this fashion, processing circuitry 50 may analyze the window of samples for samples that satisfy the above conditions. If the number of samples exceeds a threshold (e.g., 8 events per 32 samples), processing circuitry 50 may determine that the R-wave (and in turn the heartbeat) is noisy. Processing circuitry 50 may count the number of noisy samples and / or heartbeats within a heart rate cycle. If the number of noisy samples and / or heartbeats exceeds a threshold (e.g., 4 heartbeats), then processing circuitry 50 may determine that the window of samples is noisy and exclude the window from analysis (e.g., to avoid false-positives otherwise indicating detection of a SDHR event within the heart rate cycle).

[0103] Processing circuitry 50 may determine a set of heart rate cycle properties for the detected heart rate cycles to detect SDHR events (906). For example, processing circuitry 50 may determine whether peak-to-valley heart rate variation is greater than a first peak-to-valley heart rate variation threshold (e.g., 6 BPM) and less than a second peak-to-valley heart rate variation threshold (e.g., 50 BPM), whether cycle length is between a first cycle length threshold (e.g., 22 seconds) and a second cycle length threshold (e.g., 100 seconds), whether the time interval between the peak and valley of the detected heart rate variation is greater than a first time interval threshold (e.g., 3 seconds) and less than a second time interval threshold (e.g., 70 seconds), and / or the activity count per 2 seconds (or any other time interval) is less than a ratio (e.g., an activity count of 8 for 2 seconds of activity count data from IMD 10). Processing circuitry 50 may determine activity counts over a time period based on a signal of accelerometer 64 over the time period crossing one or more thresholds.

[0104] Responsive to detecting that one or more of the heart rate cycles is a SDHR event, processing circuitry 50 may determine a set of event properties for the detected SDHR events to improve specificity of SDHR detection (908). The set of event properties may include the difference between maximum and minimum heart rate, the time difference between maximum and minimum heart rate, the maximum heart rate within each cycle, and the minimum heart rate. In some examples, processing circuitry 50 maycompare the event properties of multiple events to confirm whether a detected event is a SDHR event.

[0105] In some examples, processing circuitry 50 may determine whether a SDHR event has occurred based on whether the difference between the maximum and minimum heart rate is within 3 BPM for 3 consecutive events. Processing circuitry 50 may compare the difference between the maximum and minimum heart rate for events 90 by determining the absolute value of the difference between the differences of events 90.

[0106] In some examples, processing circuitry 50 may determine whether a SDHR event has occurred based on whether the time difference between maximum and minimum heart rate for 3 consecutive events is within 6 secs. Processing circuitry 50 may compare the time difference between the maximum and minimum heart rate for the three heart rate cycles by determining the absolute value of the difference between the time differences of the various heart rate cycles.

[0107] In some examples, processing circuitry 50 may determine whether a SDHR event has occurred based on whether the maximum heart rate within each cycle is within 3 BPM for 3 consecutive events. Processing circuitry 50 may compare the maximum heart rate for the three heart rate cycles by determining the absolute value of the difference between the maximum heart rates of the various heart rate cycles.

[0108] Processing circuitry 50 may determine whether a SDHR event has occurred based on whether the minimum heart rate within each cycle is within 3 BPM for 3 consecutive events. For example, the minimum heart rate for first event 90A may be 72 BPM. The minimum heart rate for second event 90B may be 70 BPM. The minimum heart rate for third event 90C may be 73 BPM. Processing circuitry 50 may compare the minimum heart rate for the three heart rate cycles by determining the absolute value of the difference between the minimum heart rates of the various heart rate cycles.

[0109] Processing circuitry 50 may use satisfaction of one or more of the above criteria to confirm the occurrence of a SDHR event and output an indication of sleep disordered breathing (910). By using the various criteria disclosed herein, system 2 may more accurately and precisely detect sleep disordered breathing. In this way, system 2 may reduce the detection of false-positives while still collecting clinically significant results, thereby improving the medical care of patient 4 (e.g., by reducing visits to the hospital, by continuously monitoring patient 4 for sleep disordered breathing, etc.).

[0110] The following examples are illustrative of the techniques described herein.

[0111] Example 1 : A system includes an implantable medical device includes determine an occurrence of a first sleep disordered heart rate (SDHR) event within a first heart rate cycle based on a set of heart rate cycle properties for the first heart rate cycle; determine an occurrence of a second SDHR event within a second heart rate cycle based on the set of heart rate cycle properties for the second heart rate cycle; confirm the occurrence of at least one of the first SDHR event or the second SDHR event based on a set of event properties for the first SDHR event and for the second SDHR event; and responsive to confirming the occurrence of at least one of the first SDHR event or the second SDHR event, output an indication that the patient experiences sleep disordered breathing.

[0112] Example 2 : The system of example 1, wherein the set of heart rate cycle properties comprises one or more of a peak-to-valley heart rate variation, a cycle length, a peak-to-valley time interval, or an activity count.

[0113] Example 3: The system of example 2, wherein, to determine the occurrence of the first SDHR event, the processing circuitry is configured to determine whether the peak-to-valley heart rate variation for the first heart rate cycle is greater than 6 beats per minute and less than 50 beats per minute, and wherein, to determine the occurrence of the second heart rate cycle, the processing circuitry is configured to determine whether the peak-to-valley heart rate variation for the second heart rate cycle is greater than 6 beats per minute and less than 50 beats per minute.

[0114] Example 4 : The system of example 2 or 3, wherein, to determine the occurrence of the first SDHR event, the processing circuitry is configured to determine whether the cycle length for the first heart rate cycle is greater than or equal to 10 seconds and less than or equal to 22 seconds, and wherein, to determine the occurrence of the second SDHR event, the processing circuitry is configured to determine whether the cycle length for the second heart rate cycle is greater than or equal to 10 seconds and less than or equal to 22 seconds.

[0115] Example 5: The system of any one or more of examples 2 to 4, wherein, to determine the occurrence of the first SDHR event, the processing circuitry is configured to determine whether the peak-to-valley time interval for the first heart rate cycle is greater than 3 seconds and less than 70 seconds, and wherein, to determine the occurrence of thesecond SDHR event, the processing circuitry is configured to determine whether the peak- to-valley time interval for the second heart rate cycle is greater than 3 seconds and less than 70 seconds.

[0116] Example 6: The system of any one or more of examples 2 to 5, wherein, to determine the occurrence of the first SDHR event, the processing circuitry is configured to determine whether the activity count for the first heart rate cycle is less than 8, and wherein, to determine the occurrence of second first SDHR event, the processing circuitry is configured to determine whether the activity count for the second heart rate cycle is less than 8.

[0117] Example 7: The system of any one or more of examples 1 to 6, wherein the processing circuitry is further configured to: determine a short-term average heart rate of the cardiac activity; determine a long-term average heart rate of the cardiac activity; and determine that a heart rate cycle has started based on a first time the short-term average heart rate changes from being less than the long-term average heart rate to being greater than the long-term average of the heart rate; determine that the heart rate cycle has ended based on a second time the short-term average heart rate changes from being less than the long-term average heart rate to being greater than the long-term average of the heart rate.

[0118] Example 8 : The system of any one or more of examples 1 to 7, wherein confirming the occurrence of at least one of the first SDHR event or the second SDHR event comprises comparing the set of event properties for the first SDHR event with the set of event properties for the second SDHR event.

[0119] Example 9 : The system of any one or more of examples 1 to example 8, wherein comparing the set of event properties for the first SDHR event with the set of event properties for the second SDHR event comprises determining whether a difference between at least one event property of the set of event properties for the first SDHR event and the at least one event property of the set of event properties for the second SDHR event satisfies a threshold.

[0120] Example 10: The system of any one or more of examples 1 to 9, wherein the set of event properties comprises one or more of a difference between a maximum heart rate and a minimum heart rate, a time difference between maximum and minimum heart rate, the maximum heart rate, or the minimum heart rate.

[0121] Example 11: The system of example 10, wherein to confirm the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is configured to determine whether the difference between maximum and minimum heart rate for the first SDHR event and the second SDHR event is less than or equal to 3 BPM.

[0122] Example 12: The system of example 10 or 11, wherein to confirm the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is configured to determine whether the time difference between maximum and minimum heart rate for the first SDHR event and the second SDHR event is less than or equal to 6 seconds.

[0123] Example 13: The system of any one or more of examples 10 to 12, wherein to confirm the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is configured to determine whether the maximum heart rate for the first SDHR event is within 3 BPM of the maximum heart rate for the second SDHR event.

[0124] Example 14: The system of any one or more of examples 10 to 13, wherein to confirm the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is configured to determine whether the minimum heart rate for the first SDHR event is within 3 BPM of the minimum heart rate for the second SDHR event.

[0125] Example 15: The system of any one or more of examples 1 to 14, wherein to determine the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is further configured to: determine a first difference signal for a window of the cardiac signal; determine a second difference signal for the window of the cardiac signal; count a number of noisy samples in the cardiac signal based on the first difference signal and the second difference signal; determine whether the window of the cardiac signal is noisy based on the number of noisy samples; and determine the occurrence of the at least one of the first SDHR event or the second SDHR event based on whether the window of the cardiac signal is noisy.

[0126] Example 16: An implantable medical device includes sensing circuitry configured to sense cardiac activity of a patient; and processing circuitry configured to: determine an occurrence of a first sleep disordered heart rate (SDHR) event within a first heart rate cycle based on a set of heart rate cycle properties for the first heart rate cycle; determine an occurrence of a second SDHR event within a second heart rate cycle based on the set of heart rate cycle properties for the second heart rate cycle; confirm theoccurrence of at least one of the first SDHR event or the second SDHR event based on a set of event properties for the first SDHR event and for the second SDHR event; and responsive to confirming the occurrence of at least one of the first SDHR event or the second SDHR event, output an indication that the patient experiences sleep disordered breathing.

[0127] Example 17: The implantable medical device of example 16, wherein the set of heart rate cycle properties comprises one or more of a peak-to-valley heart rate variation, a cycle length, a peak-to-valley time interval, or an activity count.

[0128] Example 18: The implantable medical device of example 17, wherein, to determine the occurrence of the first SDHR event, the processing circuitry is configured to determine whether the peak-to-valley heart rate variation for the first heart rate cycle is greater than 6 beats per minute and less than 50 beats per minute, and wherein, to determine the occurrence of the second heart rate cycle, the processing circuitry is configured to determine whether the peak-to-valley heart rate variation for the second heart rate cycle is greater than 6 beats per minute and less than 50 beats per minute.

[0129] Example 19: The implantable medical device of example 17 or 18, wherein, to determine the occurrence of the first SDHR event, the processing circuitry is configured to determine whether the cycle length for the first heart rate cycle is greater than or equal to 10 seconds and less than or equal to 22 seconds, and wherein, to determine the occurrence of the second SDHR event, the processing circuitry is configured to determine whether the cycle length for the second heart rate cycle is greater than or equal to 10 seconds and less than or equal to 22 seconds.

[0130] Example 20: The implantable medical device of any one or more of examples 17 to 19, wherein, to determine the occurrence of the first SDHR event, the processing circuitry is configured to determine whether the peak-to-valley time interval for the first heart rate cycle is greater than 3 seconds and less than 70 seconds, and wherein, to determine the occurrence of the second SDHR event, the processing circuitry is configured to determine whether the peak-to-valley time interval for the second heart rate cycle is greater than 3 seconds and less than 70 seconds.

[0131] Example 21: The implantable medical device of any one or more of examples 17 to 20, wherein, to determine the occurrence of the first SDHR event, the processing circuitry is configured to determine whether the activity count for the first heart rate cycleis less than 8, and wherein, to determine the occurrence of second first SDHR event, the processing circuitry is configured to determine whether the activity count for the second heart rate cycle is less than 8.

[0132] Example 22: The implantable medical device of any one or more of examples 16 to 21, wherein the processing circuitry is further configured to: determine a short-term average heart rate of the cardiac activity; determine a long-term average heart rate of the cardiac activity; and determine that a heart rate cycle has started based on a first time the short-term average heart rate changes from being less than the long-term average heart rate to being greater than the long-term average of the heart rate; determine that the heart rate cycle has ended based on a second time the short-term average heart rate changes from being less than the long-term average heart rate to being greater than the long-term average of the heart rate.

[0133] Example 23: The implantable medical device of any one or more of examples 16 to 22, wherein confirming the occurrence of at least one of the first SDHR event or the second SDHR event comprises comparing the set of event properties for the first SDHR event with the set of event properties for the second SDHR event.

[0134] Example 24: The implantable medical device of any one or more of examples 16 to example 23, wherein comparing the set of event properties for the first SDHR event with the set of event properties for the second SDHR event comprises determining whether a difference between at least one event property of the set of event properties for the first SDHR event and the at least one event property of the set of event properties for the second SDHR event satisfies a threshold.

[0135] Example 25: The implantable medical device of any one or more of examples 16 to 24, wherein the set of event properties comprises one or more of a difference between a maximum heart rate and a minimum heart rate, a time difference between maximum and minimum heart rate, the maximum heart rate, or the minimum heart rate.

[0136] Example 26: The implantable medical device of example 25, wherein to confirm the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is configured to determine whether the difference between maximum and minimum heart rate for the first SDHR event and the second SDHR event is less than or equal to 3 BPM.

[0137] Example 27: The implantable medical device of example 25 or 26, wherein to confirm the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is configured to determine whether the time difference between maximum and minimum heart rate for the first SDHR event and the second SDHR event is less than or equal to 6 seconds.

[0138] Example 28: The implantable medical device of any one or more of examples 25 to 27, wherein to confirm the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is configured to determine whether the maximum heart rate for the first SDHR event is within 3 BPM of the maximum heart rate for the second SDHR event.

[0139] Example 29: The implantable medical device of any one or more of examples 25 to 28, wherein to confirm the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is configured to determine whether the minimum heart rate for the first SDHR event is within 3 BPM of the minimum heart rate for the second SDHR event.

[0140] Example 30: The implantable medical device of any one or more of examples 16 to 29, wherein to determine the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is further configured to: determine a first difference signal for a window of the cardiac signal; determine a second difference signal for the window of the cardiac signal; count a number of noisy samples in the cardiac signal based on the first difference signal and the second difference signal; determine whether the window of the cardiac signal is noisy based on the number of noisy samples; and determine the occurrence of the at least one of the first SDHR event or the second SDHR event based on whether the window of the cardiac signal is noisy.

[0141] Example 31: A method includes sensing, by sensing circuitry of an implantable medical device, cardiac activity of a patient; determining, by processing circuitry, an occurrence of a first sleep disordered heart rate (SDHR) event within a first heart rate cycle based on a set of heart rate cycle properties for the first heart rate cycle; determining, by the processing circuitry, an occurrence of a second SDHR event within a second heart rate cycle based on the set of heart rate cycle properties for the second heart rate cycle; confirming, by the processing circuitry, the occurrence of at least one of the first SDHR event or the second SDHR event based on a set of event properties for the first SDHRevent and for the second SDHR event; and responsive to confirming the occurrence of at least one of the first SDHR event or the second SDHR event, outputting, by the processing circuitry, an indication that the patient experiences sleep disordered breathing.

[0142] As used herein, an implantable medical device (IMD) may include, be, or be part of a variety of devices or integrated systems, such as, but not limited to, implantable cardiac monitors (ICMs), implantable pacemakers, including those that deliver cardiac resynchronization therapy (CRT), implantable cardioverter defibrillators (ICDs), diagnostics device, cardiac device, etc. Various examples have been described that include detecting episodes of sleep disordered breathing using heart rate cycle length metrics. In addition, pulmonary therapy may be provided to mitigate the severity of the sleep disordered breathing episode or counter the effects of the sleep disordered breathing episode. Any combination of detection and therapy for sleep disordered breathing episodes is contemplated.

[0143] Various aspects of the techniques may be implemented within one or more processing circuitries, including one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components, embodied in external devices, such as physician or patient external devices, electrical stimulators, or other devices. The term “processing circuitry” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry or any other equivalent circuitry.

[0144] In one or more examples, the functions described in this disclosure may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on, as one or more instructions or code, a computer-readable medium and executed by a hardware -based processing unit. Computer- readable media may include computer-readable storage media forming a tangible, non- transitory medium. Instructions may be executed by one or more processing circuitries, such as one or more DSPs, ASICs, FPGAs, general purpose microprocessors, or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processing circuitry,” as used herein may refer to one or more of any of the foregoing structure or any other structure suitable for implementation of the techniques described herein.

[0145] In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and / or software modules. Depiction of differentfeatures as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality 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. Also, the techniques could be fully implemented in one or more circuits or logic elements. The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including an IMD, an external device, a combination of an IMD and external device, an integrated circuit (IC) or a set of ICs, and / or discrete electrical circuitry, residing in an IMD and / or external device.

Claims

WHAT IS CLAIMED IS:

1. A system comprising: an implantable medical device comprising sensing circuitry configured to sense cardiac activity of a patient; and processing circuitry configured to: determine an occurrence of a first sleep disordered heart rate (SDHR) event within a first heart rate cycle based on a set of heart rate cycle properties for the first heart rate cycle; determine an occurrence of a second SDHR event within a second heart rate cycle based on the set of heart rate cycle properties for the second heart rate cycle; confirm the occurrence of at least one of the first SDHR event or the second SDHR event based on a set of event properties for the first SDHR event and for the second SDHR event; and responsive to confirming the occurrence of at least one of the first SDHR event or the second SDHR event, output an indication that the patient experiences sleep disordered breathing.

2. The system of claim 1, wherein the set of heart rate cycle properties comprises one or more of a peak-to-valley heart rate variation, a cycle length, a peak-to-valley time interval, or an activity count.

3. The system of claim 2, wherein, to determine the occurrence of the first SDHR event, the processing circuitry is configured to determine whether the peak-to-valley heart rate variation for the first heart rate cycle is greater than 6 beats per minute and less than 50 beats per minute, and wherein, to determine the occurrence of the second heart rate cycle, the processing circuitry is configured to determine whether the peak-to-valley heart rate variation for the second heart rate cycle is greater than 6 beats per minute and less than 50 beats per minute.

4. The system of claim 2 or 3, wherein, to determine the occurrence of the first SDHR event, the processing circuitry is configured to determine whether the cycle length for the first heart rate cycle is greater than or equal to 10 seconds and less than or equal to 22 seconds, and wherein, to determine the occurrence of the second SDHR event, the processing circuitry is configured to determine whether the cycle length for the second heart rate cycle is greater than or equal to 10 seconds and less than or equal to 22 seconds.

5. The system of any one or more of claims 2 to 4, wherein, to determine the occurrence of the first SDHR event, the processing circuitry is configured to determine whether the peak-to-valley time interval for the first heart rate cycle is greater than 3 seconds and less than 70 seconds, and wherein, to determine the occurrence of the second SDHR event, the processing circuitry is configured to determine whether the peak-to-valley time interval for the second heart rate cycle is greater than 3 seconds and less than 70 seconds.

6. The system of any one or more of claims 2 to 5, wherein, to determine the occurrence of the first SDHR event, the processing circuitry is configured to determine whether the activity count for the first heart rate cycle is less than 8, and wherein, to determine the occurrence of second first SDHR event, the processing circuitry is configured to determine whether the activity count for the second heart rate cycle is less than 8.

7. The system of any one or more of claims 1 to 6, wherein the processing circuitry is further configured to: determine a short-term average heart rate of the cardiac activity; determine a long-term average heart rate of the cardiac activity; and determine that a heart rate cycle has started based on a first time the short-term average heart rate changes from being less than the long-term average heart rate to being greater than the long-term average of the heart rate;determine that the heart rate cycle has ended based on a second time the short-term average heart rate changes from being less than the long-term average heart rate to being greater than the long-term average of the heart rate.

8. The system of any one or more of claims 1 to 7, wherein confirming the occurrence of at least one of the first SDHR event or the second SDHR event comprises comparing the set of event properties for the first SDHR event with the set of event properties for the second SDHR event.

9. The system of any one or more of claims 1 to claim 8, wherein comparing the set of event properties for the first SDHR event with the set of event properties for the second SDHR event comprises determining whether a difference between at least one event property of the set of event properties for the first SDHR event and the at least one event property of the set of event properties for the second SDHR event satisfies a threshold.

10. The system of any one or more of claims 1 to 9, wherein the set of event properties comprises one or more of a difference between a maximum heart rate and a minimum heart rate, a time difference between the maximum and the minimum heart rate, the maximum heart rate, or the minimum heart rate.

11. The system of claim 10, wherein to confirm the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is configured to determine whether the difference between the maximum and the minimum heart rate for the first SDHR event and the second SDHR event is less than or equal to 3 BPM.

12. The system of claim 10 or 11, wherein to confirm the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is configured to determine whether the time difference between the maximum and the minimum heart rate for the first SDHR event and the second SDHR event is less than or equal to 6 seconds.

13. The system of any one or more of claims 10 to 12, wherein to confirm the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is configured to determine whether the difference between the maximum heart rate for the first SDHR event and the second SDHR event is less than or equal to 3 BPM.

14. The system of any one or more of claims 10 to 13, wherein to confirm the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is configured to determine whether the difference between the minimum heart rate for the first SDHR event and the second SDHR event is less than or equal to 3 BPM.

15. The system of any one or more of claims 1 to 14, wherein to determine the occurrence of at least one of the first SDHR event or the second SDHR event, the processing circuitry is further configured to: determine a first difference signal for a window of the cardiac signal; determine a second difference signal for the window of the cardiac signal; count a number of noisy samples in the cardiac signal based on the first difference signal and the second difference signal; determine whether the window of the cardiac signal is noisy based on the number of noisy samples; and determine the occurrence of the at least one of the first SDHR event or the second SDHR event based on whether the window of the cardiac signal is noisy.

16. An implantable medical device comprising: sensing circuitry configured to sense cardiac activity of a patient; and processing circuitry configured to: determine an occurrence of a first sleep disordered heart rate (SDHR) event within a first heart rate cycle based on a set of heart rate cycle properties for the first heart rate cycle;determine an occurrence of a second SDHR event within a second heart rate cycle based on the set of heart rate cycle properties for the second heart rate cycle; confirm the occurrence of at least one of the first SDHR event or the second SDHR event based on a set of event properties for the first SDHR event and for the second SDHR event; and responsive to confirming the occurrence of at least one of the first SDHR event or the second SDHR event, output an indication that the patient experiences sleep disordered breathing.

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