Ambient noise detection to reduce cardiac events

Abstract

A medical device system includes a medical device including one or more sound sensors configured to generate a sound signal including noise experienced by a patient, and processing circuitry. The processing circuit is configured to determine one or more noise levels experienced by the patient based on the sound signal, determine a cardiac risk for the patient based at least in part on the one or more noise levels, and generate an output corresponding to the cardiac risk to a computing device of the patient or another user.

Description

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 381,452, filed Oct. 28, 2022, the entire content of which is incorporated herein by reference. Technical Field

[0002] The present disclosure generally relates to systems including medical devices and, more particularly, to using such systems to monitor patient health. Background Art

[0003] A variety of devices are configured to monitor a patient's physiological signals. Some types of devices may also be used to monitor one or more environmental conditions of the environment in which the patient finds themselves. Such devices include implantable or wearable medical devices, as well as a variety of wearable health or fitness tracking devices. Physiological signals sensed by such devices include, for example, electrocardiogram (ECG) signals, electroencephalogram (EEG) signals, respiratory signals, perfusion signals, activity and / or pose signals, pressure signals, blood oxygen saturation signals, body composition, and blood glucose or other blood component signals. Generally speaking, using these signals, such devices facilitate monitoring and evaluating patient health outside of a clinical setting over months or years. Summary of the Invention

[0004] Generally speaking, the present disclosure relates to techniques for patient health monitoring and risk management. More specifically, the present disclosure describes techniques for using, for example, a medical device (e.g., an implantable medical device (IMD)) to record (and in some examples process) the ambient noise level in a patient's environment. These techniques also include determining the risk of heart disease based on the noise level.

[0005] Excessive noise in a patient's environment may be associated with the development or worsening of a variety of heart diseases, such as heart failure, arrhythmia, and coronary artery disease, or other patient conditions that are risk factors for heart disease, such as hypertension, diabetes, and obesity. Examples of conditions that may expose a patient to excessive noise include traffic and certain occupations. Some people may live or work in areas where they are frequently and / or chronically exposed to high ambient noise. Excessive noise levels may activate the stress response, leading to dysregulation of cardiovascular function, cardiovascular tissue remodeling, and / or cell death.

[0006] The techniques of the present disclosure can improve the functionality of a medical device system to monitor a patient's cardiovascular health. For example, a medical device of the system (e.g., an insertable cardiac monitor or other implantable medical device) can be configured to continuously and / or long - term monitor a patient's noise exposure, thereby providing a more comprehensive record of the magnitude and impact of the patient's exposure to excessive noise than might otherwise be possible. In this way, the system can be capable of monitoring a patient over a long period (e.g., about several months or years) without the need for guidance or intervention by a clinician, patient, or another person. Additionally, the system can provide multiple analyses to determine a risk level of heart disease based on applying a standard or predictive model (e.g., a machine - learning model) to the noise levels and, in some cases, based on other physiological data. In this way, the techniques of the present invention can allow a medical device system to more quickly and fully identify exposure to potentially harmful excessive noise and communicate the resulting heart - disease risk to the patient, the patient's clinician, or other interested parties. In some cases, the communication of the heart - disease risk can advantageously facilitate or even include recommendations / directions to take action to remedy the excessive noise exposure and / or reduce the heart - disease risk level. Reducing or eliminating exposure to excessive noise can help reduce or prevent heart disease or slow the progression of the disease. Conventional medical devices lack the ability to continuously monitor noise levels and determine heart - disease risk based on noise levels, and the devices and techniques of the present disclosure represent an improvement in the functionality of medical devices and systems that are beneficial to patients by improving the ability of such devices and systems to monitor heart health.

[0007] In some examples, a medical device system includes a medical device that includes one or more sound sensors configured to generate a sound signal including noise experienced by a patient. The medical device system further includes processing circuitry configured to determine one or more noise levels experienced by the patient based on the sound signal, determine a heart - disease risk of the patient at least in part based on the one or more noise levels, and generate an output corresponding to the heart - disease risk to a computing device of the patient or another user.

[0008] In some examples, a method includes: determining, by processing circuitry of a medical device system including a medical device, one or more noise levels experienced by a patient based on a sound signal generated by a sound sensor of the medical device; determining, by the processing circuitry, a heart - disease risk for the patient at least in part based on the one or more noise levels; and generating, by the processing circuitry, an output corresponding to the heart - disease risk to a computing device of the patient or another user.

[0009] In some examples, a non - transitory computer - readable storage medium includes instructions that, when executed, cause processing circuitry of a medical device system to perform any of the methods described herein.

[0010] This disclosure aims to provide an overview of the subject matter described in this disclosure. This summary is not intended to provide an exclusive or exhaustive explanation of the systems, devices, and methods described in detail in the following drawings and specification. Further details of one or more examples of this disclosure are set forth in the following drawings and specification. Other features, objectives, and advantages will be apparent from the specification, the drawings, and the claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Figure 1 is a block diagram illustrating an exemplary system configured to detect the noise level experienced by a patient and respond to such detection in accordance with one or more techniques of this disclosure.

[0012] Figure 2 is a conceptual diagram of an exemplary apparatus of a system that senses an ambient noise level in accordance with one or more techniques described herein Figure 1 thereof.

[0013] Figure 3A is a perspective view illustrating an implantable cardiac monitor.

[0014] Figure 3B is a perspective view illustrating another implantable cardiac monitor.

[0015] Figure 4 is a block diagram illustrating an exemplary configuration of a medical device operating in accordance with one or more techniques of this disclosure.

[0016] Figure 5 is a block diagram illustrating an exemplary configuration of a computing device operating in accordance with one or more techniques of this disclosure.

[0017] Figure 6 is a block diagram illustrating a logical perspective of an exemplary service operated by an exemplary system in accordance with one or more examples of this disclosure Figure 5 thereof.

[0018] Figure 7 is a flowchart illustrating an exemplary operation for generating an output of a heart disease risk caused by ambient noise in a patient's environment in accordance with one or more techniques of this disclosure.

[0019] Figure 8 is a flowchart illustrating an exemplary operation for generating a noise profile for a patient over a period of time in accordance with one or more techniques described herein.

[0020] Throughout the specification and the drawings, like reference characters denote like elements. DETAILED DESCRIPTION

[0021] Evidence shows an association between excessive noise in the environment and cardiovascular diseases. For example, compared with the normal population, individuals with bilateral high-frequency hearing loss are about twice as likely to develop coronary heart disease. Individuals exposed to higher decibel traffic noise may have a correspondingly higher risk of cardiovascular diseases. According to the technology of the present disclosure, an implantable medical device (IMD) or other medical device can be configured to continuously determine the ambient noise level in the surrounding environment, and an IMD or another component of the system can determine the risk level of heart disease based on the noise level.

[0022] Figure 1 FIG. 4 is a conceptual diagram illustrating an environment of an exemplary medical device system 2 of patient 4 in combination with one or more technologies of the present disclosure. System 2 includes IMD 10. In some examples, IMD 10 is implanted outside the chest cavity of patient 4 (e.g., subcutaneously implanted Figure 1 in the illustrated chest position). IMD 10 can be located near the level of patient 4's heart or near the sternum directly below, e.g., at least partially within the cardiac silhouette. In some examples, IMD 10 takes the form of a LINQ TM Insertable Cardiac Monitor (ICM) (available from Medtronic plc, Dublin, Ireland). According to the technology of the present disclosure, IMD 10 includes one or more sound sensors to continuously record the sound / noise in the environment of patient 4 over an extended period of time. IMD 10 can determine the ambient noise level to which patient 4 is exposed.

[0023] Although in one example, IMD 10 takes the form of an ICM, in other examples, IMD 10 takes the form of any one of various implantable cardioverter defibrillators (ICDs) with intravascular or extravascular leads, such as a pacemaker, an intracardiac or extracardiac defibrillator, a cardiac resynchronization therapy device (CRT-D), a neuromodulation device, an implantable sensor, or a drug pump, etc. Additionally or alternatively, the technology of the present disclosure can be used to determine the ambient noise level to which patient 4 is exposed based on signals collected by one or more external medical devices, such as a patch device, a wearable device (e.g., a smartwatch or a fitness tracking device), a wearable sensor, or other external devices of patient 4 or in the patient's environment (such as a smartphone, a smart home device, other Internet of Things (IoT) devices, or any combination thereof).

[0024] Clinicians sometimes diagnose a patient with a medical condition or monitor the progression of a medical condition based on one or more observed physiological signals collected by physiological sensors such as electrodes, optical sensors, chemical sensors, temperature sensors, acoustic sensors, and motion sensors. In some cases, a clinician applies a non-invasive sensor to a patient during an outpatient or inpatient visit to sense one or more physiological signals while the patient is at a medical appointment at a clinic. Additionally, a clinician can ask a patient questions, for example, verbally or via a survey, to identify symptoms and environmental / behavioral factors that may affect the patient's health.

[0025] However, in some examples, physiological markers of a patient's condition occur when the patient is outside of a clinic. Thus, in these examples, a clinician may not be able to observe the physiological markers required to diagnose a patient with a medical condition. Additionally, it may be beneficial to monitor one or more patient parameters over an extended period of time (e.g., days, weeks, or months) such that one or more parameters can be analyzed to identify unique physiological markers of a patient's symptoms or medical condition. Further, patients often provide incomplete information in response to questions about symptoms and environmental / behavioral factors. In Figure 1 the example shown, the IMD 10 is implanted within patient 4 to continuously record one or more physiological signals of patient 4 over an extended period of time. Additionally, in accordance with the techniques of the present disclosure, the IMD 10 includes one or more acoustic sensors to continuously record sounds / noises in the environment of patient 4 over an extended period of time, e.g., on a monthly or annual basis. To continuously monitor the noise in the environment of patient 4, the IMD 10 can continuously measure the noise and determine a noise level, a heart disease risk, or other metrics described herein on a periodic and / or triggered basis without intervention or direction from a clinician, patient 4, or another user.

[0026] In addition to acoustic sensors, the IMD 10 can include any one or more electrodes, optical sensors, motion sensors (e.g., accelerometers), temperature sensors, chemical sensors, pressure sensors, or any combination thereof and any additional sensors that can be part of the IMD 10. Such sensors can sense one or more signals indicative of one or more physiological parameters of the patient. One or more physiological parameters of the patient can indicate a patient condition, including symptoms or diseases. Various features can be extracted from the sensor signals, such as: the amount of deviation from a baseline; the timing of the deviation; the absolute value of a physiological parameter corresponding to the patient at a particular point in time (e.g., a heart rate of 80 bpm).

[0027] The IMD 10 can be configured to communicate with one or more computing devices 12 such as Figure 1The patient computing devices 12A and 12B shown in the figure communicate wirelessly. The computing device 12 can be a patient computing device or a clinician programming device configured for use in an environment such as a home, clinic, or hospital. In some examples, the computing device 12 can include a programmer, an external monitor, or a consumer device such as a smartphone or a tablet computer.

[0028] As Figure 1 shown, the computing device 12 can be coupled to a remote health monitoring system (HMS) 26 via a network 16. As Figure 1 shown, the HMS 26 can be implemented by a processing circuit 22 and a memory 24 of a remote computing system (e.g., a cloud computing system such as can be obtained from Medtronic plc, Dublin, Ireland). ) The computing device 12 can send data to the computing system 20 via the network 16, such as data received from the IMD 10 and data collected by the computing device 12. The computing device 12 can communicate via near-field communication technologies (e.g., inductive coupling, near-field communication (NFC), or other communication technologies that can operate in a range of less than 10 cm to 20 cm) and far-field communication technologies (e.g., 802.11 or specification sets such as cellular network communication according to 3G, 4G, or 5G protocols, or other communication technologies that can operate in a range greater than that of near-field communication technologies).

[0029] To implement the HMS 26, the processing circuit 22 of the computing system 20 can collect and process the noise level and other patient parameter data received from the IMD 10, as described herein. Based on this analysis, the processing circuit 22 can determine the risk of heart disease, including the risk of diseases that cause heart disease, such as the risk of heart failure, arrhythmia, or hypertension. The processing circuit 22 can generate outputs of the noise level, parameter data, risk level, or other patient health metrics, such as messages, alerts, reports, network communications, or other communications, to the patient 4 via the computing device 12 and to other interested parties via their computing devices 14A and 14B (collectively referred to as "computing devices 14"). Other interested parties can include clinicians, caregivers, and family members of the patient 4. In some examples, the information provided by the processing circuit 22 can identify the time and location at which the patient 4 was exposed to excessive noise levels (e.g., determined based on global positioning system (GPS) data from the computing device 12), such that the source of the noise levels can be identified and remedied or avoided.

[0030] In some examples, the sound sensor of the IMD 10 and one or more other sensors (e.g., electrodes, motion sensors, optical sensors, temperature sensors, or any combination thereof) can sense one or more signals, where each value of the signal represents a measurement within a corresponding time interval, e.g., a periodic measurement. Multiple values can represent a sequence of parameter values measured at cyclic time intervals.

[0031] In another example, the IMD 10 can perform a measurement in response to a patient notification that a measurement should begin. In another example, the IMD 10 can continuously perform parameter measurements. In this way, the IMD 10 can be configured to more effectively track the patient's condition because the patient does not need to track the parameters in a clinic since the IMD 10 is implanted in the patient 4 and is configured to perform parameter measurements according to cyclic or other time intervals without missing an interval. In some examples, values can be measured or grouped based on a particular time of day, e.g., values measured during a window of time during the day or during a window of time during the night. In addition to the time of day, the IMD 10 can also measure the noise level or other parameters in response to a trigger, such as determining or receiving an indication that a physiological parameter or metric of the patient's condition has changed from a baseline or recent average by more than a threshold, or determining that the patient 4 has entered or left a geofence based on a location indicated by the computing device 12 (e.g., based on the global positioning system (GPS) functionality of the computing device 12).

[0032] Generally, the techniques of the present invention can be executed by the processing circuitry of one or more devices of the system 2, such as the processing circuitry of the IMD 10, the computing device 12, or one or more of the processing circuitry 22 of the computing system 20.

[0033] Figure 2 is an illustration of the IMD 10 and the computing device 12 that sense the ambient noise level according to one or more of the techniques described herein. The IMD 10 includes one or more sound sensors that are configured to generate a signal in response to ambient noise 30 and thereby sense the noise 30 in the environment of the patient 4. As will be described in more detail below, the IMD 10 processes the signal generated by the one or more sound sensors to determine the noise level to which the patient 4 has been exposed. Figure 1 is a conceptual diagram of the IMD 10 and the computing device 12 that sense the ambient noise level according to one or more of the techniques described herein. The IMD 10 includes one or more sound sensors that are configured to generate a signal in response to ambient noise 30 and thereby sense the noise 30 in the environment of the patient 4. As will be described in more detail below, the IMD 10 processes the signal generated by the one or more sound sensors to determine the noise level to which the patient 4 has been exposed.

[0034] Figure 2Illustrates source 32 of noise 30. Exemplary noise sources 32 include traffic, machinery, aircraft, explosions, or other events, as well as musical performances. In some examples, one or more components of system 2 may allow identification of the noise source 32 or the condition (e.g., situation) of patient 4's exposure to the noise source 32. For example, one or both of IMD 10 and computing device 12 may be able to identify the time of day and / or location of patient 4 associated with a noise level. By presenting such information to patient 4 or other interested parties, system 2 may allow patient 4 to avoid the noise source in the future. By presenting such information to patient 4 or other interested parties, system 2 may allow patient 4 to take other actions to reduce exposure to the noise source 32, such as wearing hearing protection equipment when they cannot be avoided. In some cases, patient 4 may not be aware that their noise exposure is excessive or poses a health risk. In some cases, if patient 4 is exposed to more long-term environmental noise, e.g., for more than 30 days, actions including the following may be recommended: adding sound insulation materials to the home or other environment where patient 4 is exposed to the noise source, or moving to a less populated area. In some examples, by monitoring the noise level (and in some cases determining the heart disease risk) after taking actions to reduce exposure to the noise, system 2 may provide feedback to patient 4 or other users indicating the effectiveness of such actions.

[0035] In some examples, one or both of computing device 12 or another computing device may also include one or more sound sensors. In such examples, both IMD 10 and the other device may be configured to determine the noise level based on their respective and simultaneous sensing of the same noise 30. The processing circuitry of one or both of IMD 10 and computing device 12 may calibrate one or more sound sensors of IMD 10 or IMD 10's determination of the noise level based on the signal sensed by computing device 12 or the determined noise level. Such calibration may, for example, allow IMD 10 to determine the decibel level of noise 30 at the location of patient 4 outside patient 4 based on the signal sensed by one or more sensors of IMD 10 inside patient 4. In some examples, one or both of computing device 12 are configured to emit one or more sounds having a known noise level (e.g., decibel level). The processing circuitry of system 2 (e.g., IMD 10 and / or computing device 12) may calibrate one or more sound sensors of IMD 10 or IMD 10's determination of the noise level based on the noise level of the signal emitted by computing device 12.

[0036] Figure 3A is a perspective view of an insertable cardiac monitor 10A, which may be an IMD as an ICM Figure 1 of the exemplary configuration of IMD 10. In Figure 3AIn the example shown, the IMD 10A can be embodied as a monitoring device having a housing 42, a proximal electrode 46A, and a distal electrode 46B. The housing 42 can also include a first major surface 44, a second major surface 48, a proximal end 50, and a distal end 52. The housing 42 encapsulates the electronic circuitry located inside the IMD 10A and protects the circuitry contained therein from body fluids. The housing 42 can be hermetically sealed and configured for subcutaneous implantation. Electrical feedthroughs provide electrical connection to the electrodes 46A and 46B.

[0037] In Figure 3A In the example shown, the IMD 10A is defined by a length L, a width W, and a thickness or depth D, and is in the form of an elongated rectangular prism, where the length L is much greater than the width W, which in turn is greater than the depth D. In one example, the geometry of the IMD 10A - specifically, the width W greater than the depth D - is chosen to allow the IMD 10A to be inserted beneath the patient's skin using minimally invasive surgery and to remain in a desired orientation during insertion. For example, Figure 3A The device shown includes a radial asymmetry (specifically, a rectangular shape) along the longitudinal axis that maintains the device in the correct orientation after insertion. For example, the spacing between the proximal electrode 46A and the distal electrode 46B can be in the range of 5 millimeters (mm) to 55 mm, 30 mm to 55 mm, 35 mm to 55 mm, and 40 mm to 55 mm, and can be any range or single spacing between 5 mm and 60 mm. Additionally, the length L of the IMD 10A can be in the range of 30 mm to about 70 mm. In other examples, the length L can be in the range of 5 mm to 60 mm, 40 mm to 60 mm, 45 mm to 60 mm, and can be any length or length range between about 30 mm and about 70 mm. Additionally, the width W of the major surface 14 can be in the range of 3 mm to 15 mm, 3 mm to 10 mm, or 5 mm to 15 mm, and can be any single width or width range between 3 mm and 15 mm. The thickness of the depth D of the IMD 10A can be in the range of 2 mm to 15 mm, 2 mm to 9 mm, 2 mm to 5 mm, 5 mm to 15 mm, and can be any single depth or depth range between 2 mm and 15 mm. Additionally, the IMD 10A according to examples of the present disclosure has a geometry and size designed for ease of implantation and patient comfort. Examples of the IMD 10A described in the present disclosure can have a volume of three cubic centimeters (cm) or less, 1.5 cubic centimeters or less, or any volume between three cubic centimeters and 1.5 cubic centimeters.

[0038] In Figure 3AIn the example shown, once inserted into the patient, the first major surface 44 faces outward, toward the patient's skin, while the second major surface 48 is located in a position opposite the first major surface 44. Additionally, in Figure 3A the example shown, the proximal end 50 and the distal end 52 are rounded to reduce discomfort and irritation to surrounding tissue once inserted beneath the patient's skin. The IMD 10A (including the apparatus and method for inserting the IMD 10A) is described, for example, in U.S. Patent Publication No. 2014 / 0276928, which is incorporated herein by reference in its entirety.

[0039] The proximal electrode 46A is located at or near the proximal end 50, and the distal electrode 46B is located at or near the distal end 52. The proximal electrode 46A and the distal electrode 46B are used to sense electrocardiogram (EGM) signals outside the chest cavity, which may be submuscular or subcutaneous. The ECG signals may be stored in the memory of the IMD 10A, and the data may be transmitted via the integrated antenna 60A to another device, which may be another implantable device or an external device such as the computing device 12. In some examples, the electrodes 46A and 46B may additionally or alternatively be used to sense any biopotential signals of interest (which may be, for example, electrogram (EGM), electroencephalogram (EEG), electromyogram (EMG), or nerve signals) from any implant location, or to measure impedance.

[0040] In Figure 3A the example shown, the proximal electrode 46A is located at or adjacent to the proximal end 50, and the distal electrode 46B is located at or adjacent to the distal end 52. In this example, the distal electrode 46B is not limited to a flat outward-facing surface, but may extend from the first major surface 44 around the circular edge 54 and / or the end surface 56 to the second major surface 48, such that the electrode 46B has a three-dimensional curved configuration. In some examples, the electrode 46B is a non-insulated portion of a metallic (e.g., titanium) portion of the housing 42.

[0041] In Figure 3A the example shown, the proximal electrode 46A is located on the first major surface 44 and is substantially flat and outward-facing. However, in other examples, the proximal electrode 46A may utilize the three-dimensional curved configuration of the distal electrode 46B, thereby providing a three-dimensional proximal electrode (not shown in this example). Similarly, in other examples, the distal electrode 46B may utilize a substantially flat outward-facing electrode located on the first major surface 44, which is similar to the electrode shown with respect to the proximal electrode 46A.

[0042] Various electrode configurations allow for configurations in which the proximal electrode 46A and the distal electrode 46B are located on both the first major surface 44 and the second major surface 48. In other configurations, such as Figure 3A the configuration shown, only one of the proximal electrode 46A and the distal electrode 46B is located on both the major surfaces 44 and 48, while in other configurations, both the proximal electrode 46A and the distal electrode 46B are located on one of the first major surface 44 or the second major surface 48 (i.e., the proximal electrode 46A is located on the first major surface 44 and the distal electrode 46B is located on the second major surface 48). In another example, the IMD 10A can include electrodes on both the major surfaces 44 and 48 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on the IMD 10A. The electrodes 16A and 16B can be formed of a variety of different types of biocompatible conductive materials (e.g., stainless steel, titanium, platinum, iridium, or alloys thereof), and one or more coatings, such as titanium nitride or fractal titanium nitride, can be utilized.

[0043] In Figure 3A the example shown, the proximal end 50 includes a head assembly 58 that includes one or more of the proximal electrode 46A, the integrated antenna 60A, the anti-migration protrusion 62, and / or the suture hole 64. The integrated antenna 60A is located on the same major surface (e.g., the first major surface 44) as the proximal electrode 46A and is also included as part of the head assembly 58. The integrated antenna 60A allows the IMD 10A to transmit and / or receive data. In other examples, the integrated antenna 60A can be formed on the major surface opposite the proximal electrode 46A, or can be incorporated within the housing 42 of the IMD 10A. In Figure 3A the example shown, the anti-migration protrusion 62 is located adjacent to the integrated antenna 60A and protrudes away from the first major surface 44 to prevent longitudinal movement of the device. In Figure 3A the example shown, the anti-migration protrusion 62 includes a plurality (e.g., nine) of small bumps or protrusions that extend away from the first major surface 44. As discussed above, in other examples, the anti-migration protrusion 62 can be located on the major surface opposite the proximal electrode 46A and / or the integrated antenna 60A. Additionally, in Figure 3A the example shown, the head assembly 58 includes a suture hole 64 that provides another means of securing the IMD 10A to the patient to prevent movement after insertion. In the example shown, the suture hole 64 is located near the proximal electrode 46A. In one example, the head assembly 58 is a molded head assembly made of a polymer or plastic material that can be integrated with or separate from the main portion of the IMD 10A.

[0044] Figure 3B is a perspective view illustrating another IMD 10B, which can be an ICM fromFigure 1 Another exemplary configuration of the IMD 10. Figure 3B The IMD 10B can be configured substantially similar to Figure 3A the IMD 10A, with the differences between them discussed herein.

[0045] The IMD 10B can include a leadless, subcutaneous implantable monitoring device, such as an ICM. The IMD 10B includes a housing having a base 70 and an insulating cover 72. The proximal electrode 46C and the distal electrode 46D can be formed or placed on the outer surface of the cover 72. For example, various circuits and components of the IMD 10B described below with respect to FIG. 3 can be formed or placed on the inner surface of the cover 72 or within the base 70. In some examples, the battery or other power source of the IMD 10B can be included within the base 70. In the illustrated example, the antenna 60B is formed or placed on the outer surface of the cover 72, but in some examples, it can be formed or placed on the inner surface. In some examples, the insulating cover 72 can be positioned over the open base 70 such that the base 70 and the cover 72 enclose the circuits and other components and protect these circuits and other components from fluids such as body fluids. The housing including the base 70 and the insulating cover 72 can be hermetically sealed and configured for subcutaneous implantation.

[0046] The circuits and components can be formed on the inner side of the insulating cover 72, such as by using flip-chip technology. The insulating cover 72 can be flipped onto the base 70. When flipped and placed on the base 70, the components formed on the inner side of the insulating cover 72 of the IMD 10B can be positioned within the gap 74 defined by the base 70. The electrodes 46C and 46D and the antenna 60B can be electrically connected to the circuits formed on the inner side of the insulating cover 72 through one or more through-holes (not shown) formed through the insulating cover 72. The insulating cover 72 can be formed of sapphire (i.e., corundum), glass, parylene, and / or any other suitable insulating material. The base 70 can be formed of titanium or any other suitable material (e.g., a biocompatible material). The electrodes 46C and 46D can be formed of any one of stainless steel, titanium, platinum, iridium, or their alloys. Additionally, the electrodes 46C and 46D can be coated with a material such as titanium nitride or fractal titanium nitride, but other suitable materials and coatings for such electrodes can also be used.

[0047] In Figure 3B the example shown, the housing of the IMD 10B defines a length L, a width W, and a thickness or depth D, and is in the form of an elongated rectangular prism, where the length L is much greater than the width W, which in turn is greater than the depth D, similar to Figure 3Athe IMD 10A. For example, the spacing between the proximal electrode 46C and the distal electrode 46D can be in the range of 5 mm to 50 mm, 30 mm to 50 mm, 35 mm to 45 mm, and can be any single spacing or range of spacings from 5 mm to 50 mm, such as approximately 40 mm. Additionally, the IMD 10B can have a length L in the range from 5 mm to about 70 mm. In other examples, the length L can be in the range of 30 mm to 70 mm, 40 mm to 60 mm, 45 mm to 55 mm, and can be any single length or range of lengths from 5 mm to 50 mm, such as approximately 45 mm. Further, the width W can be in the range of 3 mm to 15 mm, 5 mm to 15 mm, 5 mm to 10 mm, and can 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 the IMD 10B can be in the range of 2 mm to 15 mm, 5 mm to 15 mm, or 3 mm to 5 mm, and can be any single depth or range of depths between 2 mm and 15 mm, such as approximately 4 mm. The IMD 10B can have a volume of three cubic centimeters (cm) or less, or 1.5 cubic centimeters or less (such as approximately 1.4 cubic centimeters).

[0048] In Figure 3B the example shown, once subcutaneously inserted into the patient, the outer surface of the cover 72 faces outward, toward the patient's skin. Additionally, as Figure 3B shown, the proximal end 76 and the distal end 78 are rounded to reduce discomfort and irritation to the surrounding tissue once inserted beneath the patient's skin. Additionally, the edges of the IMD 10B can be rounded.

[0049] Figure 4 is a block diagram illustrating an example configuration of an IMD 10 that exemplifies one or more techniques described herein. In the illustrated example, the IMD 10 includes electrodes 46 (e.g., corresponding to any one of electrodes 46A to 46D), a processing circuit 100, a memory 102, a sensing circuit 104, one or more patient parameter sensors 106, one or more sound sensors 108, and can be connected to an antenna 60 ( Figure 3A and Figure 3B) communication circuit 110. The processing circuit 100 can be operatively coupled to the memory 102, the sensing circuit 104, one or more patient parameter sensors 106, one or more sound sensors 108, and the communication circuit 110. Although the illustrated example includes two electrodes 46, in some examples, an IMD including or coupled to more than two electrodes 46 can implement the techniques of the present disclosure. The IMD 10 also includes a power source 109 to provide operating power to the processing circuit 100, the memory 102, the sensing circuit 104, one or more patient parameter sensors 106, one or more sound sensors 108, and the communication circuit 110.

[0050] The processing circuit 100 can include fixed-function circuitry and / or programmable processing circuitry. The processing circuit 100 can 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, the processing circuit 100 can include multiple components (such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs) and other discrete or integrated logic circuitry. The functions attributed to the processing circuit 100 herein can be embodied as software, firmware, hardware, or any combination thereof.

[0051] The sensing circuit 104 can be coupled to the electrodes 46, for example, to sense electrical signals of the heart of the patient 4 controlled by the processing circuit 100, such as an ECG. In some examples, the sensing circuit 104 can include one or more filters and amplifiers for filtering and amplifying signals received from the electrodes 46, the patient parameter sensors 106, and / or the sound sensors 108. The sensing circuit 104 can include analog-to-digital conversion circuitry for converting the signals into digital samples for analysis by the processing circuit 100 and / or storage in the memory 102.

[0052] The ECG sensed via the electrodes 46 can represent one or more physiological electrical signals corresponding to the heart of the patient 4. For example, the ECG can indicate ventricular depolarization (including the QRS complex with the R wave), atrial depolarization (P wave), ventricular repolarization (T wave), and other events. Information related to the foregoing events (such as the time of one or more of the discrete events or the morphology of such events) can be used for various purposes, such as determining whether an arrhythmia is occurring, predicting whether an arrhythmia is likely to occur, and / or determining the heart disease state or risk level of the patient 4. In some examples, the sensing circuit 104 senses tissue impedance signals via the electrodes 46. The tissue impedance can be measured for various purposes, such as determining perfusion levels, edema, respiratory rate, effort and pattern, and / or heart failure.

[0053] Sensor 106 may include an optical sensor. In some cases, the optical sensor may include two or more light emitters and one or more light detectors. The optical sensor may perform one or more measurements to determine the oxygenation or blood pressure of the tissue of patient 4. Oxygen saturation and blood pressure may indicate one or more patient conditions, such as heart failure, hypertension, sleep apnea, or COPD. In some examples, sensor 106 includes one or more accelerometers. The accelerometer may generate an accelerometer signal that reflects the movement and / or posture of patient 4. In some cases, the accelerometer may collect a triaxial accelerometer signal that indicates the movement of patient 4 within a three-dimensional Cartesian space.

[0054] The sound (or acoustic) sensor 108 may include a piezoelectric crystal or an accelerometer. Such sensors may be configured to generate a signal that varies with sound in the environment of patient 4. In some examples, the sound sensor 108 (such as a piezoelectric sensor) may generate a sound signal without injecting current into the sensor, which may reduce the impact of the continuous operation of the sound sensor on the power supply of the IMD 10. The sound sensor 108 may be attached to the inner surface of the housing of the IMD 10 or otherwise within it, but in other examples may be attached to the outer surface of the IMD 10 or coupled to the IMD 10 via leads.

[0055] The communication circuit 110 may include any suitable hardware, firmware, software, or any combination thereof for communicating with another device such as the computing device 12, another networked computing device, or another IMD or sensor. Under the control of the processing circuit 100, the communication circuit 110 may receive downlink telemetry from the computing device 12 or another device via an internal antenna or an external antenna (e.g., antenna 60), and transmit uplink telemetry to the computing device or another device. The antenna 60 and the communication circuit 110 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.

[0056] In some examples, memory 102 includes computer-readable instructions that, when executed by processing circuitry 100, cause IMD 10 and processing circuitry 100 to perform the various functions attributed herein to IMD 10 and processing circuitry 100. Memory 102 can include any volatile, non-volatile, magnetic, optical, or electrical medium, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), ferroelectric RAM (FRAM), dynamic random access memory (DRAM), flash memory, or any other digital medium. As an example, memory 102 can store programming values for one or more operating parameters of IMD 10. Memory 102 can also store data collected by IMD 10 for transmission to another device using communication circuitry 110 and / or for further analysis by processing circuitry 100.

[0057] IMD 10 is an example of a medical device that includes one or more acoustic sensors 108 configured to generate an acoustic signal that includes noise experienced by patient 4. As Figure 4 shown, memory 102 can store application 120 and data 130 used by IMD 10 (e.g., processing circuitry 100) to perform techniques described in this disclosure related to determining the health of patient 4 based on the acoustic signal.

[0058] For example, application 120 can include a health monitor application 122. A noise component 124 of health monitor application 112 determines a noise level 132 of the noise experienced by patient 4 based on the acoustic signal. In some examples, each noise level in noise level 132 can be stored in memory 102 in association with the time of measurement of the noise level as data 130. In some examples, noise level 132 can be the decibel level of the noise experienced by patient 4. Noise component 124 can apply a table or function from memory 102 to the acoustic signal to determine the decibel level and can calibrate the table or function as described above with respect to Figure 2 that described.

[0059] The noise component 124 may store each determined noise level or only the noise levels exceeding a threshold noise magnitude as the noise level 132. The noise component 124 may store an average value of the noise magnitudes within a time period or other measure of central tendency as the corresponding noise level 132. The noise component 124 may store the maximum value of the noise magnitudes within a time period as the corresponding noise level 132. In some examples, the noise component 124 may store an integral or sum of the sound signal during the period, or a count, percentage, sum, or other representation of the amount of time during which the magnitude of the signal exceeds one or more thresholds, as the noise level 132 for each of a plurality of periods. For example, the noise component 124 may store a value representing the amount of time or percentage during which the decibel level is between 60 decibels and 80 decibels and / or above 80 decibels during a corresponding period as the noise level 132. Each period may be, for example, one second or more, one minute or more, or one hour or more. The ability of the noise component 124 to determine such metrics from the ambient sound signal, a task not feasible for the human mind, may provide an operational advantage in the ability of the IMD 10 to monitor the cardiovascular health of the patient 4.

[0060] Movements of the patient 4 may introduce signal noise in the noise signal generated by the sound sensor 108 or otherwise confound the determination of the noise level 132 by the noise component 124. In some examples, the processing circuit 100 may determine the activity level of the patient 4, for example, based on the accelerometers of the parameter sensor 106 and / or the sound sensor 108. The noise component 124 may be configured to determine whether the activity level of the patient 4 is below an activity threshold and, when the activity level of the patient 4 is below the threshold, determine the noise level 132 based on the sound signal generated by the sound sensor 108.

[0061] In some examples, the risk component 126 of the health monitoring application 122 determines the heart disease risk of the patient 4 based on the noise level 132 and generates an output of the heart disease risk, such as an alert or other message, to the computing device 12 via the communication circuit 110. The risk component 126 may determine the heart disease risk by determining whether the noise level 132 meets one or more noise exposure criteria 134. The noise exposure criteria 134 may include one or more threshold noise magnitudes and / or one or more threshold noise durations. In some examples, the risk component 126 may associate different thresholds with different heart disease risk levels, such as mild, moderate, and extreme or other risk level gradings / designations. In some examples, the heart disease risk level may be a numerical value on a scale, such as from 1 to 10 or 1 to 100. In some examples, the heart disease risk level may be the probability that the patient 4 will experience a worsening of heart disease, for example, within some time frame after the noise exposure.

[0062] In some examples, different noise exposure criteria 134 can be associated with different durations. For example, a first noise exposure criterion 134 (e.g., a threshold) can be compared to one or more noise levels 132 representing instantaneous or short-term noise exposure, while a second noise exposure criterion 134 (e.g., a threshold) can be compared to noise levels 132 representing longer-term or cumulative noise exposure (e.g., over one or more days or within a month).

[0063] In some examples, the risk component 126 can be configured to apply a noise level 132 (e.g., a time series of noise levels 132) as an input to one or more machine learning models 136, which can output one or more values indicating a probability or other heart disease risk level. Physiological data 138 can include values of physiological parameters determined based on signals sensed via electrodes 46 and parameter sensors 106, as discussed herein. In some examples, the risk component 126 can apply the physiological data as one or more additional inputs to the machine learning model 136. In some examples, the risk component 126 can apply techniques to determine heart disease risk based on the noise level 132 and in some cases based on other physiological data 138, which is similar to those described in U.S. Patent Application Publication No. 2012 / 0253207, titled "HEART FAILURE MONITORING," which is commonly assigned and incorporated herein by reference in its entirety. The techniques described in U.S. Patent Application Publication No. 2012 / 0253207 include applying evidence levels determined from various types of patient parameter data to a Bayesian belief network or other probability model, which is an example of the machine learning model 136.

[0064] In some examples, the noise level 132 includes a plurality of noise levels experienced by patient 4 over a period of time, and the risk component 126 can determine a noise profile based on these noise levels. The risk component 126 can apply the profile to the machine learning model 136, apply the features of the profile to the noise exposure criterion 134, or otherwise determine heart disease risk based on the noise profile. An alert or other message regarding heart disease risk output to the computing device 12 can include the noise profile, such that the user can also examine and evaluate the noise profile. The calculations by which the risk component 126 determines heart disease risk based on the noise level (e.g., using the criteria or machine learning models described above) can not be performed by human thought and can provide an advantage in monitoring the cardiovascular health of patient 4 in the capabilities system 2.

[0065] In some examples, the health monitoring application 122 is configured to determine that a patient is sleeping based on physiological parameters (such as ECG signals, EEG signals, respiratory signals, blood oxygenation signals, and blood pressure signals) sensed via electrodes 46 and / or parameter sensors 106. The health monitoring application 122 can also be configured to determine one or more sleep metrics, for example, indicating the depth or quality of sleep, while determining that the patient is sleeping based on these physiological parameters. The health monitoring application 122 can store the sleep metrics as physiological data 138 in the memory 102.

[0066] The risk component 126 can, for example, correlate one or more sleep metrics with one or more noise levels 132 experienced by the patient 4 over time, and determine the patient's heart disease risk at least in part based on the correlation between the one or more sleep metrics and the one or more noise levels. A noise level 132 sufficient to disrupt sleep may also be more likely to cause or exacerbate heart disease. To determine the heart disease risk, the risk component 126 can be configured to apply a characteristic or metric of the correlation to one or more criteria 134, or apply a time-correlated signal as an input to one or more machine learning models 136.

[0067] The impact of noise on the health of the patient 4 can be different between daytime and nighttime. For example, an equal magnitude of noise level can have different physiological effects at night (e.g., due to the startle response) than during the day. To this end, the noise component 124 can group the noise levels 132 into daytime (e.g., noon to 4 pm) or nighttime (e.g., midnight to 4 am) noise level groups, and calculate various statistics or other representations of the noise levels 132 for daytime and nighttime. The boundaries between daytime and nighttime can be configured by the patient 4, a clinician, or other user via the computing devices 12, 14. Additionally, the risk component 126 can determine the heart disease risk based on the noise level 132 in different ways depending on whether the noise level 132 is a daytime or nighttime noise level. For example, the risk component 126 can apply the noise level 132 to different thresholds (e.g., a daytime threshold and a nighttime threshold) or other criteria 134, or different machine learning models 136, based on whether the noise level 132 is a daytime noise level or a nighttime noise level.

[0068] Figure 5 is a block diagram showing an example configuration of a computing device 12 operating in accordance with one or more techniques of the present disclosure, which computing device can correspond to Figure 1 either of the computing devices 12A and 12B (or two computing devices operating in coordination). In some examples, the computing device 12 takes the form of a smart phone, a laptop computer, a tablet computer, a personal digital assistant (PDA), a smart watch or other wearable computing device, or a smart speaker, a smart home hub, or any IoT device.

[0069] As Figure 5 shown in the example of Figure 5 , the computing device 12 can be logically partitioned into a user space 142, a kernel space 144, and hardware 146. The hardware 146 can include one or more hardware components that provide an operating environment for components executing in the user space 142 and the kernel space 144. The user space 142 and the kernel space 144 can represent different segments or sections of memory, where the kernel space 144 provides higher privileges to processes and threads than the user space 142. For example, the kernel space 144 can include an operating system 188 that operates with higher privileges than components executing in the user space 142.

[0070] As Figure 5 shown, the hardware 146 includes a processing circuit 190, a memory 192, one or more input devices 194, one or more output devices 196, one or more sensors 198, and a communication circuit 199. Although shown as separate devices for purposes of illustration in Figure 5 , the computing device 12 can be any component or system that includes a processing circuit or other suitable computing environment for executing software instructions, and need not include, for example, Figure 5 the one or more elements shown in Figure 5 .

[0071] The processing circuit 190 is configured to implement functions and / or process instructions for execution within the computing device 12. For example, the processing circuit 190 can be configured to receive and process instructions stored in the memory 192 that provide functionality to components included in the kernel space 144 and the user space 142 to perform one or more operations in accordance with the techniques of the present disclosure. Examples of the processing circuit 190 can include any one or more microprocessors, controllers, GPUs, TPUs, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuits.

[0072] Memory 192 may be configured to store information within computing device 12 for processing during operation of computing device 12. In some examples, memory 192 is described as a computer-readable storage medium. In some examples, memory 192 includes temporary or volatile memory. Examples of volatile memory include random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), and other forms of volatile memory known in the art. In some examples, memory 192 also includes one or more memories configured for long-term storage of information, such as including non-volatile storage elements. Examples of such non-volatile storage elements include magnetic hard disks, optical disks, floppy disks, flash memory, or various forms of electrically programmable read-only memory (EPROM) or electrically erasable and programmable (EEPROM) memory. In some examples, memory 132 includes a storage device associated with the cloud.

[0073] One or more input devices 194 of computing device 12 may receive input, such as from patient 4 or another user. Examples of input are tactile, audio, kinetic, and optical inputs. As an example, input device 194 may include a mouse, keyboard, voice response system, camera, button, control panel, microphone, presence-sensitive or touch-sensitive component (e.g., a screen), or any other device for detecting input from a user or machine. In some examples, a microphone of input device 194 may act as a sound sensor to sense ambient noise simultaneously with sound sensor 108 of IMD 10 to calibrate, as described above, IMD 10's determination of noise level 132.

[0074] One or more output devices 196 of computing device 12 may generate output, such as to patient 4 or another user. Examples of output are tactile output, haptic output, audio output, and visual output. Output devices 196 of computing device 12 may include a presence-sensitive screen, sound card, video graphics adapter, speakers, cathode ray tube (CRT) monitor, liquid crystal display (LCD), light emitting diode (LED), or any type of device for generating tactile, audio, and / or visual output. In some examples, speakers of output device 196 may generate a predetermined noise for calibrating, as described above, IMD 10's determination of noise level 132.

[0075] One or more sensors 198 of computing device 12 may sense physiological parameters or signals of patient 4. Sensors 198 may include electrodes, axis accelerometers (e.g., 3-axis accelerometers), optical sensors, impedance sensors, temperature sensors, pressure sensors, sound sensors (e.g., microphones), and other sensors, as well as sensing circuitry (e.g., including an ADC), similar to those described above with respect to IMD 10 and Figure 4Those described

[0076] The communication circuit 199 of the computing device 12 can communicate with other devices by sending and receiving data. The communication circuit 199 can include a network interface card, such as an Ethernet card, an optical transceiver, a radio frequency transceiver, or any other type of device that can send and receive information. For example, the communication circuit 199 can include a radio transceiver that is configured to communicate according to standards or protocols such as 3G, 4G, 5G, WiFi (e.g., 802.11 or 802.15 ZigBee), or low power (BLE), etc.

[0077] As Figure 5 shown, the health monitoring application 150 executes in the user space 142 of the computing device 12. The health monitoring application 150 can be logically divided into a presentation layer 152, an application layer 154, and a data layer 156. The presentation layer 152 can include a user interface (UI) component 160 that generates and presents the user interface of the health monitoring application 150.

[0078] The application layer 154 can include, but is not limited to, a risk component 170, a location service 174, and a clock 176. The risk component 170 can determine the risk of heart disease based on the noise level 180 received from the IMD 10 via the communication circuit 199. For example, the risk component 170 can determine the risk of heart disease based on a comparison of the noise level 180 with one or more criteria 182 and / or by applying one or more noise levels 180 to one or more machine learning models 184 in the manner described above with respect to the IMD 10 and Figure 4 described. In some examples, in addition to the noise level 180, the risk component 170 can apply the physiological data 186 of patient 4 to the criteria 182 and / or the model 184 to determine the risk of heart disease in the manner described above with respect to the IMD 10 and Figure 4 described.

[0079] Physiological data 186 may include data collected by the IMD 10 as described above and received via the communication circuitry 199. In some examples, physiological data 186 may include data collected by the processing circuitry 190 via the input device 194 and the sensors 198. As an example, sensed data from the computing device 12 may include one or more of the following: activity level, walking / running distance, resting energy, active energy, exercise minutes, quantification of standing, physique, body mass index, heart rate, low heart rate events, high heart rate events, and / or irregular heart rate events, heart rate variability, walking heart rate, heart beat series, digitized ECG, blood oxygen saturation, blood pressure (systolic and / or diastolic), respiratory rate, maximal oxygen volume, blood glucose, peripheral perfusion, and sleep patterns. In some examples, patient data may include responses to inquiries about the condition of patient 4 posed to the health monitoring application 150 via the output device 196 and input by patient 4 via the input device 194.

[0080] The location service 174 may determine the location of the computing device 12 and thereby determine the inferred location of patient 4. The location service 178 may use GPS data, multilateration, and / or any other known techniques for locating the computing device. The clock 176 may generate data indicative of the time of day associated with the location of patient 4. Additionally, based on the timestamp data from the IMD 10, the processing circuitry 190 may correlate the noise level 180 received from the IMD 10 with the location and the time of day. In conjunction with the heart disease risk information, the output device 196 may present noise levels 180 determined to be excessive or contributing to the risk of heart disease and their associated locations and times of day. Based on this information, patient 4 or another interested user may identify the causes of the noise exposure.

[0081] Figure 6 is a block diagram showing a logical view of a health monitoring service (HMS) 26 operated by a computing system 20 ( Figure 1 )). Exemplary embodiments generally involve various hardware / software components that operate on the processing circuitry 22 of the computing system 20 ( Figure 1 ) and are generally configured to be network accessible for sending / receiving various data to / from patient medical devices (e.g., IMD 10), its local device (e.g., computing device 12), computing devices 14 of other users, etc.

[0082] Figure 6 Provides an operational perspective of the HMS 26 when hosted as a cloud-based platform. In Figure 6 example, the components of the HMS 26 are arranged according to multiple logical layers implementing the techniques of the present disclosure. Each layer may be implemented by one or more modules including hardware, software, or a combination of hardware and software.

[0083] Computing devices such as Figure 1 computing devices 12 and 14 can operate as clients that communicate with HMS 26 via interface layer 202. Computing devices typically execute client software applications, such as desktop applications, mobile applications, and web applications, e.g., health monitoring application 150. Interface layer 202 represents a set of application programming interfaces (APIs) or protocol interfaces provided and supported by HMS 26 for client software applications. Interface layer 202 can be implemented with one or more web servers.

[0084] As Figure 6 shown, HMS 26 also includes application layer 204, which represents a collection of services 210 for implementing the functionality attributed to HMS 26 herein. Application layer 204 receives information from client applications, e.g., the noise level determined by IMD 10 and, in some cases, the associated location and time of patient 4 determined by computing device 12, and stores them as noise level 240. Application layer 204 can similarly receive physiological data 246 from IMD 10 and computing device 12. Application layer 204 processes the information in response to the information according to one or more of services 210. Application layer 204 can be implemented as one or more discrete software services 210 executing on one or more application servers (e.g., physical or virtual machines). That is, the application server provides a runtime environment for executing services 210. In some examples, the functionality of interface layer 202 and the functionality of application layer 204 as described above can be implemented at the same server. Services 210 can communicate via logical service bus 212. Service bus 212 generally represents a logical interconnection or a set of interfaces that allows different services 210 to send messages to other services, such as through a publish / subscribe communication model.

[0085] The data layer 206 of HMS 26 provides persistence for the information in HMS 26 using one or more data repositories 220. Data repository 220 can generally be any data structure or software that stores and / or manages data. By way of example only, examples of data repository 220 include, but are not limited to, relational databases, multidimensional databases, maps, and hash tables.

[0086] As Figure 6As shown, each of services 230 to 234 is implemented in modular form within HMS 26. Although shown as separate modules for each service, in some examples, the functionality of two or more services may be combined into a single module or component. Each of services 230 to 234 may be implemented in software, hardware, or a combination of hardware and software. Additionally, services 230 to 234 may be implemented as stand-alone devices, separate virtual machines or containers, processes, threads, or software instructions generally adapted to execute on one or more physical processors.

[0087] The heart disease risk analysis service 230 may perform any of the techniques described herein for determining a heart disease risk based on applying a noise level 240 (and in some cases, physiological data 246) to one or more criteria 242 and / or a machine learning model 244. The machine learning model configuration service 232 may use training data 248 to train, validate, and otherwise configure the machine learning model 244. The training data 248 may include multiple sets of noise levels (and in some cases, time-corresponding physiological data) from different subjects that have been labeled with a heart disease risk level. Exemplary machine learning techniques that may be used to generate one or more models 244 may include various learning styles, such as supervised learning, unsupervised learning, and semi-supervised learning. Exemplary types of algorithms include Bayesian algorithms, clustering algorithms, decision tree algorithms, regularization algorithms, regression algorithms, example-based algorithms, artificial neural network algorithms, deep learning algorithms, dimensionality reduction algorithms, and the like. Various examples of specific algorithms include Bayesian linear regression, boosted decision tree regression, and neural network regression, backpropagation neural networks, convolutional neural networks (CNNs), long short-term networks (LSTMs), apriori algorithms, K-means clustering, K-nearest neighbors (kNN), learning vector quantization (LVQ), self-organizing maps (SOM), locally weighted learning (LWL), ridge regression, least absolute shrinkage and selection operator (LASSO), elastic net, and least angle regression (LARS), principal component analysis (PCA), and principal component regression (PCR).

[0088] Figure 7 is a flowchart illustrating exemplary operations for generating an output of a patient's heart disease risk caused by environmental noise in the patient's environment in accordance with one or more techniques of the present disclosure. Figure 7 The exemplary operations are described as being performed by a processing circuit, which may include one or more of the processing circuit 100 of the IMD 10, the processing circuit 190 of the computing device 12, and / or the processing circuit 22 of the computing system 20.

[0089] According to Figure 7In an example, the processing circuit determines one or more noise levels (300) based on sound signals generated by one or more sound sensors of the IMD 10. The noise level can be a magnitude (e.g., in decibels), a representative value determined from such magnitudes over a time period, a representation of the time during which the magnitude exceeds one or more thresholds during a time period, or any other noise level value described herein. The processing circuit determines a heart disease risk based on the noise level (302). As described herein, the risk can be a numerical or qualitative value of a risk magnitude, or the probability of the development or worsening of heart disease or a related condition such as heart failure, arrhythmia, or hypertension. The processing circuit can generate an output of the heart disease risk, such as an alert or other report (304). The output corresponding to the heart disease risk can be communicated via the computing device 12 of patient 4 or the computing device 14 of another user.

[0090] In some examples, the processing circuit can create a daily time graph of noise above a decibel threshold, where the decibel threshold corresponds to a significant noise environment. One or more computing devices are configured to display graphs and / or other information related to noise exposure and heart disease risk. In some examples, additional or alternative graphs can include representative values of the noise level for periods plotted on a longer time scale.

[0091] Figure 8 is a flowchart illustrating exemplary operations for generating a noise profile for a patient over a period of time in accordance with one or more techniques described herein. Figure 8 The exemplary operations are described as being performed by a processing circuit, which can include one or more of the processing circuit 100 of the IMD 10, the processing circuit 190 of the computing device 12, and / or the processing circuit 22 of the computing system 20.

[0092] According to Figure 8 In an example, the processing circuit determines one or more noise levels (400) within a time period (such as one or more hours or a day) based on sound signals generated by one or more sound sensors of the IMD 10. The processing circuit creates a noise profile based on the noise level (402) and determines the heart disease risk of the patient based on the profile (404). The profile can characterize the amount of noise exposure within the time period based on the time series of the noise level during the time period. The processing circuit can apply the profile to a machine learning model as a time series of noise levels, or apply features of the profile to noise exposure criteria to determine the heart disease risk based on the noise profile. The processing circuit can generate an output corresponding to the heart disease risk (304), for example, via communication with the computing device 12 of patient 4 or the computing device 14 of another user. In some cases, the output (e.g., a warning or report) can include a representation of the profile (e.g., a graph).

[0093] In one or more examples, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on a computer-readable medium as one or more instructions or code and executed by a hardware-based processing unit. The computer-readable medium may include a non-transitory computer-readable medium corresponding to a tangible medium such as a data storage medium (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0094] The instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general microprocessors, application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Thus, as used herein, the terms “processor” or “processing circuit” may refer to any of the foregoing structures or any other physical structure suitable for implementing the described techniques. Additionally, the techniques may be implemented entirely in one or more circuits or logic elements.

[0095] Various embodiments have been described. These and other examples are within the scope of the appended claims.

[0096] The following embodiments are a non-limiting list of clauses according to one or more techniques of the present disclosure.

[0097] Embodiment 1. A medical device system, the medical device system comprising: a medical device including one or more sound sensors configured to generate a sound signal including noise experienced by a patient; and a processing circuit configured to: determine one or more noise levels experienced by the patient based on the sound signal; determine a heart disease risk for the patient at least in part based on the one or more noise levels; and generate an output corresponding to the heart disease risk to a computing device of the patient or another user.

[0098] Embodiment 2. The medical device system according to Embodiment 1, wherein to determine the one or more noise levels, the processing circuit is configured to determine a plurality of noise levels experienced by the patient over a period of time, and wherein the processing circuit is further configured to: create a noise profile for the patient based on the plurality of noise levels determined during the period, wherein the noise profile includes at least the determined plurality of noise levels and a corresponding timestamp for each of the plurality of noise levels; determine the heart disease risk for the patient at least in part based on the noise profile.

[0099] Example 3. The medical device system according to Example 2, wherein the processing circuit is further configured to output the noise profile for the patient for display on the computing device.

[0100] Example 4. The medical device system according to any one or more of Examples 1 to 3, wherein the one or more noise levels are determined during one or more frequently occurring pre-determined time periods.

[0101] Example 5. The medical device system according to Example 4, wherein the one or more frequently occurring pre-determined time periods include one or both of a night-time period and a day-time period.

[0102] Example 6. The medical device system according to Example 5, wherein, in order to determine the heart disease risk, the processing circuit is configured to: compare the one or more noise levels during the night-time period with a first one or more criteria; and compare the one or more noise levels during the day-time period with a second one or more criteria different from the first one or more criteria.

[0103] Example 7. The medical device system according to any one or more of Examples 1 to 6, wherein the processing circuit is further configured to: determine that the activity level of the patient is below a threshold; and determine the one or more noise levels experienced by the patient based on the sound signal generated when the activity level of the patient is below the threshold.

[0104] Example 8. The medical device system according to Example 7, wherein the medical device includes an accelerometer configured to generate a motion signal of the patient, and wherein the processing circuit determines the activity level based on the motion signal.

[0105] Example 9. The medical device system according to any one or more of Examples 1 to 8, wherein the processing circuit is further configured to identify one or more noise conditions that cause the one or more noise levels experienced by the patient.

[0106] Example 10. The medical device system according to Example 9, wherein, in order to identify the one or more noise conditions, the processing circuit is further configured to determine the location of the patient associated with the noise level via global positioning system data.

[0107] Example 11. The medical device system according to Example 9 or 10, the medical device system further includes a clock, wherein, in order to identify the one or more noise conditions, the processing circuit is further configured to determine the time of day associated with the noise level via the clock.

[0108] Example 12. The medical device system according to any one or more of Examples 1 to 11, wherein the medical device includes one or more additional sensors configured to generate one or more additional signals indicative of one or more physiological parameters of the patient, and wherein the processing circuit is further configured to: determine that the patient is asleep based on the one or more physiological signals; measure one or more sleep metrics when it is determined that the patient is asleep based on the one or more physiological parameters; associate the one or more sleep metrics with one or more noise levels experienced by the patient; and determine the heart disease risk for the patient at least in part based on the correlation between the one or more sleep metrics and the one or more noise levels.

[0109] Example 13. The medical device system according to any one or more of Examples 1 to 12, wherein the computing device is configured to emit one or more sounds having a known noise level, and wherein the processing circuit is configured to calibrate the one or more sound sensors of the medical device based on the one or more sounds emitted from the computing device.

[0110] Example 14. The medical device system according to any one or more of Examples 1 to 13, wherein the one or more sound sensors are a first set of one or more sound sensors, the sound signal includes a first sound signal, and the one or more noise levels include a first one or more noise levels, and wherein the medical device system further includes the computing device, the computing device including a second set of one or more sound sensors configured to generate a second sound signal, wherein the processing circuit is configured to: determine a second one or more noise levels based on the second sound signal; and calibrate the first set of one or more sound sensors based on the second one or more noise levels.

[0111] Example 15. The medical device system according to any one or more of Examples 1 to 14, wherein the one or more sound sensors are configured to continuously generate the sound signal.

[0112] Example 16. The medical device system according to Example 15, wherein the processing circuit is configured to continuously determine the noise level based on the sound signal.

[0113] Example 17. The medical device system according to Example 15, wherein the processing circuit is configured to determine the noise level at a predetermined interval.

[0114] Example 18. The medical device system according to any one or more of Examples 1 to 17, wherein, in order to determine the heart disease risk based at least in part on the one or more noise levels, the processing circuit is configured to determine whether the one or more noise levels meet one or more noise exposure criteria.

[0115] Example 19. The medical device system according to Example 18, wherein the one or more noise exposure criteria include a threshold noise magnitude.

[0116] Example 20. The medical device system according to Example 18 or 19, wherein the one or more noise exposure criteria include a threshold noise duration.

[0117] Example 21. The medical device system according to any one or more of Examples 1 to 20, wherein the heart disease risk includes one or more of a heart failure risk, a hypertension risk, or an arrhythmia risk.

[0118] Example 22. The medical device system according to any one or more of Examples 1 to 21, wherein, in order to determine the heart disease risk, the processing circuit is configured to apply the one or more noise levels to a machine learning model.

[0119] Example 23. The medical device system according to Example 22, wherein the medical device includes one or more additional sensors configured to generate one or more additional signals indicative of one or more physiological parameters of the patient, and wherein the processing circuit is further configured to determine one or more additional inputs for the machine learning model based on the one or more additional signals.

[0120] Example 24. The medical device system according to any one or more of Examples 1 to 23, wherein the medical device is an implantable medical device configured for subcutaneous implantation.

[0121] Example 25. The medical device system according to Example 24, wherein the implantable medical device includes: a housing having a length, a width, and a depth from a first end to a second end, wherein the length is greater than the width, and the width is greater than the depth, and wherein the one or more sound sensors are within the housing; a first electrode located at or near the first end of the housing; a second electrode located at or near the second end of the housing; and a circuit within the housing, the circuit being configured to sense an electrocardiogram via the first electrode and the second electrode.

[0122] Example 26. The medical device system according to any one or more of Examples 1 to 25, wherein the medical device system includes the processing circuit.

[0123] Example 27. The medical device system according to any one or more of Examples 1 to 25, wherein the processing circuit includes: the processing circuit of the medical device is configured to determine the one or more noise levels; and the processing circuit of at least one of the computing device or a cloud computing system communicatively coupled to the computing device via a network, the processing circuit being configured to determine the heart disease risk and generate the output.

[0124] Example 28. The medical device system according to any one or more of Examples 1 to 23, wherein the medical device includes an implantable cardiac monitor, the implantable cardiac monitor including: an airtight housing configured for subcutaneous implantation in the patient, wherein the housing has a length, width, and depth from a first end to a second end, wherein the length is greater than the width and the width is greater than the depth, wherein the length ranges from 5 millimeters (mm) to 60 mm, wherein the width ranges from 5 mm to 15 mm, and wherein the depth ranges from 5 mm to 15 mm; a processing circuit within the housing; a power source within the housing and operatively coupled to the processing circuit; a memory within the housing and operatively coupled to the processing circuit; a sensing circuit within the housing and operatively coupled to the processing circuit; a first electrode at or near the first end of the housing and operatively coupled to the sensing circuit; and a second electrode at or near the second end of the housing and operatively coupled to the sensing circuit.

[0125] Example 29. A method, the method comprising: determining, by a processing circuit of a medical device system including a medical device, one or more noise levels experienced by a patient based on a sound signal generated by a sound sensor of the medical device; determining, by the processing circuit, a heart disease risk for the patient at least in part based on the one or more noise levels; and generating, by the processing circuit, an output corresponding to the heart disease risk to a computing device of the patient or another user.

[0126] Example 30. The method according to Example 29, wherein determining the one or more noise levels includes: creating a noise profile for the patient based on the plurality of noise levels determined during the time period, wherein the noise profile at least includes the determined plurality of noise levels and a corresponding timestamp for each of the plurality of noise levels; and determining the patient's risk of heart disease at least in part based on the noise profile.

[0127] Example 31. The method according to Example 30, the method further comprising: outputting, by the processing circuit, the noise profile for the patient for display on the computing device.

[0128] Example 32. The method according to any one or more of Examples 29 to 31, wherein determining the one or more noise levels includes: determining the one or more noise levels during one or more predetermined time periods that occur frequently.

[0129] Example 33. The method according to Example 32, wherein the one or more predetermined time periods that occur frequently include one or both of a nighttime period and a daytime period.

[0130] Example 34. The method according to Example 33, wherein determining the risk of heart disease includes: comparing the one or more noise levels during the nighttime period with a first one or more criteria; and comparing the one or more noise levels during the daytime period with a second one or more criteria different from the first one or more criteria.

[0131] Example 35. The method according to any one or more of Examples 29 to 34, the method further comprising: determining, by the processing circuit, that the patient's activity level is below a threshold, wherein determining the one or more noise levels includes determining the one or more noise levels experienced by the patient based on the sound signal generated when the patient's activity level is below the threshold.

[0132] Example 36. The method according to Example 35, the method further comprising: determining, by the processing circuit, the activity level based on a motion signal generated by an accelerometer of the medical device.

[0133] Example 37. The method according to any one or more of Examples 29 to 36, the method further comprising: identifying, by the processing circuit, one or more noise conditions that cause the one or more noise levels experienced by the patient.

[0134] Example 38. The method according to Example 37, wherein identifying the one or more noise conditions includes: determining the location of the patient associated with the noise level via global positioning system data.

[0135] Example 39. The method according to Example 37 or 38, wherein identifying the one or more noise conditions includes: determining the time of day associated with the noise level via a clock.

[0136] Example 40. The method according to any one or more of Examples 29 to 39, wherein the medical device includes: one or more additional sensors configured to generate one or more additional signals indicative of one or more physiological parameters of the patient, and the method further includes: determining by the processing circuit that the patient is in sleep based on the one or more physiological parameters; measuring by the processing circuit one or more sleep metrics when it is determined based on the one or more physiological parameters that the patient is sleeping; associating by the processing circuit the one or more sleep metrics with the one or more noise levels experienced by the patient; and determining by the processing circuit the risk of heart disease for the patient at least in part based on the correlation between the one or more sleep metrics and the one or more noise levels.

[0137] Example 41. The method according to any one or more of Examples 29 to 40, wherein the computing device is configured to emit one or more sounds having a known noise level, and the method further includes calibrating the one or more sound sensors of the medical device by the processing circuit based on the one or more sounds emitted from the computing device.

[0138] Example 42. The method according to any one or more of Examples 29 to 41, wherein the one or more sound sensors are a first set of one or more sound sensors, the sound signal includes a first sound signal, and the one or more noise levels include a first one or more noise levels, the computing device includes a second set of one or more sound sensors configured to generate a second sound signal, and the method further includes: determining by the processing circuit a second one or more noise levels based on the second sound signal; calibrating by the processing circuit the first set of one or more sound sensors based on the second one or more noise levels.

[0139] Example 43. The method according to any one or more of Examples 29 to 42, wherein the one or more sound sensors are configured to continuously generate the sound signal.

[0140] Example 44. The method according to Example 43, wherein determining the one or more noise levels includes continuously determining the one or more noise levels based on the sound signal.

[0141] Example 45. The method according to Example 44, wherein determining the one or more noise levels includes: determining the one or more noise levels at predetermined intervals based on the sound signal.

[0142] Example 46. The method according to any one or more of Examples 29 to 45, wherein determining the heart disease risk based at least in part on the one or more noise levels includes: determining whether the one or more noise levels meet one or more noise exposure criteria.

[0143] Example 47. The method according to Example 46, wherein the one or more noise exposure criteria include a threshold noise magnitude.

[0144] Example 48. The method according to Example 46 or 47, wherein the one or more noise exposure criteria include a threshold noise duration.

[0145] Example 49. The method according to any one or more of Examples 29 to 48, wherein the heart disease risk includes one or more of a heart failure risk, a hypertension risk, or an arrhythmia risk.

[0146] Example 50. The method according to any one or more of Examples 28 to 48, wherein determining the heart disease risk includes: applying the one or more noise levels to a machine learning model.

[0147] Example 51. The method according to Example 50, wherein the medical device includes one or more additional sensors configured to generate one or more additional signals indicative of one or more physiological parameters of the patient, and the method further includes determining, by the processing circuit, one or more additional inputs for the machine learning model based on the one or more additional signals.

[0148] Example 52. The method according to any one or more of Examples 29 to 51, wherein the medical device is an implantable medical device configured for subcutaneous implantation.

[0149] Example 53. The method according to Example 52, wherein the implantable medical device comprises: a housing having a length, a width, and a depth from a first end to a second end, wherein the length is greater than the width, and the width is greater than the depth, and wherein the one or more acoustic sensors are within the housing; a first electrode located at or near the first end of the housing; a second electrode located at or near the second end of the housing; and a circuit within the housing, the circuit being configured to sense an electrocardiogram via the first electrode and the second electrode.

[0150] Example 54. The method according to any one or more of Examples 29 to 53, wherein the medical device system comprises the processing circuit.

[0151] Example 55. The method according to any one or more of Examples 29 to 54, wherein the processing circuit comprises: the processing circuit of the medical device configured to determine the one or more noise levels; and the processing of at least one of the computing device or a cloud computing system communicating with the computing device via a network, the processing being configured to determine the heart disease risk and generate the output.

[0152] Example 56. A non-transitory computer-readable storage medium storing instructions that, when executed, cause a processing circuit of a medical device system to perform the method according to any one of Examples 19 to 55.

Claims

1. A medical device system, the medical device system comprises: a medical device, the medical device including one or more sound sensors configured to generate a sound signal including noise experienced by a patient; and a processing circuit configured to: determine one or more noise levels experienced by the patient based on the sound signal; determine a heart disease risk for the patient at least in part based on the one or more noise levels; and generate an output corresponding to the heart disease risk to a computing device of the patient or another user.

2. The medical device system according to claim 1, wherein, in order to determine the one or more noise levels, the processing circuit is configured to determine a plurality of noise levels experienced by the patient over a period of time, and wherein the processing circuit is further configured to: create a noise profile for the patient based on the plurality of noise levels determined during the period, wherein the noise profile at least includes the determined plurality of noise levels and corresponding timestamps for each of the plurality of noise levels; and determine the heart disease risk for the patient at least in part based on the noise profile.

3. The medical device system according to claim 2, wherein the processing circuit is further configured to output the noise profile for the patient for display on the computing device.

4. The medical device system according to any one or more of claims 1 to 3, wherein the one or more noise levels are determined during one or more frequently occurring predetermined periods.

5. The medical device system according to claim 4, wherein the one or more frequently occurring predetermined periods include one or both of a night time period and a day time period.

6. The medical device system according to claim 5, wherein, in order to determine the heart disease risk, the processing circuit is configured to: compare the one or more noise levels during the night time period with a first one or more criteria; and compare the one or more noise levels during the day time period with a second one or more criteria different from the first one or more criteria.

7. The medical device system according to any one or more of claims 1 to 6, wherein the processing circuit is further configured to: determine that the activity level of the patient is below a threshold; and determine the one or more noise levels experienced by the patient based on the sound signal generated when the activity level of the patient is below the threshold.

8. The medical device system according to claim 7, wherein the medical device includes an accelerometer configured to generate a motion signal of the patient, and wherein the processing circuit determines the activity level based on the motion signal.

9. The medical device system according to any one or more of claims 1 to 8, wherein the processing circuit is further configured to identify one or more noise conditions that cause the one or more noise levels experienced by the patient.

10. The medical device system according to claim 9, wherein, in order to identify the one or more noise conditions, the processing circuit is further configured to determine the location of the patient associated with the noise level via global positioning system data.

11. The medical device system according to claim 9 or 10, the medical device system further comprising a clock, wherein, in order to identify the one or more noise conditions, the processing circuit is further configured to determine the time of day associated with the noise level via the clock.

12. The medical device system according to any one or more of claims 1 to 11, wherein the medical device includes one or more additional sensors configured to generate one or more additional signals indicative of one or more physiological parameters of the patient, and wherein the processing circuit is further configured to: Determine that the patient is asleep based on the one or more physiological parameters; Measure one or more sleep metrics when it is determined based on the one or more physiological parameters that the patient is sleeping; Correlate the one or more sleep metrics with the one or more noise levels experienced by the patient; And Determine the heart disease risk for the patient at least in part based on the correlation between the one or more sleep metrics and the one or more noise levels.

13. The medical device system according to any one or more of claims 1 to 12, wherein the computing device is configured to emit one or more sounds having a known noise level, and wherein the processing circuit is configured to calibrate the one or more sound sensors of the medical device based on the one or more sounds emitted from the computing device.

14. The medical device system according to any one or more of claims 1 to 13, wherein the one or more sound sensors are a first set of one or more sound sensors, the sound signal includes a first sound signal, and the one or more noise levels include a first one or more noise levels, and wherein the medical device system further includes the computing device, the computing device including a second set of one or more sound sensors configured to generate a second sound signal, wherein the processing circuit is configured to: Determine a second one or more noise levels based on the second sound signal; and Calibrate the first set of one or more sound sensors based on the second one or more noise levels.

15. A method, the method Comprising: Determining, by a processing circuit of a medical device system including a medical device, one or more noise levels experienced by a patient based on a sound signal generated by a sound sensor of the medical device; Determining, by the processing circuit, a heart disease risk for the patient at least in part based on the one or more noise levels; And Generating, by the processing circuit, an output corresponding to the heart disease risk to a computing device of the patient or another user.

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