Operation of a medical device system to determine value of blood pressure via an optical sensor

Abstract

An example system includes an implantable medical device includes a heart sound sensor configured to sense heart sound signals; and an optical sensor configured to sense optical signals; and processing circuitry configured to: determine blood pressure values of a patient based on the optical signals; determine heart sound data using the heart sound signals; determine whether the heart sound data satisfies a heart sound blood pressure sampling threshold; in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold, adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values; and control determination of a blood pressure value of the patient via the optical sensor based on the adjusted rate of sensing optical signals.

Description

OPERATION OF A MEDICAL DEVICE SYSTEM TO DETERMINE VALUE OF BLOOD PRESSURE VIA AN OPTICAL SENSOR

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 675,762, filed July 26, 2024, the entire content of which is incorporated herein by reference.FIELD

[0002] The disclosure relates generally to medical device systems and, more particularly, medical device systems configured to determine a value of blood pressure of a patient via an optical sensor.BACKGROUND

[0003] Medical devices may be used to monitor physiological signals of a patient. For example, some medical devices are configured to sense electrocardiogram (ECG) signals indicative of the electrical activity of the heart via electrodes. Some medical devices are additionally or alternatively configured to sense other signals, such as heart sound signals indicative of the mechanical activity of the heart via a motion or vibration sensor, such as an accelerometer or microphone. Some medical devices may be configured to deliver a therapy in conjunction with or separate from the monitoring of physiological signals.SUMMARY

[0004] In general, this disclosure is directed to techniques for adjusting a rate of sensing optical via the optical sensor for determination of blood pressure values in response to a determination that heart sound data satisfies a heart sound blood pressure sampling threshold, adjusting a rate of sensing optical signals via the optical sensor in response to receiving abnormal heart sound data, and / or controlling determining of a blood pressure value of a patient using an optical sensor based on the adjusted rate of sensing optical signals. Determining blood pressure values using optical sensors may have inaccuracies in particular situations. Clinicians may also be interested in blood pressure measurements timed to particular events in patient health.

[0005] Processing circuitry may implement the techniques of this disclosure to determine blood pressure values of a patient based on sensed optical signals, determine heart sound data using the heart sound signals, and determine whether the heart sound data satisfies a heart sound blood pressure sampling threshold. In response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold, processing circuitry may adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values, and control determination of a blood pressure value of the patient via the optical sensor based on the adjusted rate of sensing optical signals. In some examples, a heart sound blood pressure sampling threshold may indicate particular conditions, such as those desired by a clinician, to determine blood pressure at an adjusted rate, such as an increased rate. In some examples, the particular conditions to determine blood pressure values via an optical sensor may include when the heart sound data indicates the patient has heart failure or a change in degree or status of heart failure. By adjusting a rate of sensing blood optical signal for determination of blood pressure values in response to particular conditions of heart sound data being satisfied, an implantable medical device (IMD) may save battery capacity and sense at higher rates during more desirable times, such as when the heart sound data indicates heart failure or a change in degree or status of heart failure.

[0006] In one example technique of this disclosure, processing circuitry may select a particular machine learning model, such as an abnormal heart sound data machine learning model that may be trained to determine blood pressure values during instance when there is an abnormal heart beat, to determine blood pressure values using the optical signals in response to a determination that heart sound data satisfies an abnormal heart sound threshold. In some examples, the abnormal heart beat may indicate a patient has heart failure. In some examples, processing circuitry may apply the optical signals to the abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals. By selecting a particular machine learning model that may be trained to determine blood pressure values during instances when there is an abnormal heart beat, such as during heart failure, to determine blood pressure values in response to the determination that the heart sound data satisfies an abnormal heart sound threshold, processing circuitry may be able to determine blood pressure values with greater specificity and sensitivity.

[0007] Additionally, as the techniques of this disclosure may be completely or partially implemented in an IMD that can continuously and / or periodically sense optical signal and / or heart sound data without human intervention while subcutaneously implanted in a patient over weeks, to determine blood pressure values. Using techniques of this disclosure in an IMD may be advantageous when a physician cannot be continuously present with the patient 24 hours of a day and / or be continuously present over weeks or months to evaluate optical signals and / or heart sound signals where performing millions of operations on weeks or months of optical signals and / or heart sound signals could not practically be performed in the mind of a physician with techniques of this disclosure. For example, heart sounds may differ during the day versus at night, and may differ in ambulatory settings versus at rest.

[0008] In some examples, using techniques as described in this disclosure may result in higher specificity and sensitivity of determining blood pressure values based on optical signals. This higher specificity and sensitivity may increase reliability of another device, user, and / or clinician on the accuracy of determining blood pressure values based on optical signals. In some examples, this improved reliability on the accuracy of determining blood pressure values based on optical signals may result in improved usefulness of the IMD, system or computing device as a clinician, user, or other computing device may not use and / or rely upon determinations that are not at or above a specificity and sensitivity threshold. With more accurate blood pressure value determinations provided with techniques of this disclosure, physicians and caregivers may also provide better-tailored care, therapies, and interventions for the patient experiencing health events, such as in an ambulatory setting.

[0009] In one example, this disclosure describes a system comprising: an implantable medical device comprising a heart sound sensor configured to sense heart sound signals; and an optical sensor configured to sense optical signals; and processing circuitry configured to: determine blood pressure values of a patient based on the optical signals; determine heart sound data using the heart sound signals; determine whether the heart sound data satisfies a heart sound blood pressure sampling threshold; in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold, adjust a rate of sensing optical signals via the optical sensor for determination ofblood pressure values; and control determination of a blood pressure value of the patient via the optical sensor based on the adjusted rate of sensing optical signals.

[0010] In another example, this disclosure describes a system comprising: an implantable medical device comprising an optical sensor configured to sense optical signals; and processing circuitry configured to determine blood pressure values of a patient based on the optical signals; based on receiving abnormal heart sound data, adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values; and control determination of a blood pressure value of the patient using the optical sensor based on the adjusted rate of sensing optical signals.

[0011] In another example, this disclosure describes a system comprising: an implantable medical device comprising a heart sound sensor configured to sense heart sound signals of a patient; and an optical sensor configured to sense optical signals of the patient; and processing circuitry configured to: determine heart sound data using the heart sound signals; determine the heart sound data satisfies an abnormal heart sound threshold; in response to the determination that the heart sound data satisfies an abnormal heart sound threshold, select an abnormal heart sound data machine learning model to determine blood pressure values using the optical signals; and apply the optical signals to the abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals.

[0012] In another example, this disclosure describes a method comprising: receiving sensed optical signals and sensed heart sound signals of a patient; determining blood pressure values of the patient based on the optical signals; determining heart sound data using the heart sound signals; determining whether the heart sound data satisfies a heart sound blood pressure sampling threshold; in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold, adjusting a rate of sensing optical signals via an optical sensor for determination of blood pressure values; and controlling determination of a blood pressure value of the patient via the optical sensor based on the adjusted rate of sensing optical signals.

[0013] In another example, this disclosure describes a method comprising: receiving sensed optical signals of a patient; determining blood pressure values of the patient based on the optical signals; in response to receiving abnormal heart sound data of the patient, adjusting a rate of sensing optical signals via an optical sensor for determination of bloodpressure values; and controlling determination of a blood pressure value of the patient using the optical sensor based on the adjusted rate of sensing optical signals.

[0014] In another example, this disclosure describes a method comprising: receiving sensed optical signals and sensed heart sound signals of a patient; determining heart sound data using the heart sound signals; determining the heart sound data satisfies an abnormal heart sound threshold; in response to the determination that the heart sound data satisfies an abnormal heart sound threshold, selecting an abnormal heart sound data machine learning model to determine blood pressure values using the optical signals; and applying the optical signals to the abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals.

[0015] The summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to provide an exclusive or exhaustive explanation of the systems, device, and methods described in detail within the accompanying drawings and description below. Further details of one or more examples of this disclosure are set forth in the accompanying drawings and in the description below. Other features, objects, and advantages will be apparent from the description and drawings, and from the claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0016] FIG. 1 illustrates the environment of an example medical system in conjunction with a patient.

[0017] FIG. 2 is a functional block diagram illustrating an example configuration of the implantable medical device (IMD) of the medical system of FIG. 1.

[0018] FIG. 3 is a conceptual side-view diagram illustrating an example configuration of the IMD of FIGS. 1 and 2.

[0019] FIG. 4A is a conceptual side-view diagram illustrating an example configuration of the IMD of FIGS. 1-3.

[0020] FIG. 4B is a conceptual side-view diagram illustrating an example configuration of the IMD of FIGS. 1-3.

[0021] FIG. 5 is a functional block diagram illustrating an example configuration of the external device of FIGS. 1-4.

[0022] FIG. 6 is a block diagram illustrating an example system that includes an access point, a network, external computing devices, such as a server, and one or more other computing devices, which may be coupled to the IMD and external device of FIGS. 1-5.

[0023] FIGS. 7A-7B are flow diagrams illustrating an example technique for operating a system to adjust a rate of sensing blood pressure via an optical sensor, in accordance with some examples of the current disclosure.

[0024] FIG. 8 is a flow diagram illustrating an example technique for operating a system to select a particular machine learning model to determine blood pressure values using optical signals, in accordance with some examples of the current disclosure.

[0025] FIG. 9 is a conceptual diagram illustrating an example training process for an artificial intelligence model, in accordance with examples of the current disclosure.

[0026] FIG. 10 is a conceptual diagram illustrating an example ML model configured to determine blood pressure values based on optical signals.

[0027] Like reference characters denote like elements throughout the description and figures.DETAILED DESCRIPTION

[0028] A variety of types of medical devices sense blood pressure of patient. An implantable medical device (IMD) may include an optical sensor to sense blood pressure. The optical sensors used by IMDs to sense blood pressure may be integrated with a housing of the IMD and / or coupled to the IMD via one or more elongated leads. Example IMDs that may be configured to monitor blood pressure include pacemakers and implantable cardioverter-defibrillators, which may be coupled to intravascular or extravascular leads, as well as pacemakers with housings configured for implantation within the heart, which may be leadless. An example of pacemaker configured for intracardiac implantation is the Micra™ Transcatheter Pacing System, available from Medtronic, Inc. Some IMDs that do not provide therapy, e.g., implantable patient monitors, may be configured to sense blood pressure. One example of such an IMD is the Reveal LINQ™ and LINQ II™ Insertable Cardiac Monitors (ICMs), available from Medtronic, Inc., which may be inserted subcutaneously. Such IMDs may facilitate relatively longer-term monitoring of patients during normal daily activities, and mayperiodically transmit collected data to a network service, such as the Medtronic Carelink™ Network.

[0029] Any medical device configured to sense blood pressure values via implanted optical sensors, including the examples identified herein, may implement the techniques of this disclosure for evaluating optical signals to determine a health condition status of a patient based on changes in blood pressure over time. For example, particular features may be extracted from the optical signals, the extracted features may be applied to a machine learning model to determine blood pressure values, which may help lead to accurate determinations of a health condition status, such as a degree of hypertension, with high specificity and sensitivity. The techniques of this disclosure for determining blood pressure values, which help lead to accurate determinations of a health condition status of a patient, such as a degree of hypertension, may facilitate determinations of a degree of hypertension, cardiac wellness, and risk of sudden cardiac death, and may lead to clinical interventions to suppress hypertension such as medications and ablations.

[0030] Any system or medical device configured to sense blood pressure via optical signals, including the examples identified herein, may implement the techniques of this disclosure for adjusting a rate of sensing optical signals via the optical sensor in response to a determination that heart sound data satisfies the heart sound blood pressure sampling threshold, adjusting a rate of sensing optical signals via the optical sensor for determination of blood pressure values in response to receiving abnormal heart sound data, and / or controlling determining of a blood pressure value of a patient using an optical sensor based on the adjusted rate of sensing optical signals. In some examples, any system or medical device configured to sense blood pressure via optical signals, including the examples identified herein, may implement the techniques of this disclosure for selecting an abnormal heart sound data machine learning model to determine blood pressure values using optical signals sensed via an optical sensor in response to the determination that the heart sound data satisfies an abnormal heart sound threshold, and applying the optical signals to the abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals. In some examples, a heart sound data satisfying an abnormal heart sound threshold may indicate the patient has heart failure, such as congestive heart failure. In some examples, a heart sound data satisfying an abnormal heart sound threshold may indicate the patient has regurgitation in the heart.

[0031] In some examples, an IMD may include an optical sensor configured to sense optical signals. In some examples, an IMD may additionally or alternatively include a heart sound sensor to sense heart sound signals. In some examples, a system or IMD may include processing circuitry configured to determine blood pressure values of a patient based on the optical signals. In some examples, processing circuitry may determine heart sound data using the heart sound signals. In some examples, processing circuitry may determine whether the heart sound data satisfies a heart sound blood pressure sampling threshold. In some examples, in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold, processing circuitry may adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values. In some examples, processing circuitry may control determination of a blood pressure value of the patient via the optical sensor based on the adjusted rate of sensing optical signals.

[0032] In some examples, a heart sound blood pressure sampling threshold may indicate desirable conditions to determine blood pressure at an adjusted rate, such as an increased rate. In some examples, some desirable conditions to determine blood pressure values via an optical sensor may include changes in cardiac function. In some examples, heart sound data may include amplitude, slew rate, frequency, envelope, heart sound time interval, and / or energy of a heart sound signal. In some examples, heart sound time interval may include a time interval from an R-wave (e.g., such as from an ECG signal) to an SI heart sound, which may be a surrogate for electromechanical activation time, or a time interval from SI heart sound to S2 heart sound, which may indicate ejection time. In some examples, increased electromechanical activation time and / or reduced ejection time may indicate reduced ventricular function and contractility. In some examples, processing circuitry may determine whether changes in heart sound data over a period of time indicate a change in cardiac function. Processing circuitry may determine to adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure in response to processing circuitry determining the heart sound data indicates a change in cardiac function (e.g., heart sound data satisfies a heart sound sampling threshold). In some examples, a change in cardiac function may indicate an occurrence of a health event, such as an acute health event. By adjusting a rate of sensing optical signals for determination of blood pressure values, as described herein, an IMD may save batterycapacity and sense at higher rates during more desirable times, such as when the heart sound data indicates heart failure and / or a change in cardiac function, such as a change in degree or status of heart failure.

[0033] In some examples, processing circuitry may select a particular machine learning model, such as abnormal heart sound data machine learning model that may be trained to determine blood pressure values during instance when there is an abnormal heart beat, such as a heart beat of a heart with heart failure, to determine blood pressure values using the optical signals in response to a determination that heart sound data satisfies an abnormal heart sound threshold. In some examples, processing circuitry may apply the optical signals to the abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals. In some examples, by selecting a particular machine learning model that may be trained to determine blood pressure values during instance when there is an abnormal heart beat to determine blood pressure values in response to the determination that the heart sound data satisfies an abnormal heart sound threshold, processing circuitry may be able to determine blood pressure values with greater specificity and sensitivity.

[0034] In some examples, when a patient has abnormal heart sounds, such as abnormal heart sounds that indicate an acute health event such as heart failure, the amplitude of an optical signal may change, such as due to reduced ability of the heart to pump and / or vasculature differences, the fiduciary points on an optical waveform may become more obscured or change in timing. In some examples, a machine learning model trained when a patient is not having abnormal heart sounds, such as abnormal heart sounds that indicate heart failure, may be ineffective in determining blood pressure values based on optical signals when a patient is having abnormal heart sounds. In some examples, an abnormal heart sound data machine learning model may be trained based on data from patients having abnormal heart sounds, such as abnormal heart sounds that indicate heart failure. In some examples, an abnormal heart sound data machine learning model may differ from a normal heart sound data machine learning model in at least one of coefficients in the respective model, underlying model structure, inputs to the respective model, timing of a window of the optical signals input to the respective model, number of beats needed to average, and / or a gain, averaging, or filtering of the optical signal that is input into the respective model.

[0035] FIG. 1 illustrates the environment of an example medical system 2 in conjunction with a patient 4, in accordance with one or more techniques of this disclosure. The example techniques may be used with an IMD 10, which may be in wireless communication with at least one of external device 12 and other devices not pictured in FIG. 1. In some examples, IMD 10 is implanted outside of a thoracic cavity of patient 4 (e.g., subcutaneously in the pectoral location illustrated in FIG. 1). IMD 10 may be positioned near the sternum near or just below the level of the heart of patient 4, e.g., at least partially within the cardiac silhouette. IMD 10 includes a plurality of electrodes (not shown in FIG. 1), and is configured to sense a cardiac data via the plurality of electrodes. In some examples, IMD 10 takes the form of the Reveal LINQ™ or LINQ II ICM™, or another ICM similar to, e.g., a version or modification of, the LINQ™ ICMs.

[0036] External device 12 may be a computing device with a display viewable by the user and an interface for providing input to external device 12 (i.e., a user input mechanism). In some examples, external device 12 may be a notebook computer, tablet computer, workstation, one or more servers, cellular phone, smartphone, personal digital assistant, or another computing device that may run an application that enables the computing device to interact with IMD 10. External device 12 is configured to communicate with IMD 10 and, optionally, another computing device (not illustrated in FIG. 1), via wireless communication. External device 12, for example, may communicate via near-field communication technologies (e.g., inductive coupling, NFC or other communication technologies operable at ranges less than 10-20 cm) and far-field communication technologies (e.g., RF telemetry according to the 802.11 or Bluetooth® specification sets, or other communication technologies operable at ranges greater than near-field communication technologies).

[0037] In some examples, external device 12 may be or additionally include wearable computing device. Awearable computing device may include electrodes and other sensors to sense physiological signals of patient 4, and may collect and store physiological data and detect episodes based on such signals. Wearable computing device may be incorporated into the apparel of patient 4, such as within clothing, shoes, eyeglasses, a watch or wristband, a hat, etc. In some examples, a wearable device may be a smartwatch or other accessory or peripheral for external device 12, for example when external device 12 is a smartphone or tablet.

[0038] External device 12 may be used to configure operational parameters for IMD 10. External device 12 may be used to retrieve data from IMD 10. The retrieved data may include values of physiological parameters measured by IMD 10, indications of blood pressure values, indications of heart sound data, indications of whether heart sound data indicates an abnormal heart beat, or other maladies detected by IMD 10, and physiological signals recorded by IMD 10. For example, external device 12 may retrieve information related to the optical signals sensed by an optical sensor and / or heart sound data sensed by a heart sound sensor from IMD 10, e.g., over a time period since the last retrieval of information by external device. In some examples, external device 12 may determine such information related to blood pressure values, whether the heart sound data is abnormal and / or the heart sound data indicates a change in cardiac function based on physiological param eters / signals and / or other data retrieved from IMD 10. External device 12 may also retrieve cardiac optical signal and / or blood pressure values recorded by IMD 10, e.g., according to a schedule, due to IMD 10 determining that the heart sound data satisfies a heart sound blood pressure sampling threshold, or in response to a request to record the segment from patient 4 or another user. As discussed in greater detail below with respect to FIG. 6, one or more remote computing devices may interact with IMD 10 in a manner similar to external device 12, e.g., to program IMD 10 and / or retrieve data from IMD 10, via a network.

[0039] Processing circuitry of medical system 2, e.g., of IMD 10, external device 12, and / or of one or more other computing devices, may be configured to perform the example techniques of this disclosure for adjusting a rate of sensing optical signals via the optical sensor for determination of blood pressure values in response to a determination that heart sound data satisfies the heart sound blood pressure sampling threshold, adjusting a rate of sensing optical signals via the optical sensor in response to receiving abnormal heart sound data, and / or controlling determining of a blood pressure value of a patient using an optical sensor based on the adjusted rate of sensing optical signals. In some examples, processing circuitry of medical system 2 may be configured to perform the example techniques of this disclosure to select an abnormal heart sound data machine learning model to determine blood pressure values using optical signals sensed via an optical sensor in response to the determination that the heart sound data satisfies an abnormal heart sound threshold, andapply the optical signals to the abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals.

[0040] In some examples, IMD 10 may include a heart sound sensor, such as an accelerometer or piezoelectric microphone, within the housing configured to sense heart sound signals, such as cardiac vibrations, of the patient. In some examples, the processing circuitry of medical system 2 may extract heart sound signals of the patient based on sensed cardiac vibrations. In some examples, IMD 10 may include an optical sensor within the housing configured to optical signals. In some examples, IMD 10 determines blood pressure values of the patient based on the optical signals. In some examples, IMD 10 may determine heart sound data based on the heart sound signals. In some examples, heart sound data may include one or more of amplitude, slew rate, frequency, envelope, heart sound time interval, or energy of a heart sound signal.

[0041] In some examples, the processing circuitry of medical system 2 may determine whether the heart sound data satisfies a heart sound blood pressure sampling threshold. In some examples, the processing circuitry of medical system 2 may determine whether one or more parameters of the heart sound data, e.g., amplitude, slew rate, frequency, envelope, heart sound time interval, or energy parameters, satisfy one or more corresponding thresholds. The thresholds may be absolute thresholds, or thresholds based on a change in the parameter over a period of time. For example, processing circuitry of medical system 2 may determine that an S3 amplitude of the heart sound signals increasing a particular amount over a period of time satisfies a heart sound blood pressure sampling threshold. In some examples, the processing circuitry of medical system 2 may determine the heart sound data is abnormal in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold. In some examples, processing circuitry of medical system 2 may determine that a heart sound time interval, such as from R-wave to SI heart sound, increasing a particular amount over a period of time satisfies a heart sound blood pressure sampling threshold (e.g., indicates an increased electromechanical activation time that indicates reduced contractility of the heart). In some examples, processing circuitry of medical system 2 may determine that changes of an S2 / S1 slew ratio of the heart sound signals over a particular period satisfies a heart sound blood pressure sampling threshold. In some examples, processing circuitry of medical system 2 may determine that an S4 amplitude of the heart sound signals increasing aparticular amount over a period of time satisfies a heart sound blood pressure sampling threshold. In some examples, processing circuitry of medical system 2 may determine high frequency murmurs are present based on the sensed heart sound signals, which may be a sign of a diseased valve due to turbulent blood flow, and that the high frequency murmurs satisfy a heart sound blood pressure sampling threshold.

[0042] In some examples, the processing circuitry of medical system 2 may determine the heart sound data indicates a change in cardiac function in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold. In some examples, the processing circuitry of medical system 2 may determine the heart sound data indicates heart failure (or a change in heart failure status) in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold. In some examples, a change in cardiac function may indicate an occurrence of a health event, such as an acute health event. In some examples, the processing circuitry of medical system 2 may determine that patient activity satisfies a patient activity threshold in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold. In some examples, the processing circuitry of medical system 2 may adjust a rate of sensing optical signals via an optical sensor for determination of blood pressure values in response to one or more of a determination that the heart sound data indicates heart failure (or a change in heart failure status), a determination that the heart sound data indicates a change in cardiac function, and / or a determination that patient activity satisfies a patient activity threshold. In some examples, the processing circuitry of medical system 2 may additionally or alternatively adjust a rate of sensing optical signals via an optical sensor for determination of blood pressure values based on one or more of ventricular rate satisfying a rate threshold, ventricular rate satisfying a regularity threshold, ambient light satisfying an ambient light threshold, and / or respiration satisfying a respiration threshold.

[0043] In some examples, the processing circuitry of medical system 2 may adjust a rate of sensing optical signals via an optical sensor for determination of blood pressure values in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold. In some examples, adjusting a rate of sensing optical signals via an optical sensor may include adjusting a duty cycle for sampling optical signals, where sample(s) of an optical signal may be used to determine one or more bloodpressure values or values related to blood pressure. In some examples, adjusting a rate of sensing optical signals may include adjusting a rate at which an optical sensor captures segments of an optical signal, such as segments having a pre-determined length. In some examples, processing circuitry of medical system 2 may determine particular windows to sense optical signals via an optical sensor for determination of blood pressure values based on the heart sounds data based on the heart sound data. For example, processing circuitry of medical system 2 may use one or more of SI, S2, or S3 of heart sound data to adjust / define windows to sense the optical signals. In some examples, processing circuitry of medical system 2 may use one or more of SI, S2, or S3 of heart sound data to align windows from several beats of the sensed optical signal to generate a time averaged optical signal for determination of blood pressure values.

[0044] In some examples, the processing circuitry of medical system 2 may adjust a rate of sensing optical signals via an optical sensor for determination of blood pressure values in response to receiving abnormal heart sound data. In some examples, the processing circuitry of medical system 2 may determine the heart sound data indicates heart failure (or a change in heart failure status) in response to receiving abnormal heart sound data. In some examples, processing circuitry of medical system 2 may increase a rate of sensing optical signals via an optical sensor in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold. In some examples, processing circuitry of medical system 2 may increase a rate of sensing optical signals via an optical sensor in response to receiving abnormal heart sound data. In some examples, the processing circuitry of medical system 2 may control determination of a blood pressure value of the patient via the optical sensor based on the adjusted rate of sensing optical signals. In some examples, the processing circuitry of medical system 2 may determine an acute health event occurred or a risk score that an acute health event may occur during a particular period of time based on blood pressure values determined via the adjusted rate of sensing optical signals and output an indication of the determination that an acute health event occurred or a risk score that an acute health event may occur during a particular period of time. In some examples, by adjusting a rate of sensing optical signals via an optical sensor, an IMD may sense blood pressure at increased rates during desirable periods of time and / or a decreased rate during less desirable periods of time which may save battery capacity while increasing clinical utility,such as sensing blood pressure (e.g., optical sensor is turned on) more frequently when the heart sound data indicates heart failure, a change in heart failure status, or a change in cardiac function. In some example, an increased or decreased sampling rate may refer to either a number or time of optical measurements per data or number of measurements per second within a cardiac cycle. For example, for an increased sampling rate, an optical sensor may adjust from sensing 2 times per day for 30 seconds each sample to 4 times per day for 30 seconds each sample, 2 times per day for 60 seconds each sample, 4 times per day for 60 seconds each sample, or any other combination of increased sampling frequency and time. In some examples, for an increased sampling rate, sampling frequency of the optical sensor may be adjusted, such as from 64Hz to 128Hz or 256Hz, while the optical sensor is sensing (e.g. turned on). In some examples, an increased sampling rate may increase optical sensor accuracy by detecting higher frequency attributes in the optical waveform.

[0045] In some examples, adjusting a sampling rate may be useful and improve accommodating different device implant locations since pressure waveforms undergo shape changes as they propagate away from the heart due to variations in blood vessel properties and the influence of reflected waves. In some examples, processing circuitry of medical system 2 may determine a progression of a cardiovascular disease based on heart sound data. In some examples, processing circuitry of medical system 2 may adjust a sampling rate of optical sensor based on the determination of a progression of a cardiovascular disease, such as setting maximum sampling rate. In some examples, processing circuitry of medical system 2 adjusting a sampling rate based on a progression of cardiovascular disease may improve preserving battery capacity since some high frequency optical waveform features such as the shape of a dicrotic notch tend to degrade with cardiovascular disease, such as processing circuitry of medical system 2 decreasing a sampling rate without compromising sensor performance. In some examples, the processing circuitry of medical system 2 may determine whether heart sound data satisfies an abnormal heart sound threshold. In some examples, the processing circuitry of medical system 2 may determine a heart has heart failure in response to the heart sound data satisfying an abnormal heart sound threshold. In some examples, the processing circuitry of medical system 2 may select a particular machine learning model, such as abnormal heart sound data machine learning model, to determine blood pressure values using theoptical signals in response to a determination that the heart sound data satisfies an abnormal heart sound threshold. In some examples, an abnormal heart sound data machine learning model may be trained to determine blood pressure values during instance when there is an abnormal heart beat. In some examples, processing circuitry of medical system 2 may apply the optical signals to the abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals. In some examples, machine learning models generally trained to determine blood pressure values based on optical signals may have increased false detections when a heart beat is an irregular heart beat. In some examples, by selecting a particular machine learning model that may be trained to determine blood pressure values during instance when there is an abnormal heart beat to determine blood pressure values in response to the determination that the heart sound data satisfies an abnormal heart sound threshold, processing circuitry may be able to determine blood pressure values with greater specificity and sensitivity.

[0046] Although described in the context of examples in which IMD 10 that senses the cardiac data comprises an insertable cardiac monitor, example systems including one or more implantable or external devices of any type configured to sense cardiac data may be configured to implement the techniques of this disclosure.

[0047] FIG. 2 is a functional block diagram illustrating an example configuration of IMD 10 of FIG. 1 in accordance with one or more techniques described herein. In the illustrated example, IMD 10 includes electrodes 16A and 16B (collectively “electrodes 16”), antenna 26, processing circuitry 50, sensing circuitry 52, communication circuitry 54, storage device 56, switching circuitry 58, sensors 62, heart sound sensor(s) 62A, and optical sensors 62B. Although the illustrated example includes two electrodes 16, IMDs including or coupled to more than two electrodes 16 may implement the techniques of this disclosure in some examples.

[0048] Processing circuitry 50 may include fixed function circuitry and / or programmable processing circuitry. Processing circuitry 50 may include any one or more of a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or analog logic circuitry. In some examples, processing circuitry 50 may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs, as well asother discrete or integrated logic circuitry. The functions attributed to processing circuitry 50 herein may be embodied as software, firmware, hardware or any combination thereof.

[0049] Sensing circuitry 52 may be selectively coupled to electrodes 16 via switching circuitry 58, e.g., to select the electrodes 16 and polarity, referred to as the sensing vector, used to sense an ECG signal, as controlled by processing circuitry 50. Sensing circuitry 52 may sense signals from electrodes 16, e.g., to produce an ECG signal, in order to facilitate monitoring the electrical activity of the heart. Sensing circuitry 52 may include components / modules for converting the raw ECG signal to a processed ECG signal that can be analyzed to detect sense events. Sensing circuitry 52 also may monitor signals from sensors 62, such as heart sound sensor(s) 62 A optical sensor(s) 62B. In some examples, heart sound sensor(s) 62A may be configured to sense cardiac vibrations. In some examples, heart sound sensor(s) 62A may include one or more accelerometers. In some examples, heart sound sensor(s) 62A may additionally or alternatively include one or more microphones and / or other vibration / motion sensors. In some examples, the optical sensor(s) 62B of IMD 10 is a photoplethysmography (PPG) sensor. One or more optical sensor(s) 62B may include one or more light detector(s) configured to receive and / or detect light signals, such as reflected light signals originating from light emitter(s). Light signals received and / or detected by optical sensor(s) 62B may be referred to as optical signals. In some examples, one or more of optical sensor(s) 62B may be configured as a PPG sensor, e.g., by being configured to receive light reflected by blood in one or more blood vessels. In some examples, an optical sensor 62B may be included in a same sensor package and / or may be implemented using the same transducer(s). IMD 10 may, in some cases, include one or more optical sensors 62B including two or more light emitters and one or more light detectors. Sensing circuitry 52 may receive raw cardiac vibrations monitored by heart sound sensors 62A. Sensing circuitry 52 may include components / modules for converting the raw cardiac vibrations to a processed heart sound beat signal that can be analyzed to detect sense events. In some examples, processing circuitry 50 may use signals received from sensors 62, including heart sound beat signals sensed by heart sound sensor(s) 62A and / or optical signals sensed by optical sensor(s) 62B, to determine / adjust a rate of sensing optical signals and / or determine blood pressure values. In some examples, sensing circuitry 52 may include one or more filters andamplifiers for filtering and amplifying signals received from electrodes 16 and / or sensors 62.

[0050] IMD 10, e.g., optical sensor(s) 62B, may produce a signal proportional to instantaneous blood pressure variations. In some examples, IMD 10 and / or optical sensor(s) 62B may be calibrated such that IMD 10 may be able to determine blood pressure values based on the signal. In some examples in which IMD 10 is not so calibrated, the optical signal may nevertheless be useful for tracking variations in the amplitude / morphology of blood pressure, such as systolic and / or diastolic blood pressure and other features as described herein, over a period of time. In some examples, this period of time may be one week, two weeks, one month, two months, and / or other periods of time. The days used in the period of time may be adjacent days (e.g., such as Monday through Friday) or may be non-adjacent days (e.g., such as every Monday).

[0051] Some examples of a system and / or IMD determining one or more blood pressure values based on the optical signals is described in U.S. Provisional Patent Application No. 63 / 498,903, incorporated herein by reference in its entirety. Some examples of a system and / or IMD determining heart sound data, such as extracting heart sound features in particular windows, using heart sound signals is described in U.S. Provisional Patent Application No. 63 / 593,125, incorporated herein by reference in its entirety.

[0052] Sensing circuitry 52 and / or processing circuitry 50 may be configured to detect cardiac depolarizations (e.g., P-waves of atrial depolarizations or R-waves of ventricular depolarizations) when the ECG signal amplitude crosses a sensing threshold. An ECG signal may include P-waves (depolarization of the atria), R-waves (depolarization of the ventricles), and T-waves (repolarization of the ventricles), among other events. Sensing circuitry 52 and / or processing circuitry 50 may be configured to detect one or more features of the P-waves, R-waves, and / or T-waves in an ECG signal. For cardiac depolarization detection, sensing circuitry 52 may include a rectifier, filter, amplifier, comparator, and / or analog-to-digital converter, in some examples. In some examples, sensing circuitry 52 may output an indication to processing circuitry 50 in response to sensing of a cardiac depolarization. In this manner, processing circuitry 50 may receive detected cardiac depolarization indicators corresponding to the occurrence of detected R-waves and P-waves in the respective chambers of heart. Processing circuitry 50 mayuse the indications of detected R-waves and P-waves for determining inter-depolarization intervals, heart rate, and detecting arrhythmias, such as tachyarrhythmias and asystole.

[0053] Sensing circuitry 52 may also provide one or more optical signals, heart sound signals, or digitized ECG signals to processing circuitry 50 for analysis, e.g., for use in analysis to determine blood pressure values of the patient based on the optical signals and / or determine heart sound data using the heart sound signals. Sensing circuitry 52 may include one or more detection channels, each of which may include an amplifier. The detection channels may be used to sense cardiac data, such as blood pressure values, heart sound data, or an ECG signal. Some detection channels may detect events, such as R- waves, P-waves, and T-waves and provide indications of the occurrences of such events to processing circuitry 50. One or more other detection channels may provide the signals to an analog-to-digital converter, for conversion into a digital signal for processing or analysis by processing circuitry 50.

[0054] Communication circuitry 54 may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as external device 12, another networked computing device, or another IMD or sensor. Under the control of processing circuitry 50, communication circuitry 54 may receive downlink telemetry from, as well as send uplink telemetry to external device 12 or another device with the aid of an internal or external antenna, e.g., antenna 26. In addition, processing circuitry 50 may communicate with a networked computing device via an external device (e.g., external device 12) and a computer network, such as the Medtronic CareLink™ Network. Antenna 26 and communication circuitry 54 may be configured to transmit and / or receive signals via inductive coupling, electromagnetic coupling, Near Field Communication (NFC), Radio Frequency (RF) communication, Bluetooth, WiFi, or other proprietary or non-proprietary wireless communication schemes.

[0055] In some examples, storage device 56 includes computer-readable instructions that, when executed by processing circuitry 50, cause IMD 10 and processing circuitry 50 to perform various functions attributed to IMD 10 and processing circuitry 50 herein. Storage device 56 may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital media. Storage device 56 may store, as examples, programmed valuesfor one or more operational parameters of IMD 10 and / or data collected by IMD 10 for transmission to another device using communication circuitry 54. Data stored by storage device 56 and transmitted by communication circuitry 54 to one or more other devices may include blood pressure detection quantifications / indications, digitized optical signals, and / or digitized heart sound signals, as examples.

[0056] FIG. 3 is a conceptual side-view diagram illustrating an example configuration of IMD 10 of FIGS. 1 and 2. In the example shown in FIG. 3, IMD 10 may include a leadless, subcutaneously-implantable monitoring device having a housing 15 and an insulative cover 76. Electrode 16A and electrode 16B may be formed or placed on an outer surface of cover 76. Circuitries and sensors 50-62, described above with respect to FIG. 2, may be formed or placed on an inner surface of cover 76, or within housing 15. In the illustrated example, antenna 26 is formed or placed on the inner surface of cover 76, but may be formed or placed on the outer surface in some examples. In some examples, one or more of sensors 62 may be formed or placed on the outer surface of cover 76. In some examples, insulative cover 76 may be positioned over an open housing 15 such that housing 15 and cover 76 enclose antenna 26 and circuitries and sensors 50-62, and protect the antenna and circuitries from fluids such as body fluids.

[0057] One or more of antenna 26 or circuitries and sensors 50-62 may be formed on the inner side of insulative cover 76, such as by using flip-chip technology. Insulative cover 76 may be flipped onto a housing 15. When flipped and placed onto housing 15, the components of IMD 10 formed on the inner side of insulative cover 76 may be positioned in a gap 78 defined by housing 15. Electrodes 16 may be electrically connected to switching circuitry 58 through one or more vias (not shown) formed through insulative cover 76. Insulative cover 76 may be formed of sapphire (i.e., corundum), glass, parylene, and / or any other suitable insulating material. Housing 15 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 16 may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes 16 may be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.

[0058] FIG. 4A is a conceptual drawing illustrating an IMD 10A, which may be an example configuration of IMD 10 of FIGS. 1-3 as an ICM. In the example shown in FIG. 4A, IMD 10A may be embodied as a monitoring device having housing 15, proximalelectrode 16A and distal electrode 16B. Housing 15 may further comprise first major surface 14, second major surface 18, proximal end 20, and distal end 22. Housing 15 encloses electronic circuitry located inside the IMD 10A and protects the circuitry contained therein from body fluids. Electrical feedthroughs provide electrical connection of electrodes 16A and 16B.

[0059] In the example shown in FIG. 4A, IMD 10A is defined by a length / ., a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D. In one example, the geometry of the IMD 10A - in particular a width W greater than the depth D - is selected to allow IMD 10A to be inserted under the skin of the patient using a minimally invasive procedure and to remain in the desired orientation during and after insertion. For example, the device shown in FIG. 4 A includes radial asymmetries (notably, the rectangular shape) along the longitudinal axis that maintains the device in the proper orientation following insertion. For example, the spacing between proximal electrode 64 and distal electrode 66 may range from 30 millimeters (mm) to 55mm, 35mm to 55mm, and from 40mm to 55mm and may be any range or individual spacing from 25mm to 60mm. In addition, IMD 10A may have a length L that ranges from 30mm to about 70mm. In other examples, the length L may range from 5mm to 60mm, 15mm to 50mm, 40mm to 60mm, 45mm to 60mm and may be any length or range of lengths between about 5mm and about 80mm. In addition, the width W of major surface 14 may range from 5mm to 15mm, 3mm to 10mm, and may be any single or range of widths between 3mm and 15mm. The thickness of depth D of IMD 10A may range from 2mm to 9mm. In other examples, the depth D of IMD 10A may range from 2mm to 5mm, may range from 5mm to 15mm, and may be any single or range of depths from 2mm to 15mm. In addition, IMD 10A according to an example of the present disclosure is has a geometry and size designed for ease of implant and patient comfort. Examples of IMD 10A described in this disclosure may have a volume of three cubic centimeters (cm) or less, 1.5 cubic cm or less or any volume between three and 1.5 cubic cm.

[0060] In the example shown in FIG. 4A, once inserted within the patient, the first major surface 14 faces outward, toward the skin of the patient while the second major surface 18 is located opposite the first major surface 14. In addition, in the example shown in FIG. 4 A, proximal end 20 and distal end 22 are rounded to reduce discomfortand irritation to surrounding tissue once inserted under the skin of the patient. IMD 10A, including instrument and method for inserting IMD 10 is described, for example, in U.S. Patent Publication No. 2014 / 0276928, incorporated herein by reference in its entirety.

[0061] Proximal electrode 16A and distal electrode 16B are used to sense cardiac data, e.g. ECG signals, intra-thoracically or extra-thoracically, which may be sub-muscularly or subcutaneously. Cardiac data, such as ECG signals, may be stored in a memory of IMD 10 A, and data may be transmitted via integrated antenna 30A to another medical device, which may be another implantable device or an external device, such as external device 12. In some example, electrodes 16A and 16B may additionally or alternatively be used for sensing any bio-potential signal of interest, which may be, for example, an ECG, EGM, EEG, EMG, or a nerve signal, from any implanted location.

[0062] In the example shown in FIG. 4A, proximal electrode 16A is in close proximity to the proximal end 20 and distal electrode 16B is in close proximity to distal end 22. In this example, distal electrode 16B is not limited to a flattened, outward facing surface, but may extend from first major surface 14 around rounded edges 24 and / or end surface 25 and onto the second major surface 18 so that the electrode 16B has a three-dimensional curved configuration. In some examples, electrode 16B is an uninsulated portion of a metallic, e.g., titanium, part of housing 15.

[0063] In the example shown in FIG. 4A, proximal electrode 16A is located on first major surface 14 and is substantially flat, and outward facing. However, in other examples proximal electrode 16A may utilize the three dimensional curved configuration of distal electrode 16B, providing a three dimensional proximal electrode (not shown in this example). Similarly, in other examples distal electrode 16B may utilize a substantially flat, outward facing electrode located on first major surface 14 similar to that shown with respect to proximal electrode 16 A. In some examples, IMD 10A may include sensors 62, as described with respect to FIG. 2.

[0064] The various electrode configurations allow for configurations in which proximal electrode 16A and distal electrode 16B are located on both first major surface 14 and second major surface 18. In other configurations, such as that shown in FIG. 4 A, only one of proximal electrode 16A and distal electrode 16B is located on both major surfaces 14 and 18, and in still other configurations both proximal electrode 16A and distal electrode 16B are located on one of the first major surface 14 or the second major surface18 (e.g., proximal electrode 16A located on first major surface 14 while distal electrode 16B is located on second major surface 18). In another example, IMD 10A may include electrodes on both major surface 14 and 18 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on IMD 10 A. Electrodes 16A and 16B may be formed of a plurality of different types of biocompatible conductive material, e.g. stainless steel, titanium, platinum, iridium, or alloys thereof, and may utilize one or more coatings such as titanium nitride or fractal titanium nitride.

[0065] In the example shown in FIG. 4A, proximal end 20 includes a header assembly 28 that includes one or more of proximal electrode 16 A, integrated antenna 30 A, antimigration projections 32, and / or suture hole 34. Integrated antenna 30A is located on the same major surface (i.e., first major surface 14) as proximal electrode 16A and is also included as part of header assembly 28. Integrated antenna 30A allows IMD 10A to transmit and / or receive data. In other examples, integrated antenna 30A may be formed on the opposite major surface as proximal electrode 16 A, or may be incorporated within the housing 15 of IMD 10 A. In the example shown in FIG. 4A, anti -migration projections 32 are located adjacent to integrated antenna 30A and protrude away from first major surface 14 to prevent longitudinal movement of the device. In the example shown in FIG. 4A, anti -migration projections 32 include a plurality (e.g., nine) small bumps or protrusions extending away from first major surface 14. As discussed above, in other examples anti-migration projections 32 may be located on the opposite major surface as proximal electrode 16A and / or integrated antenna 30A. In addition, in the example shown in FIG. 4A, header assembly 28 includes suture hole 34, which provides another means of securing IMD 10A to the patient to prevent movement following insertion. In the example shown, suture hole 34 is located adjacent to proximal electrode 16A. In one example, header assembly 28 is a molded header assembly made from a polymeric or plastic material, which may be integrated or separable from the main portion of IMD 10 A.

[0066] FIG. 4B is a perspective drawing illustrating another IMD 10B, which may be another example configuration of IMD 10 from FIGS. 1-3. IMD 10B of FIG. 4B may be configured substantially similarly to IMD lOA of FIG. 4A, with differences between them discussed herein.

[0067] IMD 10B may include a leadless, subcutaneously-implantable monitoring device, e.g. an ICM. IMD 10B includes housing having a base 40 and an insulative cover42. Proximal electrode 16C and distal electrode 16D may be formed or placed on an outer surface of cover 42. Various circuitries and components of IMD 10B, e.g., described below with respect to FIG. 3, may be formed or placed on an inner surface of cover 42, or within base 40. In some examples, a battery or other power source of IMD 10B may be included within base 40. In the illustrated example, antenna 30B is formed or placed on the outer surface of cover 42, but may be formed or placed on the inner surface in some examples. In some examples, insulative cover 42 may be positioned over an open base 40 such that base 40 and cover 42 enclose the circuitries and other components and protect them from fluids such as body fluids. In some examples, IMD 10B may include sensors 62, as described with respect to FIG. 2.

[0068] Circuitries and components may be formed on the inner side of insulative cover 42, such as by using flip-chip technology. Insulative cover 42 may be flipped onto a base 40. When flipped and placed onto base 40, the components of IMD 10B formed on the inner side of insulative cover 42 may be positioned in a gap 44 defined by base 40. Electrodes 16C and 16D and antenna 30B may be electrically connected to circuitry formed on the inner side of insulative cover 42 through one or more vias (not shown) formed through insulative cover 42. Insulative cover 42 may be formed of sapphire (i.e., corundum), glass, parylene, and / or any other suitable insulating material. Base 40 may be formed from titanium or any other suitable material (e.g., a biocompatible material). Electrodes 16C and 16D may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes 16C and 16D may be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.

[0069] In the example shown in FIG. 4B, the housing of IMD 10B defines a length / ., a width W and thickness or depth D and is in the form of an elongated rectangular prism wherein the length L is much larger than the width W, which in turn is larger than the depth D, similar to IMD 10A of FIG. 4B. For example, the spacing between proximal electrode 64 and distal electrode 66 may range from 30mm to 50mm, from 35mm to 45mm, or be approximately 40mm. In addition, IMD 10B may have a length L that ranges from 30mm to about 70mm. In other examples, the length L may range from 5mm to 60mm, 40mm to 60mm, 45mm to 55mm, or be approximately 45mm. In addition, the width may range from 3mm to 15mm, such as approximately 8mm. The thickness ofdepth D of IMD 10B may range from 2mm to 15mm, from 3 to 5mm, or be approximately 4mm. IMD 10B may have a volume of three cubic centimeters (cm) or less, or 1.5 cubic cm or less, such as approximately 1.4 cubic cm.

[0070] In the example shown in FIG. 4B, once inserted subcutaneously within the patient, outer surface of cover 42 faces outward, toward the skin of the patient. In addition, as shown in FIG. 4B, proximal end 46 and distal end 48 are rounded to reduce discomfort and irritation to surrounding tissue once inserted.

[0071] FIG. 5 is a block diagram illustrating an example configuration of components of external device 12. In the example of FIG. 5, external device 12 includes processing circuitry 80, communication circuitry 82, storage device 84, and user interface 86.

[0072] Processing circuitry 80 may include one or more processors that are configured to implement functionality and / or process instructions for execution within external device 12. For example, processing circuitry 80 may be capable of processing instructions stored in storage device 84. Processing circuitry 80 may include, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, processing circuitry 80 may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions ascribed herein to processing circuitry 80.

[0073] Communication circuitry 82 may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as IMD 10. Under the control of processing circuitry 80, communication circuitry 82 may receive downlink telemetry from, as well as send uplink telemetry to, IMD 10, or another device. Communication circuitry 82 may be configured to transmit 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. Communication circuitry 82 may also be configured to communicate with devices other than IMD 10 via any of a variety of forms of wired and / or wireless communication and / or network protocols.

[0074] Storage device 84 may be configured to store information within external device 12 during operation. Storage device 84 may include a computer-readable storage medium or computer-readable storage device. In some examples, storage device 84 includes one or more of a short-term memory or a long-term memory. Storage device 84may include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. In some examples, storage device 84 is used to store data indicative of instructions for execution by processing circuitry 80. Storage device 84 may be used by software or applications running on external device 12 to temporarily store information during program execution.

[0075] Data exchanged between external device 12 and IMD 10 may include operational parameters. External device 12 may transmit data including computer readable instructions which, when implemented by IMD 10, may control IMD 10 to change one or more operational parameters and / or export collected data. For example, processing circuitry 80 may transmit an instruction to IMD 10 which requests IMD 10 to export collected cardiac data (e.g., digitized ECG signals, and / or digitized heart sound beat signals) to external device 12. In turn, external device 12 may receive the collected cardiac data from IMD 10 and store the collected cardiac data in storage device 84.

[0076] Processing circuitry 80 may implement any of the techniques described herein to adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values in response to a determination that heart sound data satisfies the heart sound blood pressure sampling threshold, adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values in response to receiving abnormal heart sound data, and / or control determination of a blood pressure value of a patient using an optical sensor based on the adjusted rate of sensing optical signals. Processing circuitry 80 may implement any of the techniques described herein to select a particular machine learning model, such as abnormal heart sound data machine learning model, to determine blood pressure values using the optical signals in response to a determination that heart sound data satisfies an abnormal heart sound threshold, and apply the optical signals to the abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals.

[0077] A user, such as a clinician or patient 4, may interact with external device 12 through user interface 86. User interface 86 includes a display (not shown), such as a liquid crystal display (LCD) or a light emitting diode (LED) display or other type of screen, with which processing circuitry 80 may present information related to IMD 10, e.g., cardiac data, ECG signals, optical signals, blood pressure values, heart sound signals, heart sound features, indications of blood pressure values, indications of a determinationthat an acute health event occurred or a risk score that an acute health event may occur during a particular period of time. In addition, user interface 86 may include an input mechanism to receive input from the user. The input mechanisms may include, for example, any one or more of buttons, a keypad (e.g., an alphanumeric keypad), a peripheral pointing device, a touch screen, or another input mechanism that allows the user to navigate through user interfaces presented by processing circuitry 80 of external device 12 and provide input. In other examples, user interface 86 also includes audio circuitry for providing audible notifications, instructions or other sounds to the user, receiving voice commands from the user, or both.

[0078] FIG. 6 is a block diagram illustrating an example system that includes an access point 90, a network 92, external computing devices, such as a server 94, and one or more other computing devices 100A-100N (collectively, “computing devices 100”), which may be coupled to IMD 10 and external device 12 via network 92, in accordance with one or more techniques described herein. In this example, IMD 10 may use communication circuitry 54 to communicate with external device 12 via a first wireless connection, and to communicate with an access point 90 via a second wireless connection. In the example of FIG. 6, access point 90, external device 12, server 94, and computing devices 100 are interconnected and may communicate with each other through network 92.

[0079] Access point 90 may include a device that connects to network 92 via any of a variety of connections, such as telephone dial-up, digital subscriber line (DSL), or cable modem connections. In other examples, access point 90 may be coupled to network 92 through different forms of connections, including wired or wireless connections. In some examples, access point 90 may be a user device, such as a tablet or smartphone, that may be co-located with the patient. IMD 10 may be configured to transmit physiological data, such as digitized heart sound signals, heart sound data, digitized optical signals, blood pressure values, indications of blood pressure values, and / or digitized ECG signals indications to access point 90. Access point 90 may then communicate the retrieved physiological data to server 94 via network 92.

[0080] In some cases, server 94 may be configured to provide a secure storage site for data that has been collected from IMD 10 and / or external device 12. In some cases, server 94 may assemble data in web pages or other documents for viewing by trainedprofessionals, such as clinicians, via computing devices 100. One or more aspects of the illustrated system of FIG. 6 may be implemented with general network technology and functionality, which may be similar to that provided by the Medtronic CareLink™ Network. In some examples, server 94 may communicate with computing device 100 via network 92. For example, server 94 may communicate an analysis of data, such as determination of heart sound data using the heart sound signals, determination of whether the heart sound data satisfies a heart sound blood pressure sampling, determination of blood pressure values of the patient based on the optical signals, determination of whether an acute health event occurred or a risk score that an acute health event may occur during a particular period of time based on blood pressure values determined via an adjusted rate of sensing optical signals, application of optical signals to a selected abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals to computing device 100, external device 12, or any other computing device via network 92. For example, server 94 may communicate a blood pressure value of a patient or communicate an indication of the determination that an acute health event occurred or a risk score that an acute health event may occur during a particular period of time to computing device 100, external device 12, or any other computing device via network 92.

[0081] In some examples, one or more of computing devices 100 may be a tablet or other smart device located with a clinician, by which the clinician may program, receive alerts from, and / or interrogate IMD 10. For example, the clinician may access data collected by IMD 10 through a computing device 100, such as when patient 4 is in in between clinician visits, to check on a status of a medical condition. In some examples, the clinician may enter instructions for a medical intervention for patient 4 into an application executed by computing device 100, such as based on a status of a patient condition determined by IMD 10, external device 12, server 94, or any combination thereof, or based on other patient data known to the clinician. Device 100 then may transmit the instructions for medical intervention to another of computing devices 100 located with patient 4 or a caregiver of patient 4. For example, such instructions for medical intervention may include an instruction to change a drug dosage, timing, or selection, to schedule a visit with the clinician, or to seek medical attention. In further examples, a computing device 100 may generate an alert to patient 4 based on a status of amedical condition of patient 4, which may enable patient 4 proactively to seek medical attention prior to receiving instructions for a medical intervention. In this manner, patient 4 may be empowered to take action, as needed, to address his or her medical status, which may help improve clinical outcomes for patient 4.

[0082] In the example illustrated by FIG. 6, server 94 includes a storage device 96, e.g., to store data retrieved from IMD 10, and processing circuitry 98. Although not illustrated in FIG. 6 computing devices 100 may similarly include a storage device and processing circuitry. Processing circuitry 98 may include one or more processors that are configured to implement functionality and / or process instructions for execution within server 94. For example, processing circuitry 98 may be capable of processing instructions stored in storage device 96. Processing circuitry 98 may include or be coupled to communication circuitry that may include any suitable hardware, firmware, software or any combination thereof for communicating with another device. In some examples, a description of processing circuitry 98 outputting a signal, such as a classification, may include processing circuitry 98 causing communication circuitry of server 94 to output the signal. Processing circuitry 98 may include, for example, microprocessors, DSPs, ASICs, FPGAs, or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuitry. Accordingly, processing circuitry 98 may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions ascribed herein to processing circuitry 98. Processing circuitry 98 of server 94 and / or the processing circuity of computing devices 100 may implement any of the techniques described herein to adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values in response to a determination that heart sound data satisfies the heart sound blood pressure sampling threshold, adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values in response to receiving abnormal heart sound data, and / or control determining of a blood pressure value of a patient using an optical sensor based on the adjusted rate of sensing optical signals. Processing circuitry 98 of server 94 and / or the processing circuity of computing devices 100 may implement any of the techniques described herein to select a particular machine learning model to determine blood pressure values using the optical signals in response to a determination that heart sound data satisfies an abnormal heart sound threshold and apply the optical signals to the abnormal heart sound data machinelearning model to determine blood pressure values of the patient based on the optical signals.

[0083] Storage device 96 may include a computer-readable storage medium or computer-readable storage device. In some examples, storage device 96 may be referred to as a memory. In some examples, storage device 96 includes one or more of a short-term memory or a long-term memory. Storage device 96 may include, for example, RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. In some examples, storage device 96 is used to store data indicative of instructions for execution by processing circuitry 98.

[0084] Although the techniques for adjusting a rate of sensing optical signals via the optical sensor for determination of blood pressure values in response to a determination that heart sound data satisfies the heart sound blood pressure sampling threshold, adjusting a rate of sensing optical signals via the optical sensor for determination of blood pressure values in response to receiving abnormal heart sound data, and / or controlling determining of a blood pressure value of a patient using an optical sensor based on the adjusted rate of sensing optical signals, and / or the techniques for selecting a particular machine learning model to determine blood pressure values using the optical signals in response to a determination that heart sound data satisfies an abnormal heart sound threshold and applying the optical signals to the abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals are described herein primarily (e.g., with respect to FIGS. 7—9) as being performed by processing circuitry 50 of IMD 10, such techniques may be performed, in whole or part, by processing circuitry of any one or more devices of system 2, processing circuitry 98 of server 94, processing circuitry of one or more computing devices 100, or processing circuitry 80 of external device 12.

[0085] FIG. 7A is a flow diagram illustrating an example technique for operation of a medical system 2 to adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values in response to a determination that heart sound data satisfies the heart sound blood pressure sampling threshold. As indicated by FIG. 7A, processing circuitry 50 may determine blood pressure values of a patient based on optical signals sensed via an optical sensor 62B at a particular rate of sensing optical signals (700). Processing circuitry 50 may determine heart sound data using sound signals sensedvia a heart sound sensor 62A (710). Processing circuitry 50 may determine whether heart sound data satisfies a heart sound blood pressure sampling threshold (720). In some examples, a heart sound blood pressure sampling threshold may indicate desirable conditions to determine blood pressure at an adjusted rate of sensing optical signals, such as an increased rate. In some examples, trending stability in the sensed optical signal as a stand-alone measurement may be used to adjust rate of sensing optical signals without input from the heart sounds sampling threshold. In some examples, processing circuitry 50 may determine the heart sound data indicates a change in cardiac function in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold. In some examples, a change in cardiac function may indicate an occurrence of a health event, such as an acute health event. In some examples, processing circuitry 50 may determine the heart sound data indicates patient activity satisfies a patient activity threshold in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold.

[0086] In some examples, in response to the heart sound data not satisfying a heart sound blood pressure sampling threshold (e.g., “NO” of 720), processing circuitry may determine to maintain a rate of sensing optical signals via the optical sensor 62B for determination of blood pressure values (735). In some examples, in response to the heart sound data not satisfying a heart sound blood pressure sampling threshold (e.g., “NO” of 720), processing circuitry may determine to temporarily stop sensing blood pressure via the optical sensor.

[0087] In response to the heart sound data satisfying a heart sound blood pressure sampling threshold (e.g., “YES” of 720), processing circuitry 50 may adjust a rate of sensing optical signals via the optical sensor 62B for determination of blood pressure values (730). Processing circuitry 50 may control determination of a blood pressure value of the patient via the optical sensor based on the adjusted rate of sensing optical signals (740).

[0088] FIG. 7B is a flow diagram illustrating an example technique for operation of a medical system 2 to adjust a rate of sensing optical signals via the optical sensor in response to receiving abnormal heart sound data for determination of blood pressure values. As indicated by FIG. 7B, processing circuitry 50 may determine blood pressure values of a patient based on optical signals sensed via an optical sensor 62B (770). Inresponse to receiving abnormal heart sound data, processing circuitry 50 may adjust a rate of sensing optical signals via the optical sensor 62B (780). Processing circuitry 50 may control determination of a blood pressure value of the patient using the optical sensor based on the adjusted rate of sensing optical signals (790).

[0089] FIG. 8 is a flow diagram illustrating an example technique for operation of a medical system 2 to select a particular machine learning model to determine blood pressure values using the optical signals in response to a determination that heart sound data satisfies an abnormal heart sound threshold and apply the optical signals to the abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals. As indicated by FIG. 8, processing circuitry 50 may determine heart sound data using sound signals sensed via a heart sound sensor 62 A (800). Processing circuitry 50 may determine whether heart sound data satisfies an abnormal heart sound threshold (810). In some examples, in response to the heart sound data not satisfying an abnormal heart sound threshold (e.g., “NO” of 810), processing circuitry 50 may determine to select a normal heart sound data machine learning model, such as a machine learning model trained on normal heart sound data, to determine blood pressure values using the optical signals (825). Processing circuitry 50 may apply the optical signals to the normal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals (835). In some examples, in response to the heart sound data satisfying an abnormal heart sound threshold (e.g., “YES” of 810), processing circuitry 50 may determine to select an abnormal heart sound data machine learning model, such as a machine learning model trained on abnormal heart sound data, to determine blood pressure values using the optical signals (820). Processing circuitry 50 may apply the optical signals to the abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals (830).

[0090] In some examples, processing circuitry 50 may utilize machine learning, such as a deep learning algorithm or model (e.g., a neural network or deep belief network), to determine blood pressure values of the patient based on the optical signals and, in some examples, one or more additional parameters of patient 4. Processing circuitry 50 may be configured to execute an artificial intelligence (Al) engine that operates according to one or more models, such as machine learning models. Machine learning models may includeany number of different types of machine learning models, such as neural networks, deep neural networks, convolution neural networks, recurrent neural networks, such as long short term memory networks, dense neural networks, and the like. In some examples, various feature inputs to the Al engine may be fed as direct inputs to different layers in a network and not necessarily prior to the convolution layers. Although described with respect to machine learning models, the techniques described in this disclosure are also applicable to other types of Al models, including rule-based models, finite state machines, and the like. For example, the techniques described in this disclosure are also applicable to Bayesian Belief Networks (BBN) or Bayesian machine learning models (these sometimes referred to as Bayesian Networks or Bayesian frameworks herein), Markov random fields, graphical models, Al models (e.g., Naive Bayes classifiers or deep learning models), and / or other belief networks, such as sigmoid belief networks, deep belief networks (DBNs), etc. In other examples, the disclosed technology may leverage non-Bayesian prediction or probability modeling, such as frequentist inference modeling or other statistical models.

[0091] Machine learning may generally enable a computing device to analyze input data and identify an action to be performed responsive to the input data. Each machine learning model may be trained using training data that reflects likely input data. The training data may be labeled or unlabeled (meaning that the correct action to be taken based on a sample of training data is explicitly stated or not explicitly stated, respectively).

[0092] The training of the machine learning model may be guided (in that a designer, such as a computer programmer, may direct the training to guide the machine learning model to identify the correct action in view of the input data) or unguided (in that the machine learning model is not guided by a designer to identify the correct action in view of the input data). In some instances, the machine learning model is trained through a combination of labeled and unlabeled training data, a combination of guided and unguided training, or possibly combinations thereof. Examples of machine learning include nearest neighbor, naive Bayes, decision trees, linear regression, support vector machines, neural networks, k-Means clustering, Q-learning, temporal difference, deep adversarial networks, evolutionary algorithms or other supervised, unsupervised, semi-supervised, or reinforcement learning algorithms to train one or more models.

[0093] Processing circuitry 50 may utilize machine learning, such as a deep learning algorithm or model (e.g., a neural network or deep belief network), to blood pressure values based on extracted optical signals. Processing circuitry 50 may train a deep learning model to represent a relationship of the optical signals to blood pressure values. For example, processing circuitry 50 may train the deep learning model using optical signals from other patients. In some examples, processing circuitry 50 may train the deep leaning model by adjusting the weights of a hidden layer of a neural network model to balance the contribution of each input (e.g., characteristics of the optical signals) according to determining blood pressure values. Once the deep learning model is trained, processing circuitry 50 may obtain and apply data, such as the sensed optical signals, to the trained deep learning model.

[0094] Parameters of patient 4 that may be used as inputs for blood pressure value determination in addition to optical signals may include heart sound data, heart rate metrics, heart rate variability metrics, patient activity metrics, arrhythmia metrics, pacing therapy metrics, respiration metrics, and / or metrics of congestion, perfusion, or edema, such as may be determined based on measurements of an impedance of the patient. Further examples of patient parameters and artificial intelligence techniques that may be used to determine heart failure status (e.g., an example of heart sound data satisfying an abnormal heart sound threshold) with the heart sound metrics described herein are described in commonly assigned U.S. Patent Nos. 8,255,046 and 9,713,701, U.S App. Pub. Nos. 20160361026, 20200397308, 20210093253, and 20210093220, and International App. No. PCT / IB2023 / 053802, the entire contents of each of which are incorporated herein by reference.

[0095] FIG. 9 is an example of a machine learning model 902 and / or machine learning model 1100 being trained using supervised and / or reinforcement learning techniques, such as machine learning model 1100. Machine learning model 902 may correspond to any machine learning model described herein. The machine learning model 902 may be implemented using any number of models for supervised and / or reinforcement learning, such as but not limited to, an artificial neural network, a decision tree, naive Bayes network, support vector machine, or k-nearest neighbor model, to name only a few examples. In some examples, one or more of IMD 10, external device 12, server 94, and / or computing device(s) 100 initially trains the machine learning model 902 basedon a training set of metrics and corresponding to blood pressure values. The training set 900 may include a set of feature vectors, where each feature in the feature vector represents a value for a particular metric. One or more of IMD 10, external device 12, server 94, and / or computing device(s) 100 may select a training set comprising a set of training instances, each training instance comprising an association between one or more optical signals and blood pressure values. A prediction or classification by the machine learning model 902 may be compared 904 to the target output 903, and an error signal and / or machine learning model weights modification may sent / applied to the machine learning model 902 based on the comparison to learn / train 905 the machine learning model to modify / update the machine learning model 902. For example, one or more of IMD 10, external device 12, server 94, and / or computing device(s) 100 may, for each training instance in the training set, modify, based on the respective optical signals, and / or blood pressure values of the training instance, the machine learning model 902 to change a score generated by the machine learning model 902 in response to subsequent optical signals applied to the machine learning model 902.

[0096] FIG. 10 is a conceptual diagram illustrating an example machine learning model 1100 configured to generate one or more values indicative of blood pressure based on physiological parameter values, e.g., optical signals sensed by an optical sensor of an IMD and / or other devices as described herein. Machine learning model 1100 is an example of a deep learning model, or deep learning algorithm. One or more of IMD 10, external device 12, or server 94 may train, store, and / or utilize machine learning model 1100, but other devices may apply inputs associated with a particular patient to machine learning model 1100 in other examples. Some non-limiting examples of machine learning techniques include Bayesian probability models, Support Vector Machines, K- Nearest Neighbor algorithms, and Multi-layer Perceptron.

[0097] As shown in the example of FIG. 10, machine learning model 1100 may include three layers. These three layers include input layer 1102, hidden layer 1104, and output layer 1106. Output layer 1106 comprises the output from the transfer function 1105 of output layer 1106. Input layer 1102 represents each of the input values XI through X4 provided to machine learning model 1100. The number of inputs may be less than or greater than 4, including much greater than 4, e.g., hundreds or thousands. In someexamples, the input values may be any of the of physiological or other patient parameter values described herein.

[0098] Each of the input values for each node in the input layer 1102 is provided to each node of hidden layer 1104. In the example of FIG. 10, hidden layers 1104 include two layers, one layer having four nodes and the other layer having three nodes, but fewer or greater number of nodes may be used in other examples. Each input from input layer 1102 is multiplied by a weight and then summed at each node of hidden layers 1104. During training of machine learning model 1100, the weights for each input are adjusted to establish the relationship between input physiological parameter values and one or more output values indicative of a risk level of a health event for the patient. In some examples, one hidden layer may be incorporated into machine learning model 1100, or three or more hidden layers may be incorporated into machine learning model 1100, where each layer includes the same or different number of nodes.

[0099] The result of each node within hidden layers 1104 is applied to the transfer function of output layer 1106. The transfer function may be liner or non-linear, depending on the number of layers within machine learning model 1100. Example non-linear transfer functions may be a sigmoid function or a rectifier function. The output 1107 of the transfer function may be a value or values indicative of a blood pressure value of the patient. By applying the optical signals to a machine learning model, such as machine learning model 1100, processing circuitry of system 2 is able to determine blood pressure values with great accuracy, specificity, and sensitivity.

[0100] The techniques described in this disclosure may be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, various aspects of the techniques may be implemented within one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic QRS circuitry, as well as any combinations of such components, embodied in external devices, such as physician or patient programmers, stimulators, or other devices. The terms “processor” and “processing circuitry” may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry, or any other equivalent circuitry, and alone or in combination with other digital or analog circuitry.

[0101] For aspects implemented in software, at least some of the functionality ascribed to the systems and devices described in this disclosure may be embodied as instructions ona computer-readable storage medium such as RAM, DRAM, SRAM, magnetic discs, optical discs, flash memories, or forms of EPROM or EEPROM. The instructions may be executed to support one or more aspects of the functionality described in this disclosure.

[0102] In addition, in some aspects, the functionality described herein may be provided within dedicated hardware and / or software modules. Depiction of different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be realized by separate hardware or software components. Rather, functionality associated with one or more modules or units may be performed by separate hardware or software components, or integrated within common or separate hardware or software components. Also, the techniques could be fully implemented in one or more circuits or logic elements. The techniques of this disclosure may be implemented in a wide variety of devices or apparatuses, including an IMD, an external programmer, a combination of an IMD and external programmer, an integrated circuit (IC) or a set of ICs, and / or discrete electrical circuitry, residing in an IMD and / or external programmer.

[0103] Various aspects of the techniques may enable the following examples.

[0104] Example 1 : A system includes an implantable medical device including a heart sound sensor configured to sense heart sound signals; and an optical sensor configured to sense optical signals; and processing circuitry configured to: determine blood pressure values of a patient based on the optical signals; determine heart sound data using the heart sound signals; determine whether the heart sound data satisfies a heart sound blood pressure sampling threshold; in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold, adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values; and control determination of a blood pressure value of the patient via the optical sensor based on the adjusted rate of sensing optical signals.

[0105] Example 2: The system of example 1, wherein the heart sound data blood pressure sampling threshold is based on one or more of amplitude, slew rate, frequency, envelope, heart sound time interval, or energy of a heart sound signal.

[0106] Example 3: The system of any of examples 1-2, wherein the processing circuitry is positioned in the implantable medical device.

[0107] Example 4: The system of any of examples 1-3, wherein to adjust the rate of sensing optical signals via the optical sensor for determination of blood pressure values the processing circuitry is configured to increase the rate of sensing optical signals via the optical sensor.

[0108] Example 5: The system of any of examples 1-4, wherein the processing circuitry is configured to determine the heart sound data is abnormal in response to the determination that the heart sound data satisfies the heart sound blood pressure sampling threshold.

[0109] Example 6: The system of any of examples 1-5 wherein the processing circuitry is configured to determine the heart sound data indicates heart failure based on a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold.

[0110] Example 7: The system of any of examples 1-4, wherein the processing circuitry is configured to determine the heart sound data indicates patient activity satisfies a patient activity threshold in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold.

[0111] Example 8: The system of any of examples 1-7, wherein the processing circuitry is configured to: determine an acute health event occurred or a risk score that an acute health event may occur during a particular period of time based on blood pressure values determined via the adjusted rate of sensing optical signals; and output an indication of the determination that an acute health event occurred or a risk score that an acute health event may occur during a particular period of time.

[0112] Example 9: The system of any of examples 1-8, wherein the heart sound sensor comprises an accelerometer.

[0113] Example 10: The system of any of examples 1-8, wherein the heart sound sensor comprises a piezoelectric microphone.

[0114] Example 11 : A system includes an implantable medical device including an optical sensor configured to sense optical signals; and processing circuitry configured to determine blood pressure values of a patient based on the optical signals; based on receiving abnormal heart sound data, adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values; and control determination of a bloodpressure value of the patient using the optical sensor based on the adjusted rate of sensing optical signals.

[0115] Example 12: The system of example 11, wherein the processing circuitry is positioned in the implantable medical device.

[0116] Example 13: The system of any of examples 11-12, wherein to adjust the rate of sensing optical signals via the optical sensor for determination of blood pressure values the processing circuitry is configured to increase the rate of sensing optical signals via the optical sensor.

[0117] Example 14: The system of any of examples 11-13, wherein the abnormal heart sound data indicates heart failure.

[0118] Example 15: The system of any of examples 11-14, wherein the processing circuitry is configured to: determine an acute health event occurred or a risk score that an acute health event may occur during a particular period of time based on blood pressure values determined via the adjusted rate of sensing optical signals; and output an indication of the determination that an acute health event occurred or a risk score that an acute health event may occur during a particular period of time.

[0119] Example 16: A system includes an implantable medical device including a heart sound sensor configured to sense heart sound signals of a patient; and an optical sensor configured to sense optical signals of the patient; and processing circuitry configured to: determine heart sound data using the heart sound signals; determine the heart sound data satisfies an abnormal heart sound threshold; in response to the determination that the heart sound data satisfies an abnormal heart sound threshold, select an abnormal heart sound data machine learning model to determine blood pressure values using the optical signals; and apply the optical signals to the abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals.

[0120] Example 17: A method includes receiving sensed optical signals and sensed heart sound signals of a patient; determining blood pressure values of the patient based on the optical signals; determining heart sound data using the heart sound signals; determining whether the heart sound data satisfies a heart sound blood pressure sampling threshold; in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold, adjusting a rate of sensing optical signals via an opticalsensor for determination of blood pressure values; and controlling determination of a blood pressure value of the patient via the optical sensor based on the adjusted rate of sensing optical signals.

[0121] Example 18: The method of example 17, wherein the heart sound data blood pressure sampling threshold is based on one or more of amplitude, slew rate, frequency, envelope, heart sound time interval, or energy of a heart sound signal.

[0122] Example 19: The method of any of examples 17-18, wherein adjusting the rate of sensing optical signals via an optical sensor for determination of blood pressure values comprises increasing the rate of sensing optical signals via the optical sensor.

[0123] Example 20: The method of any of examples 17-19 further includes determining the heart sound data is abnormal in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold.

[0124] Example 21 : The method of any of examples 17-20 further includes determining the heart sound data indicates heart failure based on the determination that the heart sound data satisfies the heart sound blood pressure sampling threshold.

[0125] Example 22: The method of any of examples 17-19 further includes determining the heart sound data indicates patient activity satisfies a patient activity threshold in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold.

[0126] Example 23: The method of any of examples 17-22 further includes determining an acute health event occurred or a risk score that an acute health event may occur during a particular period of time based on blood pressure values determined via the adjusted rate of sensing optical signals; and outputting an indication of the determination that an acute health event occurred or a risk score that an acute health event may occur during a particular period of time.

[0127] Example 24: A method includes receiving sensed optical signals of a patient; determining blood pressure values of the patient based on the optical signals; in response to receiving abnormal heart sound data of the patient, adjusting a rate of sensing optical signals via an optical sensor for determination of blood pressure values; and controlling determination of a blood pressure value of the patient using the optical sensor based on the adjusted rate of sensing optical signals.

[0128] Example 25: The method of example 24, wherein the abnormal heart sound data indicates heart failure.

[0129] Example 26: A method includes receiving sensed optical signals and sensed heart sound signals of a patient; determining heart sound data using the heart sound signals; determining the heart sound data satisfies an abnormal heart sound threshold; in response to the determination that the heart sound data satisfies an abnormal heart sound threshold, selecting an abnormal heart sound data machine learning model to determine blood pressure values using the optical signals; and applying the optical signals to the abnormal heart sound data machine learning model to determine blood pressure values of the patient based on the optical signals.

[0130] Various examples have been described. These and other examples are within the scope of the following claims.

Claims

CLAIMSWhat is claimed is:

1. A system comprising: an implantable medical device comprising: a heart sound sensor configured to sense heart sound signals; and an optical sensor configured to sense optical signals; and processing circuitry configured to: determine blood pressure values of a patient based on the optical signals; determine heart sound data using the heart sound signals; determine whether the heart sound data satisfies a heart sound blood pressure sampling threshold; in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold, adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values; and control determination of a blood pressure value of the patient via the optical sensor based on the adjusted rate of sensing optical signals.

2. The system of claim 1, wherein the heart sound data blood pressure sampling threshold is based on one or more of amplitude, slew rate, frequency, envelope, heart sound time interval, or energy of a heart sound signal.

3. The system of any of claims 1-2, wherein the processing circuitry is positioned in the implantable medical device.

4. The system of any of claims 1-3, wherein to adjust the rate of sensing optical signals via the optical sensor for determination of blood pressure values the processing circuitry is configured to increase the rate of sensing optical signals via the optical sensor.

5. The system of any of claims 1-4, wherein the processing circuitry is configured to determine the heart sound data is abnormal in response to the determination that the heart sound data satisfies the heart sound blood pressure sampling threshold.

6. The system of any of claims 1-5, wherein the processing circuitry is configured to determine the heart sound data indicates heart failure based on a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold.

7. The system of any of claims 1-4, wherein the processing circuitry is configured to determine the heart sound data indicates patient activity satisfies a patient activity threshold in response to a determination that the heart sound data satisfies the heart sound blood pressure sampling threshold.

8. The system of any of claims 1-7, wherein the processing circuitry is configured to: determine an acute health event occurred or a risk score that an acute health event may occur during a particular period of time based on blood pressure values determined via the adjusted rate of sensing optical signals; and output an indication of the determination that an acute health event occurred or a risk score that an acute health event may occur during a particular period of time.

9. The system of any of claims 1-8, wherein the heart sound sensor comprises an accelerometer.

10. The system of any of claims 1-8, wherein the heart sound sensor comprises a piezoelectric microphone.

11. A system comprising: an implantable medical device comprising an optical sensor configured to sense optical signals; andprocessing circuitry configured to: determine blood pressure values of a patient based on the optical signals; based on receiving abnormal heart sound data, adjust a rate of sensing optical signals via the optical sensor for determination of blood pressure values; and control determination of a blood pressure value of the patient using the optical sensor based on the adjusted rate of sensing optical signals.

12. The system of claim 11, wherein the processing circuitry is positioned in the implantable medical device.

13. The system of any of claims 11-12, wherein to adjust the rate of sensing optical signals via the optical sensor for determination of blood pressure values the processing circuitry is configured to increase the rate of sensing optical signals via the optical sensor.

14. The system of any of claims 11-13, wherein the abnormal heart sound data indicates heart failure.

15. The system of any of claims 11-14, wherein the processing circuitry is configured to: determine an acute health event occurred or a risk score that an acute health event may occur during a particular period of time based on blood pressure values determined via the adjusted rate of sensing optical signals; and output an indication of the determination that an acute health event occurred or a risk score that an acute health event may occur during a particular period of time.

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