Body stability measurement

Abstract

A medical device system and method are disclosed, comprising accelerometer circuitry configured to generate at least one signal, memory, and processing circuitry coupled to the accelerometer circuitry and the memory. The processing circuitry is configured to: monitor a patient's sit-to-stand transition based on the at least one signal; detect the sit-to-stand transition; determine whether the patient was inactive for a predetermined period of time prior to the sit-to-stand transition; and determine a body stability score for the patient if the patient was inactive for at least the predetermined period of time prior to the sit-to-stand transition.

Description

Technical Field

[0001] The present disclosure relates generally to device systems, and more particularly to device systems configured to predict the likelihood that a person, such as a patient, may fall based on data generated by an accelerometer. Background Art

[0002] Implantable medical devices (IMDs) and external medical devices (e.g., wearable medical devices), including implantable pacemakers and implantable cardioverter-defibrillators (ICDs), and non-therapeutic insertable cardiac monitors (e.g., Medtronic's LINQ TM ), records electrocardiogram (EGM) signals (e.g., P and R waves) to sense cardiac events. An IMD detects episodes of bradycardia, tachycardia, and / or fibrillation from the sensed cardiac events, and some IMDs respond to the episodes as needed with pacing therapy or high-voltage anti-tachyarrhythmia shocks (e.g., cardioversion or defibrillation shocks). These and other medical devices may include sensors that generate other physiologically based signals, such as signals that vary based on patient motion or activity, cardiovascular stress, blood oxygen saturation, edema, or thoracic impedance, or be part of a system that includes these sensors. Summary of the Invention

[0003] Generally, the present disclosure relates to techniques for determining an increased likelihood that a patient may fall based on accelerometer-generated data. More specifically, the present disclosure contemplates a medical device that monitors a patient's sit-to-stand transitions and determines a body stability parameter or score for the patient based on accelerometer-generated data surrounding the sit-to-stand transitions.

[0004] In other examples, a device is disclosed that includes: an accelerometer circuit system configured to generate at least one signal; a memory; and a processing circuit system connected to the accelerometer circuit system and the memory, the processing circuit system configured to: detect a sit-to-stand transition of a patient based on the at least one signal; determine whether the patient was inactive for a predetermined time period before the sit-to-stand transition; and if the patient was inactive for at least the predetermined time period before the sit-to-stand transition, determine a body stability score for the patient based on the at least one signal.

[0005] In other examples, a method is disclosed that includes detecting a sit-to-stand transition of a patient based on at least one accelerometer signal; determining whether the patient was inactive for a predetermined time period before the sit-to-stand transition; and determining a body stability score for the patient based on the at least one accelerometer signal if the patient was inactive for at least the predetermined time period before the sit-to-stand transition.

[0006] In other examples, a non-transitory computer-readable storage medium containing instructions is disclosed that, when executed by a processing circuit system of a device, causes the device to: detect a sit-to-stand transition of a patient based on at least one accelerometer signal; determine whether the patient was inactive for a predetermined time period before the sit-to-stand transition; and determine a body stability score for the patient based on the at least one accelerometer signal if the patient was inactive for at least the predetermined time period before the sit-to-stand transition.

[0007] This 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 devices and methods described in detail in the following figures and description. The details of one or more aspects of the disclosure are set forth in the figures and the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] Figure 1 is a conceptual diagram illustrating an example medical device system in conjunction with a patient.

[0009] Figure 2 is a conceptual diagram illustrating another example medical device system in conjunction with a patient.

[0010] Figure 3 It is depicted Figure 2 A perspective view of an example configuration of an implantable cardiac monitor;

[0011] Figures 4A to 4C conceptual diagrams depicting another example medical device system in conjunction with a patient from the front, side, and top views, respectively.

[0012] Figure 5 is a conceptual diagram illustrating another example medical device system in conjunction with a patient.

[0013] Figure 6 It is depicted Figures 4A to 5 Conceptual diagram of an example configuration of an intracardiac pacing device.

[0014] Figure 7 is a functional block diagram illustrating an example configuration of an implantable medical device.

[0015] Figure 8 is a functional block diagram illustrating an example configuration of an external device configured to communicate with one or more implantable medical devices.

[0016] Figure 9 is a functional block diagram illustrating an example system including a remote computing device, such as a server and one or more other computing devices, connected to an implantable medical device and / or an external device via a network.

[0017] Figure 10is a flow chart depicting a first example method for determining a patient's physical stability score based on accelerometer-generated data according to the present disclosure.

[0018] Figure 11 is a flow chart depicting a second example method for determining a patient's physical stability score based on accelerometer-generated data according to the present disclosure.

[0019] Figure 12 This is a conceptual diagram showing the sagittal axis, longitudinal axis, and transverse axis in a three-dimensional coordinate system.

[0020] Figure 13 is a graph depicting sagittal, longitudinal, and transverse axis signals generated by an accelerometer during a series of sit-to-stand and stand-to-sit movements.

[0021] Figure 14 It is depicted Figure 13 Conceptual illustration of how several features of the sagittal axis signal change over a range of sit-to-stand and stand-to-sit movements. DETAILED DESCRIPTION

[0022] Implantable medical devices (IMDs) and external medical devices (e.g., wearable medical devices), including implantable pacemakers and implantable cardioverter-defibrillators (ICDs), record electrocardiogram (EGM) signals (e.g., P waves and R waves) to sense cardiac events. An IMD detects episodes of bradycardia, tachycardia, and / or fibrillation from the sensed cardiac events, and some IMDs respond to the episodes as needed with pacing therapy or high-voltage anti-tachyarrhythmia shocks (e.g., cardioversion or defibrillation shocks). These and other medical devices may include sensors that generate other physiologically based signals, such as signals that vary based on patient motion or activity, cardiovascular pressure, blood oxygen saturation, edema, or thoracic impedance, or be part of a system that includes these sensors. According to features or aspects of the present disclosure, one or more such signals may be utilized to provide an objective measurement of a patient's physical stability.

[0023] For example, a medical device system according to certain features or aspects of the present disclosure includes an accelerometer circuit system that is configured to generate multiple signals including sagittal (frontal) axis signals, and a processing circuit system that is configured to calculate a patient-specific body stability score based on data generated by the accelerometer around the sit-to-stand transition from the sagittal axis signal, the transverse axis signal, and / or the longitudinal axis. Among them, such embodiments can provide an objective measurement of health changes (or no changes) to help guide treatment, because a patient-specific body stability score based on data generated by the accelerometer around the sit-to-stand transition can help determine whether health status is improving, declining, or stable. Although not limited to this, an understanding of various aspects of the present disclosure can be obtained from the following discussion in conjunction with the accompanying drawings. Although the present disclosure can provide examples, including identifying medical devices that can be configured to implement the technology described herein, these identifications are not meant to be limiting. Any device with an accelerometer can be used to implement the technology of the present disclosure.

[0024] For example, Figure 1 is a conceptual diagram illustrating an example medical device system 8A in conjunction with patient 14A. Medical device system 8A is an example of a medical device system configured to implement the techniques described herein for determining a patient's physical stability based on data generated by an accelerometer. In the illustrated example, medical device system 8A includes an implantable medical device (IMD) 10A coupled to a ventricular lead 20 and an atrial lead 21. IMD 10A is an implantable cardioverter-defibrillator (ICD) capable of delivering pacing, cardioversion, and defibrillation therapy to heart 16A of patient 14A and will be referred to hereinafter as ICD 10A.

[0025] A ventricular lead 20 and an atrial lead 21 are electrically coupled to the ICD 10A and extend into the patient's heart 16A. Ventricular lead 20 includes electrodes 22 and 24, positioned on the lead in the patient's right ventricle (RV) as shown, for sensing ventricular EGM signals and pacing in the RV. Atrial lead 21 includes electrodes 26 and 28, positioned on the lead in the patient's right atrium (RA) for sensing atrial EGM signals and pacing in the RA.

[0026] The ventricular lead 20 additionally carries a high voltage coil electrode 42, and the atrial lead 21 carries a high voltage coil electrode 44, for delivering cardioversion and defibrillation shocks. The term "anti-tachyarrhythmia shock" may be used herein to refer to both a cardioversion shock and a defibrillation shock. In other examples, the ventricular lead 20 may carry both high voltage coil electrodes 42 and 44, or may carry both a high voltage coil electrode and a defibrillation shock. Figure 1 High voltage coil electrodes other than those shown in the examples.

[0027] ICD 10A can use both ventricular lead 20 and atrial lead 21 to acquire electrocardiogram (EGM) signals from patient 14A and deliver therapy in response to the acquired data. Medical device system 8A is shown as having a dual-chamber ICD configuration, but other examples may include one or more additional leads, such as a coronary sinus lead that extends into the right atrium, through the coronary sinus, and into the cardiac veins to position an electrode along the left ventricle (LV) for sensing LV EGM signals and delivering pacing pulses to the LV. In other examples, the medical device system may be a single-chamber system, or otherwise not include an atrial lead 21.

[0028] Processing circuitry, sensing circuitry, and other circuitry configured to perform the techniques described herein are housed within a sealed housing 12. Housing 12 (or a portion thereof) may be electrically conductive so as to function as an electrode for pacing or sensing, or as an active electrode, during defibrillation. As such, housing 12 is also referred to herein as a "housing electrode" 12.

[0029] The ICD 10A may transmit EGM signal data and cardiac rhythm episode data acquired by the ICD 10A, as well as data regarding the delivery of therapy administered by the ICD 10A, and data associated with the patient's physical stability derived from accelerometer-generated data, in manipulated and / or raw form, which may be compressed, encoded, and / or the like, to an external device 30A. The external device 30A may be a computing device, for example, used in a home, outpatient, clinic, or hospital setting, to communicate with the ICD 10A via wireless telemetry. The external device 30A may be coupled to a remote monitoring system, such as the one available from Medtronic plc of Dublin, Ireland. As examples, external device 30A may be a programmer, an external monitor, or a consumer device, such as a smartphone, such as that manufactured by Apple Inc. of Cupertino, California.

[0030] The external device 30A can be used to program commands or operating parameters into the ICD 10A to control its functions, for example, when configured as a programmer for the ICD 10A, or when configured to provide time-stamped data for use in calculating a patient-specific physical stability score associated with a sit-to-stand transition. The external device 30A can be used to interrogate the ICD 10A to retrieve data, including device operating data and physiological data accumulated in the IMD memory, such as data associated with a patient-specific physical stability score associated with a sit-to-stand transition. Interrogation can be automatic, for example, according to a schedule, or in response to a remote or local user command. Programmers, external monitors, and consumer devices are examples of external devices 30A that can be used to interrogate the ICD 10A. Examples of communication technologies used by the ICD 10A and the external device 30A include radio frequency (RF) telemetry, which can be via RF link established by wireless LAN, wireless WAN, Medical Implant Communications Service (MICS) or other wireless connections.

[0031] like Figure 1 As shown, in some examples, medical device system 8A may also include a pressure-sensing IMD 50. In the example shown, pressure-sensing IMD 50 is implanted in the pulmonary artery of patient 14A. In some examples, one or more pressure-sensing IMDs 50 may additionally or alternatively be implanted within a chamber of heart 16A, or generally at other locations in the circulatory system.

[0032] In one example, pressure sensing IMD 50 is configured to sense the blood pressure of patient 14A. For example, pressure sensing IMD 50 can be arranged in the pulmonary artery and configured to sense the pressure of blood flowing from the right ventricular outflow tract (RVOT) of the right ventricle through the pulmonary valve to the pulmonary artery. Thus, pressure sensing IMD 50 can directly measure the pulmonary artery diastolic pressure (PAD) of patient 14A. The PAD value is a pressure value that can be used for patient monitoring. For example, PAD can be used as a basis for assessing congestive heart failure in a patient.

[0033] However, in other examples, the pressure sensing IMD 50 may be used to measure blood pressure values other than the PAD. For example, the pressure sensing IMD 50 may be positioned in the right ventricle 28 of the heart 14 to sense RV systolic or diastolic pressure, or may sense systolic or diastolic pressure at other locations in the cardiovascular system, such as within the pulmonary artery. Figure 1 As shown, pressure-sensing IMD 50 is located in the main trunk of pulmonary artery 39. In other examples, a sensor such as pressure-sensing IMD 50 may be located in the right or left pulmonary artery beyond the pulmonary artery bifurcation.

[0034] Furthermore, the placement of pressure-sensing IMD 50 is not necessarily limited to the pulmonary side of the circulation. Pressure-sensing IMD 50 can potentially be placed on the systemic side of the circulation. For example, under certain conditions and with appropriate safety measures, pressure-sensing IMD 50 can even be placed in the left atrium, left ventricle, or aorta. Additionally, pressure-sensing IMD 50 is not limited to placement within the cardiovascular system. For example, pressure-sensing IMD 50 can be placed in the renal circulation. Placing pressure-sensing IMD 50 in the renal circulation can be beneficial, for example, to monitor the patient's degree of renal insufficiency based on the pressure-sensing IMD 50's monitoring of the renal circulation's pressure or some other indication.

[0035] In some examples, pressure-sensing IMD 50 includes a pressure sensor configured to respond to absolute pressure within the pulmonary artery of patient 14A. In such examples, pressure-sensing IMD 50 can be any of a variety of different types of pressure sensors. One form of pressure sensor that can be used to measure blood pressure is a capacitive pressure sensor. Another example pressure sensor is an inductive sensor. In some examples, pressure-sensing IMD 50 can also include a piezoelectric or piezoresistive pressure transducer. In some examples, pressure-sensing IMD 50 can include a flow sensor.

[0036] In one example, pressure-sensing IMD 50 includes a wireless pressure sensor that includes a capacitive pressure sensing element configured to measure blood pressure within the pulmonary artery. Pressure-sensing IMD 50 can communicate wirelessly with ICD 10A and / or external device 30A, for example, to transmit blood pressure measurements to one or both of the devices. Pressure-sensing IMD 50 can employ, for example, radio frequency (RF) or other telemetry technology to communicate with ICD 10A and other devices, including, for example, external device 30A. In another example, pressure-sensing IMD 50 can include a tissue conductance communication (TCC) system by which the device uses tissue of patient 14A as an electrical communication medium through which information is sent to and received from ICD 10A and / or external device 30A.

[0037] Medical device system 8A is an example of a medical device system that is configured to determine the body stability of a patient based on data generated by an accelerometer. Such contemplated techniques may be performed individually or collectively by processing circuitry of medical device system 8A, such as processing circuitry of one or both of ICD 10A and external device 30A, as discussed in further detail below. Figures 2 to 9Other example medical device systems that can be configured to implement these techniques are described. Although primarily described herein in the context of implantable medical devices that generate signals and, in some instances, deliver therapy, a medical device system implementing the techniques described in this disclosure may additionally or alternatively include an external medical device, such as a smartphone, that is configured to generate time-stamped data based at least on data generated by an accelerometer for use in measuring or determining patient body stability.

[0038] Figure 2 FIG1 is a conceptual diagram illustrating another example medical device system 8B in conjunction with patient 14B. Medical device system 8B is another example of a medical device system configured to implement the techniques described herein for determining patient body stability based on accelerometer-generated data. In the illustrated example, medical device system 8B includes IMD 10B and external device 30B.

[0039] IMD 10B is an insertable cardiac monitor (ICM) capable of sensing and recording cardiac EGM signals from a location external to heart 16B, and will be referred to hereinafter as ICM 10B. In addition, ICM 10B is capable of implementing one or more techniques according to the present disclosure for determining a patient's physical stability based on data generated by an accelerometer. In some instances, ICM 10B includes or is coupled to one or more additional sensors that generate one or more other physiological signals, such as signals that vary based on patient movement and / or posture, blood flow, or respiration. ICM 10B may be implanted external to the chest of patient 14B, e.g., subcutaneously or submuscularly, as in Figure 2 In some examples, the ICM 10B may use Reveal LINQ TM A form of ICM is available from Medtronic, Dublin, Ireland.

[0040] The external device 30B may be configured in a manner substantially similar to that described above with respect to the external devices 30A and 30B. Figure 1 The external device 30B may be configured in the manner described herein. The external device 30B may wirelessly communicate with the ICM 10B, for example, to program the functionality of the ICM and to retrieve from the ICM recorded physiological signals and / or patient parameter values or scores or other data derived from such signals. Both the ICM 10B and the external device 30B include processing circuitry, and the processing circuitry of either or both devices may perform the techniques described herein for determining patient body stability based on accelerometer-generated data, as discussed in further detail below.

[0041] Although not in Figure 2Although not illustrated in the example of FIG10B, a medical device system configured to implement the techniques of the present disclosure may include one or more implanted or external medical devices in addition to or in place of the ICM 10B. For example, a medical device system may include a pressure sensing IMD 50, a vascular ICD (e.g., Figure 1 ICD 10A), extravascular ICD (e.g., Figures 4A to 5 ICD 10C) or pacemaker (e.g., Figures 4A to 6 The invention also provides an IPD 10D implanted outside the heart but coupled to endocardial or epicardial leads. One or more such devices can generate accelerometer signals and include processing circuitry configured to perform, in whole or in part, the techniques described herein for determining patient body stability based on accelerometer-generated data. The implanted devices can communicate with each other and / or with an external device 30, and one of the implanted devices or the external device can ultimately calculate patient-specific body stability associated with a sit-to-stand transition from at least one of the sagittal, longitudinal, and transverse axis signals.

[0042] Figure 3 is a conceptual diagram illustrating an example configuration of the ICM 10B. Figure 3 In the example shown, ICM 300 can be embodied as a monitoring device having a housing 62, a proximal electrode 64, and a distal electrode 66. Housing 62 can also include a first major surface 68, a second major surface 70, a proximal end 72, and a distal end 74. Housing 62 encloses the electronic circuitry within ICM 10B and protects the circuitry contained therein from bodily fluids. Electrical feedthroughs provide electrical connections between electrodes 64 and 66.

[0043] exist Figure 3 In the example shown, the ICM 10B is defined by a length L, a width W, and a thickness or depth D, and is in the form of an elongated rectangular prism, wherein the length L is much greater than the width W, which in turn is greater than the depth D. In one example, the geometry of the ICM 10B—particularly the width W being greater than the depth D—is selected to allow the ICM 10B to be inserted under the patient's skin using a minimally invasive procedure and to be maintained in a desired orientation during insertion. For example, Figure 3The device shown includes radial asymmetry (particularly a rectangular shape) along the longitudinal axis, which maintains the device in the correct orientation after insertion. For example, in one instance, the spacing between the proximal electrode 64 and the distal electrode 66 can be in the range of 30 millimeters (mm) to 55 mm, 35 mm to 55 mm, and 40 mm to 55 mm, and can be any range or individual spacing from 25 mm to 60 mm. In addition, the ICM 10B can have a length L in the range of 30 mm to approximately 70 mm. In other instances, the length L can be in the range of 40 mm to 60 mm, 45 mm to 60 mm, and can be any length or range of lengths between about 30 mm and about 70 mm. In addition, the width W of the major surface 68 can be in the range of 3 mm to 10 mm, and can be any single width or range of widths between 3 mm and 10 mm. The thickness of the depth D of the ICM 10B can be in the range of 2 mm to 9 mm. In other instances, the depth D of the ICM 10B can be in the range of 2 mm to 5 mm, and can be any single depth or range of depths from 2 mm to 9 mm. In addition, the ICM 10B according to examples of the present disclosure has a geometry and size designed for ease of implantation and patient comfort. The examples of the ICM 10B described in the present disclosure can have a volume of 3 cubic centimeters (cm) or less, a volume of 1.5 cubic centimeters or less, or any volume between 3 cubic centimeters and 1.5 cubic centimeters. And, as discussed in further detail below, it is contemplated that the axis of the accelerometer that coincides with the axis along D can correspond to the patient's sagittal axis, and the sagittal axis signal can be used to measure or determine the patient's body stability, for example as part of an SST (sit-to-stand) performance transition. This is because the 3D accelerometer in the ICM 10B, for example, implanted in the chest, is relatively fixed during the life of the implant. The fixed chest position provides an opportunity to monitor changes in the upper body that occur during various activities. For example, when a patient sits down in a chair and gets out of it, the upper body has a reproducible movement (similar to a "bow" movement) which can be detected by the signal generated by the accelerometer.

[0044] exist Figure 3 In the example shown, once inserted into the patient, the first major surface 68 faces outwardly, toward the patient's skin, while the second major surface 70 is located opposite the first major surface 68. Figure 3In the example shown, the proximal end 72 and the distal end 74 are rounded to reduce discomfort and irritation to surrounding tissue once inserted under the patient's skin. The ICM 10B, including instruments and methods for inserting the ICM 10B, is described, for example, in U.S. patent application Ser. No. 14 / 204,227, filed on Mar. 11, 2014, entitled "SUBCUTANEOUS DELIVERY TOOL," which now publishes as U.S. Publication No. 2014 / 0276928 and claims priority to U.S. Provisional Patent Application No. 61 / 788,940.

[0045] Proximal electrode 64 and distal electrode 66 are used to sense cardiac signals, such as intrathoracic or extrathoracic ECG signals, which may be submuscular or subcutaneous. The ECG signals may be stored in a memory of ICM 10B, and the ECG data may be transmitted via integrated antenna 82 to another medical device, which may be another implantable device or an external device such as external device 30B. In some examples, electrodes 64 and 66 may additionally or alternatively be used to sense any biopotential signal of interest from any implanted location, which may be, for example, an EGM, EEG, EMG, or neural signal.

[0046] exist Figure 3 In the example shown, the proximal electrode 64 is proximal to the proximal end 72, and the distal electrode 66 is proximal to the distal end 74. In this example, the distal electrode 66 is not limited to a flat, outwardly facing surface, but may extend from the first major surface 68 around a rounded edge 76 and / or end surface 78 to the second major surface 70 so that the electrode 66 has a three-dimensional curved configuration. Figure 3 In the example shown, the proximal electrode 64 is located on the first major surface 68 and is substantially flat and outward-facing. However, in other examples, the proximal electrode 64 can utilize a three-dimensional curved configuration of the distal electrode 66 to provide a three-dimensional proximal electrode (not shown in this example). Similarly, in other examples, the distal electrode 66 can utilize a substantially flat, outward-facing electrode located on the first major surface 68, similar to the electrode shown with respect to the proximal electrode 64.

[0047] Various electrode configurations allow for configurations where the proximal electrode 64 and the distal electrode 66 are located on the first major surface 68 and the second major surface 70. In other configurations, such as Figure 3In the configuration shown, only one of the proximal electrode 64 and the distal electrode 66 is located on both major surfaces 68 and 70, while in other configurations, both the proximal electrode 64 and the distal electrode 66 are located on one of the first major surface 68 or the second major surface 70 (i.e., the proximal electrode 64 is located on the first major surface 68 and the distal electrode 66 is located on the second major surface 70). In another example, the ICM 10B can include electrodes on major surfaces 68 and 70 at or near the proximal and distal ends of the device, such that a total of four electrodes are included on the ICM 10B. The electrodes 64 and 66 can be formed from a variety of different types of biocompatible conductive materials, such as stainless steel, titanium, platinum, iridium, or alloys thereof, and can utilize one or more coatings, such as titanium nitride or fractal titanium nitride.

[0048] exist Figure 3 In the example shown, the proximal end 72 includes a head assembly 80 that includes one or more of the proximal electrode 64, an integrated antenna 82, anti-migration protrusions 84, and / or suture holes 86. The integrated antenna 82 is located on the same major surface (e.g., the first major surface 68) as the proximal electrode 64 and is also included as part of the head assembly 80. The integrated antenna 82 allows the ICM 10B to transmit and / or receive data. In other examples, the integrated antenna 82 can be formed on a major surface opposite the proximal electrode 64, or can be incorporated within the housing 82 of the ICM 10B. Figure 3 In the example shown, the anti-migration protrusion 84 is located adjacent to the integrated antenna 82 and projects away from the first major surface 68 to prevent longitudinal movement of the device. Figure 3 In the example shown, the anti-migration protrusions 84 include a plurality (e.g., nine) of small bumps or protrusions extending away from the first major surface 68. As discussed above, in other examples, the anti-migration protrusions 84 can be located on the major surface opposite the proximal electrode 64 and / or the integrated antenna 82. Figure 3 In the example shown, the head assembly 80 includes suture holes 86, which provide another means of securing the ICM 10B to the patient to prevent migration after insertion. In the example shown, the suture holes 86 are located adjacent to the proximal electrode 64. In one example, the head assembly 80 is a molded head assembly made of a polymer or plastic material that can be integrated with or separated from the main portion of the ICM 10B.

[0049] Figures 4A to 4C 1 and 2 are conceptual diagrams illustrating front, side, and top views, respectively, of another example medical device system 8C in conjunction with patient 14C. Medical device system 8C is another example of a medical device system configured to implement the techniques described herein for determining patient body stability based on accelerometer-generated data.

[0050] In the illustrated example, medical device system 8C includes an extracardiovascular ICD system 100A implanted in patient 14C. ICD system 100A includes an IMD 10C, which is an ICD and hereinafter referred to as ICD 10C, connected to at least one implantable cardioverter-defibrillator lead 102A. ICD 10C is configured to deliver high-energy cardioversion or defibrillation pulses to the patient's heart 16C when atrial or ventricular fibrillation is detected. The cardioversion shock is typically delivered synchronously with a detected R-wave when fibrillation detection criteria are met. The defibrillation shock is typically delivered when fibrillation criteria are met, and the R-wave cannot be discerned from the signal sensed by ICD 10C.

[0051] The ICD 10C is implanted subcutaneously or submuscularly on the left side of the patient 14C above the thorax. The defibrillation lead 102A may be at least partially implanted in a substernal location, for example, between the thorax and / or sternum 110 and the heart 16C. In one such configuration, a proximal portion of the lead 102A extends subcutaneously from the ICD 10C toward the sternum 110, and a distal portion of the lead 102A extends above, below, or beneath the sternum 110 in the anterior mediastinum 112. Figure 4C The anterior mediastinum 112 is composed of the pleura 116 ( Figure 1 C) laterally bounded by the pericardium 114 ( Figure 4C ) posteriorly and anteriorly by the sternum 110. In some instances, the anterior wall of the anterior mediastinum may also be formed by the transverse thoracic muscle and one or more costal cartilages. The anterior mediastinum includes a large amount of loose connective tissue (e.g., cellulite), some lymphatic vessels, lymph glands, substernal musculature (e.g., transverse pectoral muscles), branches of the internal thoracic artery, and the internal thoracic vein. In one instance, the distal portion of the lead 102A extends substantially along the posterior side of the sternum 110 within the loose connective tissue and / or substernal musculature of the anterior mediastinum. The lead 102A may be at least partially implanted in other intrathoracic locations, e.g., other non-vascular, extrapericardial locations, including spaces, tissues, or other anatomical features surrounding the pericardium or other portions of the heart and adjacent to but not attached to the pericardium or other portions of the heart and not above the sternum 110 or the thorax.

[0052] In other examples, the lead 102A can be implanted at other extracardiac locations. For example, the defibrillation lead 102A can extend subcutaneously above the thorax from the ICD 10C toward the center of the torso of the patient 14C, bend or rotate near the center of the torso, and extend subcutaneously above the thorax and / or sternum 110. The defibrillation lead 102A can be laterally offset to the left or right side of the sternum 110 or positioned on the sternum 110. The defibrillation lead 102A can extend substantially parallel to the sternum 110 or be angled laterally from the sternum 110 at either a proximal or distal end.

[0053] The defibrillation lead 102A includes an insulated lead body having a proximal end including a connector 104 configured to connect to the ICD 10C and a distal portion including one or more electrodes. The defibrillation lead 102A also includes one or more conductors that form an electrically conductive path within the lead body and interconnect the electrical connector and corresponding electrodes.

[0054] Defibrillation lead 102A includes a defibrillation electrode comprising two segments or sections 106A and 106B, collectively (or alternatively) referred to as defibrillation electrode 106. Defibrillation electrode 106 is oriented toward a distal portion of defibrillation lead 102A, for example, toward a portion of defibrillation lead 102A extending along sternum 110. Defibrillation lead 102A is positioned beneath and / or along sternum 110 such that a therapy carrier between defibrillation electrode 106A or 106B and a housing electrode formed by or on ICD 10C (or other second electrode of a therapy carrier) substantially spans a ventricle of heart 16C. In one example, the therapy carrier can be viewed as a line extending from a point on defibrillation electrode 106 (e.g., the center of one of defibrillation electrode segments 106A or 106B) to a point on the housing electrode of ICD 10C. In one example, defibrillation electrode 106 can be an elongated coil electrode.

[0055] The defibrillation lead 102A may also include one or more sensing electrodes, such as sensing electrodes 108A and 108B (individually or collectively, “sensing electrodes 108 ”), positioned along a distal portion of the defibrillation lead 102A. Figure 4A and Figure 4B In the depicted example, sensing electrodes 108A and 108B are separated from each other by defibrillation electrode 106A. However, in other examples, sensing electrodes 108A and 108B may both be distal to defibrillation electrode 106 or both be proximal to defibrillation electrode 106. In other examples, lead 102A may include more or fewer electrodes at various locations proximal and / or distal to defibrillation electrode 106. In the same or different examples, ICD 10C may include one or more electrodes on another lead (not shown).

[0056] The ICD system 100A can sense electrical signals via one or more sensing vectors comprising a combination of electrodes 108A and 108B and the housing electrodes of the ICD 10C. In some cases, the ICD 10C can sense cardiac electrical signals using a sensing vector comprising one of the defibrillation electrode segments 106A and 106B and one of the sensing electrodes 108A and 108B or the housing electrodes of the ICD 10C. The sensed intrinsic electrical signals may include electrical signals generated by the myocardium and indicative of depolarization and repolarization of the heart 16C at various times during the cardiac cycle. The ICD 10C analyzes the electrical signals sensed by the one or more sensing vectors to detect tachyarrhythmias, such as ventricular tachycardia or ventricular fibrillation. In response to detecting a tachyarrhythmia, the ICD 10C can begin charging a storage element (e.g., a bank of one or more capacitors) and, while charging, deliver one or more defibrillation pulses via the defibrillation electrodes 106 of the defibrillation lead 102A if the tachyarrhythmia persists.

[0057] Medical device system 8C also includes an IMD 10D, which is implanted within heart 16C and configured to deliver cardiac pacing to the heart, such as an intracardiac pacing device (IPD). Hereinafter, IMD 10D will be referred to as IPD 10D. In the illustrated example, IPD 10D is implanted within the right ventricle of heart 16C. However, in other examples, system 8C may additionally or alternatively include one or more IPDs 10D within other chambers of heart 16C, or a similarly configured pacing device attached to an outer surface of heart 16C (e.g., in contact with the epicardium) so that the pacing device is positioned externally of heart 16C.

[0058] The IPD 10D is configured to sense the electrical activity of the heart 16C and deliver pacing therapy, such as bradycardia pacing therapy, cardiac resynchronization therapy (CRT), anti-tachycardia pacing (ATP) therapy, and / or post-shock pacing, to the heart 16C. The IPD 10D can be attached to the inner wall of the heart 16C via one or more tissue-penetrating fixation elements. These fixation elements can secure the IPD 10D to the cardiac tissue and maintain contact between the electrodes (e.g., cathode or anode) and the cardiac tissue.

[0059] The IPD 10D is capable of sensing electrical signals using electrodes carried on the housing of the IPD 10D. These electrical signals may be electrical signals generated by the myocardium and indicative of depolarization and repolarization of the heart 16C at various times during the cardiac cycle. The IPD 10D may analyze the sensed electrical signals to detect bradycardias and tachyarrhythmias, such as ventricular tachycardia or ventricular fibrillation. In response to detecting bradycardia, the IPD 10D may deliver bradycardia pacing via the electrodes of the IPD 10D. In response to detecting a tachyarrhythmia, the IPD 10D may deliver ATP therapy via the electrodes of the IPD 10D, for example, depending on the type of tachyarrhythmia. In some instances, the IPD 10D may deliver post-shock pacing in response to determining that another medical device (e.g., ICD 10C) has delivered an anti-tachyarrhythmia shock.

[0060] The IPD 10D and ICD 10C can be configured to coordinate their arrhythmia detection and treatment activities. In some instances, the IPD 10D and ICD 10C can be configured to operate completely independently of each other. In such cases, the IPD 10D and ICD 10C cannot establish a telemetry communication session with each other to exchange information about sensing and / or treatment using one-way or two-way communication. Instead, each of the IPD 10D and ICD 10C analyzes data sensed via their respective electrodes to make tachyarrhythmia detection and / or treatment decisions. As such, each device is unaware of whether the other device will detect a tachyarrhythmia, whether or when it will provide treatment, etc. In some instances, the IPD 10D can be configured to detect anti-tachyarrhythmia shocks delivered by the ICD system 100A, which can improve treatment coordination between the subcutaneous ICD 10C and IPD 10D without the need for device-to-device communication. In this way, the IPD 10D can coordinate the delivery of cardiac stimulation therapy, including termination of ATP and initiation of delivery of post-shock pacing, where an anti-tachyarrhythmia shock is applied solely by detecting the detection of a defibrillation pulse, without the need to communicate with the defibrillation device that applies the anti-tachyarrhythmia shock.

[0061] In other examples, the IPD 10D and ICD 10C can communicate to facilitate appropriate detection of arrhythmias and / or delivery of treatment. The communication can include one-way communication, wherein one device is configured to send communication messages and the other device is configured to receive those messages. Instead, the communication can include two-way communication, wherein each device is configured to transmit and receive communication messages. Two-way communication and coordination of the delivery of patient treatment between the IPD 10D and ICD 10C are described in commonly assigned U.S. Patent No. 8,744,572, entitled “SYSTEMS AND METHODS FOR LEADLESS PACING AND SHOCK THERAPY,” issued on June 3, 2014.

[0062] The external device 30C may be configured substantially similarly to the configuration described above with respect to Figure 1 External device 30A is described. External device 30C can be configured to communicate with one or both of ICD 10C and IPD 10D. In instances where external device 30C communicates with only one of ICD 10C and IPD 10D, the non-communicating device can receive instructions from or transmit data to the device communicating with external device 30C. In some instances, a user can interact with device 30C remotely via a networked computing device. A user can interact with external device 30C to communicate with IPD 10D and / or ICD 10C.

[0063] For example, a user can interact with external device 30C to send query requests and retrieve sensed physiological data or therapy delivery data stored by one or both of the ICD 10C and IPD 10D, program or update therapy parameters defining therapy, or perform any other activity related to the ICD 10C and IPD 10D. While the user may be a physician, technician, surgeon, electrophysiologist, or other healthcare professional, in some instances the user may be patient 14C. For example, external device 30C may allow the user to program any coefficients, weighting factors, or techniques used to determine a difference metric, score, and / or threshold, or other data used by the medical device system described herein to determine patient body stability based on accelerometer-generated data. As another example, external device 30C may be used to program commands or operating parameters into the ICD 10C to control its functions. External device 30C may be used to query the ICD 10C to retrieve data, including device operating data and physiological data accumulated in the IMD memory, such as data associated with patient-specific body stability associated with sit-to-stand transitions. The ICD 10C may be configured to implement various features or aspects of the present disclosure for determining patient physical stability based on accelerometer-generated data.

[0064] Medical device system 10D is an example of a medical device system that is configured to determine patient body stability based on data generated by an accelerometer. Such techniques contemplated herein may be performed individually or collectively by processing circuitry of medical device system 10D, such as processing circuitry of one or both of system 10D and external device 30C, as described below in conjunction with Figure 10 and 11 Other example medical device systems that can be configured to implement these techniques are described below.

[0065] Figure 5 FIG. 1 is a conceptual diagram illustrating another example medical device system 8D including an extracardiac ICD system 100B and an IPD 10D implanted in a patient. The medical device system 8B may be configured to perform the functions described herein. Figures 4A to 4C Any of the techniques described in medical device system 8C. Figures 4A to 4C and Figure 5 Components with similar reference numbers in the drawings may be similarly configured and provide similar functionality.

[0066] exist Figure 5 In the example of FIG, an extracardiovascular ICD system 100B includes an ICD 10C coupled to a defibrillation lead 102B. Figures 4A to 4CUnlike the defibrillation lead 102A of the ICD 10C, the defibrillation lead 102B extends subcutaneously above the thorax from the ICD 10C. In the illustrated example, the defibrillation lead 102B extends toward the center of the torso of the patient 14D, bends or rotates about the center of the torso, and extends subcutaneously above the thorax and / or sternum 110. The defibrillation lead 102B can be laterally offset to the left or right side of the sternum 110 or positioned on the sternum 110. The defibrillation lead 102B can extend substantially parallel to the sternum 102 or be angled laterally from the sternum at either a proximal or distal end.

[0067] Defibrillation lead 102B includes an insulated wire body having a proximal end including a connector 104 configured to connect to an ICD 10C and a distal portion including one or more electrodes. Defibrillation lead 102B also includes one or more conductors that form a conductive path within the lead body and interconnect the electrical connector and corresponding electrodes. In the illustrated example, defibrillation lead 102B includes a single defibrillation electrode 106 toward the distal portion of defibrillation lead 102B, such as toward the portion of defibrillation lead 102B that extends along sternum 110. Defibrillation lead 102B is positioned along sternum 110 such that a therapy carrier between defibrillation electrode 106 and a housing electrode formed by or on the ICD 10C (or other second electrode of the therapy carrier) substantially spans the ventricle of heart 16D.

[0068] The defibrillation lead 102B may also include one or more sensing electrodes, such as sensing electrodes 108A and 108B, positioned along a distal portion of the defibrillation lead 102B. Figure 5 In the illustrated example, sensing electrodes 108A and 108B are separated from each other by defibrillation electrode 106. However, in other examples, sensing electrodes 108A and 108B may both be distal to defibrillation electrode 106 or both be proximal to defibrillation electrode 106. In other examples, lead 102B may include more or fewer electrodes at various locations proximal and / or distal to defibrillation electrode 106, and lead 102B may include multiple defibrillation electrodes, for example. Figures 4A to 4C Defibrillation electrodes 106A and 106B are shown in the example.

[0069] Medical device system 8D is an example of a medical device system that is configured to determine patient body stability based on data generated by an accelerometer. Such contemplated techniques may be performed individually or collectively by processing circuitry of medical device system 8D, such as processing circuitry of one or both of system 8D and external device 30D, as discussed in further detail below.

[0070] Figure 6 1 is a conceptual diagram illustrating an example configuration of an IPD 10D. Figure 6As shown, the IPD 10D includes a housing 130, a cover 138, an electrode 140, an electrode 132, a securing mechanism 142, a flange 134, and an opening 136. The housing 130 and the cover 138 together can be considered the housing of the IPD 10D. In this manner, the housing 130 and the cover 138 can enclose and protect various electrical components within the IPD 10D, such as the circuitry. The housing 130 can enclose substantially all of the electrical components, and the cover 138 can seal the housing 130 and form an airtight, sealed housing for the IPD 10D. Although the IPD 10D is generally described as including one or more electrodes, the IPD 10D can generally include at least two electrodes (e.g., electrodes 132 and 140) to deliver electrical signals (e.g., a therapy such as cardiac pacing) and / or provide at least one sensing vector.

[0071] Electrodes 132 and 140 are carried on a housing formed by housing 130 and cover 138. In this manner, electrodes 132 and 140 can be considered as leadless electrodes. Figure 6 In the example of FIG, electrode 140 is disposed on the outer surface of cover 138. Electrode 140 can be a circular electrode positioned to contact cardiac tissue when implanted. Electrode 132 can be an annular or cylindrical electrode disposed on the outer surface of housing 130. Both housing 130 and cover 138 can be electrically insulated.

[0072] Electrode 140 can be used as the cathode and electrode 132 can be used as the anode, or vice versa, to deliver cardiac pacing, such as bradycardia pacing, CRT, ATP, or post-shock pacing. However, electrodes 132 and 140 can be used in any stimulation configuration. In addition, electrodes 132 and 140 can be used to detect intrinsic electrical signals from the myocardium.

[0073] Fixation mechanism 142 can attach IPD 10D to cardiac tissue. Fixation mechanism 142 can be an active fixation fork, a screw, a clamp, an adhesive member, or any other mechanism for attaching the device to tissue. Figure 6 As shown in the example of FIG, the fixing mechanism 142 can be composed of a memory material that maintains a preformed shape, such as a shape memory alloy (e.g., nickel titanium). During implantation, the fixing mechanism 142 can be bent forward to penetrate the tissue and allowed to bend backward toward the housing 130. In this way, the fixing mechanism 142 can be embedded in the target tissue.

[0074] A flange 144 can be provided on one end of housing 130 to enable tethering or removal of IPD 10D. For example, sutures or other devices can be inserted around flange 144 and / or through opening 146 and attached to tissue. In this manner, if securing mechanism 142 fails, flange 144 can provide a secondary attachment structure to tether or retain IPD 10D within heart 16C (or 16D). Once the IPD needs to be removed (or removed) from patient 14D, flange 144 and / or opening 146 can also be used to remove IPD 10D if deemed necessary.

[0075] Return Reference Figures 4A to 5 , medical device systems 8C and 8D are examples of medical device systems that are configured to determine patient body stability based on data generated by an accelerometer. Such techniques may be performed individually or collectively by processing circuitry of medical device system 8C or 8D, such as processing circuitry of one or more of ICD 10C, IPD 10D, and external device 30C or 30D. Although Figures 4A to 5 The example medical device systems 8C and 8D are depicted as including both an ICD 10C and an IPD 10D, but other examples may include only one of the ICD 10C or the IPD 10D, alone or in combination with other implanted or external devices.

[0076] Figure 7 1 is a functional block diagram illustrating an example configuration of an IMD 10. IMD 10 may correspond to any of ICD 10A, ICM 10B, ICD 10C, IPD 10D, or another IMD configured to implement the techniques described in the present disclosure for determining patient physical stability based on accelerometer-generated data. In the illustrated example, IMD 10 includes processing circuitry 160 and associated memory 170, sensing circuitry 162, therapy delivery circuitry 164, one or more sensors 166, and communication circuitry 168. However, ICD 10A, ICM 10B, ICD 10C, and IPD 10D need not include all of these components or may include additional components. For example, in some examples, ICM 10B may not include therapy delivery circuitry 164 (illustrated by the dotted line).

[0077] Memory 170 includes computer-readable instructions that, when executed by processing circuitry 160, cause IMD 10 and processing circuitry 160 to perform the various functions attributed herein to IMD 10 and processing circuitry 160 (e.g., calculating patient-specific body stability associated with a sit-to-stand transition from at least one of the sagittal axis signal, the longitudinal axis signal, and the transverse axis signal). Memory 170 may include any volatile, nonvolatile, magnetic, optical, or electrical medium, such as random access memory (RAM), read-only memory (ROM), nonvolatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other digital or analog medium.

[0078] Processing circuitry 160 may include fixed-function circuitry and / or programmable processing circuitry. Processing circuitry 160 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 160 may include multiple components, such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as other discrete or integrated logic circuitry. The functionality attributed herein to processing circuitry 160 may be embodied in software, firmware, hardware, or any combination thereof.

[0079] Sensing circuitry 162 and therapy delivery circuitry 164 are coupled to electrode 190 . Figure 7 The electrodes 190 shown may correspond to, for example, the ICD 10A ( Figure 1 ) of electrodes 12, 22, 24, 26, 28, 44, and 44; ICM 10B ( Figure 3 ) electrodes 64 and 66; ICD 10C ( Figures 4A to 5 ) electrodes 106, 108 and one or more housing electrodes; or IPD 10D ( Figure 6 ) electrodes 132 and 140.

[0080] Sensing circuit system 162 monitors signals from two or more selected electrodes 190 to monitor electrical activity, impedance, or other electrical phenomena of heart 26. Sensing of cardiac electrical signals can be performed to determine heart rate or heart rate variability or to detect arrhythmias (e.g., tachyarrhythmias or bradycardias) or other electrical signals. In some examples, sensing circuit system 162 can include one or more filters and amplifiers for filtering and amplifying the signals received from electrodes 190.

[0081] The resulting cardiac electrical signal can be passed to cardiac event detection circuitry, which detects a cardiac event when the cardiac electrical signal crosses a sensing threshold. The cardiac event detection circuitry can include a rectifier, a filter, and / or an amplifier, a sense amplifier, a comparator, and / or an analog-to-digital converter. Sensing circuitry 162 outputs an indication to processing circuitry 160 in response to sensing of a cardiac event (e.g., a detected P wave or R wave).

[0082] In this manner, processing circuitry 160 may receive detected cardiac event signals corresponding to the occurrence of detected R- and P-waves in the respective chambers of heart 26. Indications of detected R- and P-waves may be used to detect episodes of ventricular and / or atrial tachyarrhythmias, such as episodes of ventricular or atrial fibrillation. Some detection channels may be configured to detect cardiac events, such as P- or R-waves, and provide indications of the occurrence of such events to processing circuitry 160, for example, as described in U.S. Patent No. 5,117,824, entitled “APPARATUS FOR MONITORING ELECTRICAL PHYSIOLOGIC SIGNALS,” issued June 2, 1992.

[0083] Sensing circuit system 162 may also include a switch module to select which of the available electrodes 190 (or electrode polarity) is used to sense cardiac activity. In an example with multiple electrodes 190, processing circuit system 160 may select the electrodes to serve as sensing electrodes, i.e., select a sensing configuration, via the switch module within sensing circuit system 162. Sensing circuit system 162 may also pass one or more digitized EGM signals to processing circuit system 160 for analysis, e.g., for cardiac rhythm discrimination.

[0084] Processing circuitry 160 may implement a programmable counter. If IMD 10 is configured to generate and deliver pacing pulses to heart 26, such a counter may control basic time intervals associated with bradycardia pacing (e.g., DDD, VVI, DVI, VDD, AAI, DDI, DDDR, VVIR, DVIR, VDDR, AAIR, DDIR pacing) and other pacing modes. Intervals defined by processing circuitry 160 may include atrial and ventricular pacing escape intervals, refractory periods during which sensed P and R waves are ineffective in restarting the timing of the escape interval, and pulse widths of pacing pulses. The durations of these intervals may be determined by processing circuitry 160 in response to pacing mode parameters stored in memory 170.

[0085] The interval counter implemented by processing circuit system 160 can be reset when R waves and P waves are sensed by the detection channel of sensing circuit system 162, or when a pacing pulse is generated by therapy delivery circuit system 164, and thereby control cardiac pacing functions, including the basic timing of bradycardia pacing, CRT, ATP or post-shock pacing. When reset by the sensed R waves and P waves, the count value present in the interval counter can be used by processing circuit system 160 to measure the duration of RR intervals, PP intervals, PR intervals and RP intervals, which are measurements that can be stored in memory 170. Processing circuit system 160 can use the counts in the interval counter to detect tachyarrhythmia events, such as atrial fibrillation (AF), atrial tachycardia (AT), VF or VT. These intervals can also be used to detect overall heart rate, ventricular contraction rate and heart rate variability. A portion of memory 170 may be configured as a plurality of recirculating buffers capable of maintaining a series of measured intervals that may be analyzed by processing circuitry 160 in response to the occurrence of a pacing or sensing interruption to determine whether the patient's heart 26 is currently exhibiting an atrial or ventricular tachyarrhythmia.

[0086] In some examples, the arrhythmia detection method may include any suitable tachyarrhythmia detection algorithm. In one example, processing circuitry 160 may utilize all or a subset of the rule-based detection methods described in U.S. Pat. No. 5,545,186, issued on August 13, 1996, entitled “PRIORITIZED RULE BASED METHOD AND APPARATUS FOR DIAGNOSIS AND TREATMENT OF ARRHYTHMIAS,” or U.S. Pat. No. 5,755,736, issued on May 26, 1998, entitled “PRIORITIZED RULE BASED METHOD AND APPARATUS FOR DIAGNOSIS AND TREATMENT OF ARRHYTHMIAS.” However, in other examples, processing circuitry 160 may also employ other arrhythmia detection methods, such as those that utilize the timing and morphology of the electrocardiogram.

[0087] In some examples, processing circuitry 160 may determine that a tachyarrhythmia has occurred by identifying a shortened RR (or PP) interval length. Typically, processing circuitry 160 detects tachycardia when the interval length is less than 220 milliseconds and fibrillation when the interval length is less than 180 milliseconds. In other examples, processing circuitry 160 may detect ventricular tachycardia when the interval length is between 3 and 30 milliseconds and detect ventricular fibrillation when the interval length is less than 240 milliseconds. These interval lengths are merely examples, and the user may define the interval length as needed, which may then be stored in memory 170. As examples, it may be necessary to detect this interval length for a specific number of consecutive cycles, for a specific percentage of cycles within an operating window, or for a running average of a specific number of cardiac cycles. In other examples, additional patient parameters may be used to detect arrhythmias. For example, processing circuitry 160 may analyze one or more morphological measurements, impedance, or any other physiological measurements to determine whether patient 14 is experiencing a tachyarrhythmia.

[0088] In addition to detecting and identifying specific types of cardiac events (e.g., cardiac depolarizations), sensing circuitry 162 may also sample the detected intrinsic signals to generate an electrogram or other time-based indication of a cardiac event. Sensing circuitry 162 may include an analog-to-digital converter or other circuitry configured to sample and digitize the electrical signals sensed via electrodes 190. Processing circuitry 160 may analyze the digitized signals for various purposes, including morphological identification or confirmation of a tachyarrhythmia of heart 26. As another example, processing circuitry 160 may analyze the digitized cardiac electrogram signals to identify and measure various morphological features of the signals.

[0089] In some examples, sensing circuitry 162 is configured to sense other physiological signals of the patient. For example, sensing circuitry 162 can be configured to sense signals that vary with the changing thoracic impedance of patient 14. Thoracic impedance can vary based on the fluid volume or edema in patient 14.

[0090] Sensing circuitry 162 may sense thoracic impedance using any two or more of electrodes 190. As the fluid content of the tissue within the thoracic cavity of patient 14 changes, the impedance between the two electrodes may also change. For example, the impedance between the defibrillation coil electrodes (42, 44, 106) and the housing electrodes may be used to monitor the changing thoracic impedance.

[0091] In some examples, processing circuitry 160 measures thoracic impedance to determine a fluid index. As more fluid is retained in patient 14, e.g., as edema increases, and thoracic impedance decreases or remains relatively high, the fluid index increases. Conversely, as thoracic impedance increases or remains relatively low, the fluid index decreases. An example system for measuring thoracic impedance and determining a fluid index is described in U.S. Patent No. 8,255,046, issued on August 28, 2012, entitled “DETECTING WORSENING HEART FAILURE BASED ON IMPEDANCE MEASUREMENTS.”

[0092] Thoracic impedance may also vary as the patient breathes. In some examples, processing circuitry 160 may determine values of one or more respiration-related patient parameters based on the thoracic impedance sensed by sensing circuitry 162. Respiration-related patient parameters may include, for example, respiratory rate, depth of respiration, or the occurrence or degree of dyspnea or apnea.

[0093] The amplitude of the electrocardiogram may also vary based on patient respiration, for example, typically at a lower frequency than the cardiac cycle. In some examples, processing circuitry 160 and / or sensing circuitry 162 may filter the electrocardiogram to emphasize the respiratory component of the signal. Processing circuitry 160 may analyze the filtered electrocardiogram signal to determine the value of a respiration-related patient parameter.

[0094] exist Figure 7 In the example of , IMD 10 includes one or more sensors 166 coupled to sensing circuitry 162. Although Figure 7 10, but one or more sensors 166 may be external to IMD 10, e.g., coupled to IMD 10 via one or more wires, or configured to communicate wirelessly with IMD 10. In some examples, sensor 166 converts a signal indicative of a patient parameter, which may be amplified, filtered, or otherwise processed by sensing circuitry 162. In such examples, processing circuitry 160 determines a value of the patient parameter based on the signal. In some examples, sensor 166 determines the patient parameter values and transmits them to processing circuitry 160, e.g., via a wired or wireless connection.

[0095] In some instances, sensor 166 includes one or more accelerometers 167, such as one or more 3-axis accelerometers. Signals generated by one or more accelerometers 167, such as one or more of a sagittal axis signal, a longitudinal axis signal, and a transverse axis signal, can indicate, for example, overall body movement (e.g., activity) of patient 14, patient posture, heart sounds or other vibrations or movements associated with a heartbeat, or coughs, rales, or other respiratory abnormalities. In some instances, sensor 166 includes one or more microphones configured to detect heart sounds or respiratory abnormalities, and / or other sensors configured to detect patient activity or posture, such as a gyroscope and / or a strain gauge. In some instances, sensor 166 can include a sensor configured to convert signals indicating blood flow, blood oxygen saturation, or patient temperature, and processing circuit system 160 can determine patient parameter values based on these signals.

[0096] In some examples, sensor 166 includes one or more pressure sensors that convert one or more signals indicative of blood pressure, and processing circuitry 160 determines one or more patient parameter values based on the pressure signals. The patient parameter values determined based on the pressure may include, for example, systolic or diastolic pressure values, such as a pulmonary artery diastolic pressure value. In some examples, a separate pressure-sensing IMD 50 includes one or more sensors and sensing circuitry configured to generate pressure signals, and processing circuitry 160 determines patient parameter values related to blood pressure based on information received from IMD 50.

[0097] The therapy delivery circuitry 164 is configured to generate and deliver electrical therapy to the heart. The therapy delivery circuitry 164 may include one or more pulse generators, capacitors, and / or other components capable of generating and / or storing energy to deliver pacing therapy, defibrillation therapy, cardioversion therapy, other therapy, or a combination of therapies. In some cases, the therapy delivery circuitry 164 may include a first set of components configured to provide pacing therapy and a second set of components configured to provide anti-tachyarrhythmia shock therapy. In other cases, the therapy delivery circuitry 164 may utilize the same set of components to provide both pacing and anti-tachyarrhythmia shock therapy. In other cases, the therapy delivery circuitry 164 may share some pacing and shock therapy components, while using other components only for pacing or shock delivery.

[0098] The therapy delivery circuitry 164 may include charging circuitry, one or more charge storage devices, such as one or more capacitors, and switching circuitry to control when the capacitors are discharged to the electrodes 190 and the pulse width. Charging the capacitors to a programmed pulse amplitude and discharging the capacitors to a programmed pulse width may be performed by the therapy delivery circuitry 164 based on control signals received from the processing circuitry 160, which provides the control signals based on parameters stored in the memory 170. The processing circuitry 160 controls the therapy delivery circuitry 164 to deliver the generated therapy to the heart via one or more combinations of electrodes 190, for example, based on the parameters stored in the memory 170. The therapy delivery circuitry 164 may include switching circuitry to select which of the available electrodes 190 is used to deliver therapy, for example, as controlled by the processing circuitry 160.

[0099] In some examples, processing circuitry 160 monitors sit-to-stand transitions and determines a corresponding body stability score for each sit-to-stand transition. Details on how to determine that a sit-to-stand transition has occurred based on accelerometer signals can be found in commonly assigned U.S. Patent Application No. 15 / 607,945, filed on May 25, 2017, entitled “ACCELEROMETER SIGNAL CHANGE AS A MEASURE OF PATIENT FUNCTIONAL STATUS,” now published as U.S. Patent Application Publication No. US2018 / 0035924 A1 and claiming the benefit of Provisional Application No. 62 / 370,138, filed on August 2, 2016. The determined body stability score 174 can be stored in memory 170.

[0100] In some examples, processing circuitry 160 may determine a body stability score based on the length of time it takes from transitioning from sitting to standing until patient 14 takes their first step. Details on how to determine when a step has been taken can be found in commonly assigned U.S. patent application Ser. No. 15 / 603,776, filed May 24, 2017, entitled “STEP DETECTION USING ACCELEROMETER AXIS,” now published as U.S. Patent Application Publication No. US 2018 / 0035920 A1 and claiming the benefit of Provisional Application Ser. No. 62 / 370,102, filed August 2, 2016.

[0101] The measurement of the length of time it takes to transition from sitting to standing until the first step can begin at the beginning of the transition from sitting to standing, at the end of the transition from sitting to standing, or anywhere in between, such as at the peak of the signal indicating the transition from sitting to standing. The measurement of the length of time it takes to transition from sitting to standing until the first step can end at the beginning of the first step, at the end of the first step, or anywhere in between, such as at the peak of the signal indicating the first step. In this example, the body stability score can be in time units, such as seconds.

[0102] In other examples, processing circuit system 160 may determine a body stability score based on the number of peaks and / or valleys in the accelerometer signal during a predetermined time period (e.g., a number of seconds, such as 5 seconds). The predetermined time period is associated with the sit-to-stand transition and may begin at any time during the sit-to-stand transition or immediately after the sit-to-stand transition. Multiple peaks and / or valleys may indicate that patient 14 is swaying. In these examples, the body stability score may be a simple count of peaks and / or valleys in the accelerometer signal.

[0103] In other examples, processing circuitry 160 may determine a body stability score by measuring the peak-to-valley amplitude in the accelerometer signal during a sit-to-stand transition. In these examples, the body stability score may be measured in g (or gravity).

[0104] In other examples, processing circuitry 160 may determine a body stability score by measuring the time it takes for the peak-to-valley value in the accelerometer signal to reach a predetermined amplitude threshold (e.g., 0.2 g). In these examples, the body stability score may be in units of time, such as seconds or fractions thereof.

[0105] In other examples, processing circuitry 160 may determine a body stability score by measuring the slope of a sit-to-stand transition in the accelerometer signal. In these examples, the body stability score may be measured in degrees.

[0106] In some examples, processing circuitry 160 may determine a body stability score based on any combination of: 1) the length of time it takes to transition from sit-to-stand until patient 14 takes their first step; 2) the number of peaks and / or valleys in the accelerometer signal within a predetermined time period; 3) the amplitude of the peak-to-valley values in the accelerometer signal during the sit-to-stand transition; 4) the time it takes for the peak-to-valley values in the accelerometer signal to reach a predetermined amplitude threshold; and 5) the slope of the sit-to-stand transition in the accelerometer signal. Each of the measurements used to determine the body stability score may be weighted equally or unequally. Where more than one unit of measurement is used (e.g., time, counts, and g), processing circuitry may convert the units of measurement by scaling each unit of measurement and combining them into a raw score.

[0107] Communication circuitry 168 may include any suitable hardware, firmware, software, or any combination thereof, for communicating with another device, such as external device 30 or another IMD or sensor. Under the control of processing circuitry 160, communication circuitry 168 may receive downlink telemetry from external device 30 or another device and transmit uplink telemetry to external device 30 or another device with the aid of an antenna, which may be internal and / or external. In some examples, communication circuitry 168 may communicate with a local external device, and processing circuitry 160 may communicate via the local external device and a computer network, such as a Medtronic® network developed by Medtronic plc of Dublin, Ireland. Network) to communicate with networked computing devices.

[0108] A clinician or other user may retrieve data from IMD 10 using external device 30 or another local or networked computing device configured to communicate with processing circuitry 160 via communication circuitry 168. A clinician may also use external device 30 or another local or networked computing device to program parameters of IMD 10. In some examples, a clinician may select a method for quantifying the body stability score.

[0109] Figure 8 is a functional block diagram illustrating an example configuration of an external device 30 configured to communicate with one or more IMDs 10. Figure 8 In the example of , the external device 30 includes a processing circuit system 200, a memory 202, a user interface (UI) 204, and a communication circuit system 206. The external device 30 may correspond to Figure 1 、 24A through 5. External device 30 may be a dedicated hardware device having dedicated software for programming and / or interrogating IMD 10. Alternatively, external device 30 may be an off-the-shelf computing device, such as a smartphone running a mobile application that enables external device 30 to program and / or interrogate IMD 10. In some instances where the external device 30 is a smart phone, the external device 30 may include a mobile application that facilitates interaction with the IMD 10, for example, as described in commonly assigned U.S. patent application Ser. No. 15 / 607,945, filed on May 30, 2017, entitled “MOBILE APPLICATION TOPROMPT PHYSICAL ACTION TO MEASURE PHYSIOLOGIC RESPONSE IN IMPLANTABLE DEVICE,” now published as U.S. Patent Application Publication No. US 2018 / 0035956 A1 and claiming the benefit of Provisional Application No. 62 / 370,146, filed on August 2, 2016.

[0110] In some instances, the user of external device 30 can be a clinician, physician, healthcare provider, patient, family member of the patient, or friend of the patient. In some instances, the user uses external device 30 to select or program any one of the operating parameter values of IMD 10, for example, for measuring or determining the patient's physical stability based on data generated by the accelerometer. In some instances, the user uses external device 30 to receive data collected by IMD 10, such as physical stability score 174 or other operational and performance data of IMD 10. The user can also receive an alert provided by IMD 10 indicating that an acute cardiac event (e.g., ventricular tachyarrhythmia) is predicted. The user can also receive an alert that the patient may be more likely to fall or that the patient needs attention due to deterioration of the patient's physical stability. The user can interact with external device 30 via UI 204, which can include a display that presents a graphical user interface to the user, and a keypad or another mechanism (e.g., a touch-sensitive screen) for receiving input from the user. External device 30 may communicate wirelessly with IMD 10 using communication circuitry 206 , which may be configured for RF communication with communication circuitry 168 of IMD 10 .

[0111] Processing circuitry 200 may include any combination of integrated circuitry, discrete logic circuitry, analog circuitry (e.g., one or more microprocessors), digital signal processors (DSPs), application specific integrated circuits (ASICs), or field programmable gate arrays (FPGAs). In some examples, processing circuitry 200 may include multiple components, such as one or more microprocessors, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as any combination of other discrete or integrated logic circuitry and / or analog circuitry.

[0112] Memory 202 may store program instructions, which may include one or more program modules executable by processing circuitry 200. When executed by processing circuitry 200, such program instructions may cause processing circuitry 200 and external device 30 to provide the functionality attributed thereto herein. The program instructions may be embodied in software, firmware, and / or RAMware. Memory 202 may include any volatile, nonvolatile, magnetic, optical, or electrical medium, such as random access memory (RAM), read-only memory (ROM), nonvolatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other digital medium.

[0113] In some examples, processing circuitry 200 of external device 30 may be configured to provide some or all of the functionality attributed herein to processing circuitry 160 of IMD 10. For example, processing circuitry 200 may receive physiological signals generated by one or more IMDs 10 and determine body stability score 174 and / or may receive body stability score 174 from one or more IMDs 10. Processing circuitry 200 may determine body stability mean 176, body stability baseline score 178, and threshold value 180 for use in determining patient body stability based on data generated by the accelerometer in the manner described herein with respect to processing circuitry 160 of IMD 10.

[0114] Figure 9 is a functional block diagram illustrating an example system including an external computing device, such as a server 224 and one or more other computing devices 230A through 230N, coupled to IMD 10 and external device 30 via a network 222. In this example, IMD 10 can use its communication module 168 to communicate with external device 30 via a first wireless connection and with access point 220 via a second wireless connection, e.g., at different times and / or in different locations or settings. Figure 9 In the example of , access point 220 , external device 30 , server 224 , and computing devices 230A to 230N are interconnected and can communicate with one another via network 222 .

[0115] Access point 220 may comprise a device that connects to network 222 via any of a variety of connections, such as a telephone dial-up, a digital subscriber line (DSL), or a cable modem connection. In other examples, access point 220 may be coupled to network 222 via different forms of connection, including a wired connection or a wireless connection. In some examples, access point 220 may be co-located with patient 14. Access point 220 may interrogate IMD 10, for example, periodically or in response to a command from patient 14 or network 222, to retrieve physiological signals, body stability score 174, body stability mean 176, body stability baseline score 178, threshold 180, alerts for acute cardiac events, and / or other operational or patient data from IMD 10. Access point 220 may provide the retrieved data to server 224 via network 222.

[0116] In some cases, server 224 can be configured to provide a secure storage site for data that has been collected from IMD 10 and / or external device 30. In some cases, server 224 can compile the data into a web page or other document for viewing by trained professionals, such as clinicians, via computing devices 230A through 230N. Figure 9 The depicted system may be implemented using general networking technology and functionality similar to that developed by Medtronic plc of Dublin, Ireland. Network technologies and capabilities provided by the network.

[0117] In some examples, one or more of access point 220, server 224, or computing device 230 may be configured to perform, for example, processing circuitry configured to perform some or all of the techniques described herein with respect to processing circuitry 160 of IMD 10 and processing circuitry 200 of external device 30 involving determining patient body stability based on data generated by an accelerometer. Figure 9 In the example of FIG1 , server 224 includes memory 226 for storing signals or body stability scores 174 received from IMD 10 and / or external device 30, and processing circuitry 228, which may be configured to provide some or all of the functionality attributed herein to processing circuitry 160 of IMD 10 and processing circuitry 200 of external device 30. For example, processing circuitry 228 may determine body stability scores 174 and / or may receive body stability scores 174 from one or more IMDs 10. Processing circuitry 228 may determine body stability mean 176, body stability baseline score 178, and threshold value 180 for determining patient body stability based on data generated by the accelerometer, in the manner described above with respect to processing circuitry 160 of IMD 10.

[0118] As described above, a medical device system according to certain features or aspects of the present disclosure includes an accelerometer circuit system configured to generate a plurality of signals including a sagittal (frontal) axis signal, and a processing circuit system configured to calculate a patient-specific body stability score associated with a sit-to-stand transition from the sagittal axis signal. Among other things, such embodiments can provide an objective measure of change (or lack of change) in health to help guide treatment, as the patient-specific body stability score associated with a sit-to-stand transition can help determine whether health is improving, declining, or stable.

[0119] Figure 10 is a flow chart illustrating a first example of determining the patient's body stability based on data generated by an accelerometer according to the present disclosure. Figures 1 to 9 The present invention can be implemented with any of the implantable medical devices discussed, as each of these implantable medical devices is configured to include at least one accelerometer (i.e., accelerometer circuitry), and communication and processing circuitry (see Figure 7 and corresponding description) to facilitate determining the patient's body stability based on data generated by the accelerometer. This example can also be implemented by an external medical device or any implantable or external device having at least one accelerometer.

[0120] For example, reference Figure 2 The ICM 10B can monitor a sit-to-stand transition of the patient 14 (302). The ICM 10B can use an on-board accelerometer signal to determine that a sit-to-stand transition is occurring. The signal can be one or more of a sagittal axis signal, a longitudinal axis signal, and a transverse axis signal.

[0121] The ICM 10B may then determine whether a sit-to-stand transition has occurred (304). If the ICM 10B determines that a sit-to-stand transition has not occurred, the ICM 10B may continue to monitor for sit-to-stand transitions (302). If the ICM 10B determines that a sit-to-stand transition has occurred, the ICM 10B may determine whether the patient 14 was inactive for a predetermined time period prior to the sit-to-stand transition (306). The ICM 10B may make this determination based on a signal from an activity sensor. In some instances, the activity sensor is an accelerometer within the ICM 10B or any medical device that implements the techniques of the present disclosure. In some instances, the processing circuitry 160 determines a number of activity counts based on one or more accelerometer signals exceeding one or more thresholds 180, and uses the number of activity counts to determine whether the patient was inactive for the predetermined time period. The activity count used to determine whether the patient was inactive for the predetermined time period may be the total, mean, or median of the counts for the time period. In some examples, ICM 10B may determine that patient 14 has not taken a step by monitoring the accelerometer signal as described above for an indication that a step has been taken, thereby determining whether patient 14 has been in an inactive state.

[0122] If the ICM 10B does not determine that the patient 14 was inactive for at least a predetermined time period prior to the sit-to-stand transition, the ICM 10B may ignore the sit-to-stand transition and continue monitoring for another sit-to-stand transition (302). The predetermined time period may be programmed, for example, by the external device 30, or may be fixed. In some instances, the predetermined time period may be a number of minutes, such as six minutes. The ICM 10B may ignore the sit-to-stand transition shortly after the time period in which the patient 14 was active because recent activity may reduce the likelihood that the patient 14's body stability is worse than normal, or the measurement regarding the sit-to-stand transition may not be comparable to other measurements because it is inconsistent with less inactive time. By ignoring the sit-to-stand transition shortly after the time period in which the patient 14 was active, the ICM 10B may conserve battery power and may save a dataset of sit-to-stand transitions that is more indicative of measurements of body stability issues. Alternatively, the ICM 10B may not determine whether the patient 14 was inactive for the predetermined time period prior to the sit-to-stand transition, skip diamond 306 and proceed directly to the sit-to-stand transition. Figure 10 Diamond 304 proceeds to block 308 .

[0123] If patient 14 is inactive for at least a predetermined period of time prior to the sit-to-stand transition, ICM 10B may determine a body stability score (308). The body stability score may be a representation of the body stability of patient 14 during the sit-to-stand transition. In some instances, processing circuitry 160 may determine the body stability score based on the length of time it takes from the sit-to-stand transition until patient 14 takes their first step. In other instances, processing circuitry 160 may determine the body stability score based on the number of peaks and / or valleys in the accelerometer signal during a predetermined period of time (e.g., 5 seconds). In other instances, processing circuitry 160 may determine the body stability score by measuring the amplitude of the peak-to-valley values in the accelerometer signal during the sit-to-stand transition (e.g., 0.6 g). In other instances, processing circuitry 160 may determine the body stability score by measuring the time it takes for the peak-to-valley values in the accelerometer signal to reach a predetermined amplitude threshold (e.g., 0.1 g or 0.2 g). In other examples, processing circuitry 160 may determine a body stability score by measuring the slope of the accelerometer signal, as a steep slope may be more indicative of a stable body and a shallow slope may be more indicative of an unstable body. In some examples, processing circuitry 160 may determine a body stability score based on any combination of: 1) the length of time it takes for patient 14 to take their first step from a sit-to-stand transition; 2) the number of peaks and / or valleys in the accelerometer signal within a predetermined time period; 3) the amplitude of the peak-to-valley values in the accelerometer signal during the sit-to-stand transition; 4) the time it takes for the peak-to-valley values in the accelerometer signal to reach a predetermined amplitude threshold; and 5) the slope of the accelerometer signal during the sit-to-stand transition.

[0124] The ICM 10B may then store the body stability score in, for example, a body stability score 174 in the memory 170 (310). The ICM 10B may compare the determined body stability score to a body stability baseline score. The ICM 10B may calculate the body stability baseline score by determining the body stability scores over a period of time (e.g., a week) and then calculating the mean, median, or mode of the body stability scores determined over the period of time. In some instances, the ICM 10B may discard outlier scores before calculating the mean, median, or mode. For example, the ICM 10B may store the mean, median, or mode as the body stability baseline score 178 in the memory 202. Alternatively, the body stability baseline score 178 may be input into the ICM 10B by the external device 30. The body stability baseline score 178 may be fixed or may change over time. For example, the ICM 10B may or may not calculate a new body stability score 174 into the body stability baseline score 178.

[0125] The ICM 10B may then compare the body stability score to the body stability baseline score 178 (312). If the body stability score deviates from the body stability baseline score 178 by at least a predetermined amount in a manner that indicates lower body stability (e.g., taking longer to take the first step after transitioning from sitting to standing), the ICM 10B may send an alert to a recipient (e.g., a physician, clinician, healthcare worker, patient 14, a family member of patient 14, a friend of patient 14, etc.) (314). In some instances, the predetermined amount may be approximately 50% deviation from the baseline and may indicate an acute change in the body stability of patient 14. For example, if the body stability score is the time it takes for patient 14 to take their first step after transitioning from sitting to standing, and the body stability baseline score is 4 seconds, then if patient 14 now takes 6 seconds to take their first step from transitioning from sitting to standing, the ICM 10B may send an alert. In some instances, the ICM 10B may send an alert only after measuring a body stability score that deviates from the body stability baseline score 178 by at least a predetermined amount on two consecutive days.

[0126] In some examples, the ICM 10B can monitor chronic changes in body stability. For example, the ICM 10B can determine the slope of the body stability score 174 over time. If the slope of the body stability score 174 deviates significantly from zero over a longer period of time (e.g., two weeks), the ICM 10B can send an alert. In other examples, the ICM 10B can use statistical process control that uses the variability of the body stability baseline score to determine whether the current body stability score is outside of normal variability.

[0127] For example, an alert can be sent to external device 30 or computing device 230. The alert can inform the recipient that patient 14 has stability issues and is at an increased risk of falling when attempting to stand. This can indicate a deteriorating health condition, illness, or loss of lower body and / or core strength. By sending the alert, ICM 10B can facilitate human intervention to assist patient 14.

[0128] In some examples, rather than comparing the individual body stability scores 174 to the body stability baseline scores 178, the ICM 10B may calculate a mean, median, or mode body stability score and compare it to the baseline score instead of or in addition to examining each individual body stability score. In some examples, the ICM 10B may discard outlier scores before calculating the mean, median, or mode.

[0129] For simplicity, the following examples are described as using the mean of body stability. However, it should be understood that any measure of central tendency, such as the mean, median, and mode, may be used.

[0130] For example, the ICM 10B may calculate the body stability average 176 periodically (eg, once a day), or may calculate the body stability average 176 based on a request to do so received from the external device 30 . Figure 11 is a flow chart depicting an example of determining body stability based on accelerometer data according to techniques of this disclosure. These techniques can be used with Figure 10 Use a combination of technologies.

[0131] ICM 10B may check to see if it is time to check the body stability of patient 14 (320). This may be based on the expiration of a periodic time (e.g., one day) or receiving a request from external device 30 to check the body stability of patient 14. If it is not time to check the body stability of patient 14, ICM 10B may continue to monitor whether it is time to check the body stability of patient 14 (320). If it is time to check the body stability of patient 14, ICM 10B may calculate the average of the body stability scores 174 stored in memory 170 (322). In one example, ICM 10B calculates the average body stability using the body stability scores 174 stored in the last 24 hours. After calculating the average, ICM 10B may store the average in the body stability average 176 in memory 170. ICM 10B may then retain the body stability scores 174, retain the body stability scores 174 but mark them so that they are not used to calculate the body stability average 176 again, or discard the body stability scores 174.

[0132] The ICM 10B may then compare the body stability mean 176 to the body stability baseline score 178 (324). The body stability baseline score 178 may be as described with respect to Figure 10 1 and 2. If the body stability mean 176 does not deviate from the body stability baseline score 178 by a predetermined amount in a manner that indicates lower body stability (e.g., taking longer to take the first step than the baseline score), the ICM 10B may continue to monitor whether it is time to check body stability (320). In some instances, the predetermined amount may deviate from the body stability baseline score by approximately 50%. For example, if the body stability score 174 is a measurement of the time it takes for the patient 14 to take the first step after transitioning from sitting to standing, and the body stability baseline score is 4 seconds, then if the body stability mean 176 is now 6 seconds, then it is approximately 50% off and may indicate an acute change in the body stability of the patient 14.

[0133] In some examples, in addition to or instead of calculating the mean body stability score 176 and comparing the mean body stability score 176 to the body stability baseline score 178, the ICM 10B may also calculate a slope (321) of the individual body stability scores collected over a period of time (e.g., 24 hours). The ICM 10B may also store the slope of the individual body stability scores in the memory 170. If the slope deviates significantly from zero, an acute change in the body stability of the patient 14 may be indicated.

[0134] If the body stability mean 176 does deviate from the body stability baseline score 178 by a predetermined amount in a negative manner (e.g., taking 50% longer to take the first step after a transition) or if the slope of the body stability score 174 deviates significantly from zero, the ICM 10B may send an alert (326) to a physician, clinician, healthcare worker, patient 14, family member of patient 14, friend of patient 14, etc. In some examples, the ICM 10B may send an alert only after measuring the body stability mean 176 deviating from the body stability baseline score 178 by at least a predetermined amount on two consecutive days.

[0135] In some examples, the ICM 10B can monitor chronic changes in body stability. For example, the ICM 10B can determine the slope of the body stability mean 176 over time. If the slope of the body stability mean 176 deviates significantly from zero over a longer period of time (e.g., two weeks), the ICM 10B can send an alert.

[0136] For example, ICM 10B can send an alert to external device 30. This alert can alert the recipient that patient 14 has stability issues and is more likely to fall when attempting to stand. This can indicate a deteriorating health condition, illness, or a loss of lower body and / or core strength. By sending the alert, ICM 10B can facilitate human intervention to assist patient 14.

[0137] Figure 12 1 is a conceptual diagram 1100 illustrating a sagittal axis 1102, a longitudinal axis 1104, and a transverse axis 1106 in a three-dimensional coordinate system. As shown, the sagittal axis 1102 extends in an anterior-posterior direction, the longitudinal axis 1104 extends longitudinally, and the transverse axis extends side-to-side.

[0138] Figure 13 is a graph showing the motion of the accelerometer (see e.g. Figure 7Graph 1200 of a sagittal axis signal 1202, a longitudinal axis signal 1204, and a transverse axis signal 1206 generated by an element 166 of a plurality of axes. The sagittal axis signal 1202 corresponds to a trajectory or trend that exhibits the largest amplitude variation primarily on the (+) side of the y-axis (arbitrary units) spanning each of A1 to A2, B1 to B2, and C1 to C2. The longitudinal axis signal 1204 corresponds to a trajectory or trend that exhibits moderate amplitude variation on both the (+) and (-) sides of the y-axis spanning each of A1 to A2, B1 to B2, and C1 to C2. The transverse axis signal 1206 corresponds to a trajectory or trend that exhibits amplitude variation primarily on the (-) side of the y-axis spanning each of A1 to A2, B1 to B2, and C1 to C2, exhibiting a number of zero crossings less than the number of zero crossings of the longitudinal axis signal 1204.

[0139] The range of voltage changes provided within the sagittal axis signal 1202, the longitudinal axis signal 1204, and the transverse axis signal 1206 is not limited to any particular range of voltage changes, and in some instances is the voltage change of the sagittal axis signal 1202, the longitudinal axis signal 1204, and the transverse axis signal 1206 provided by an accelerometer configured to generate and provide a processed single-axis accelerometer output signal to detect a step. In various instances, instead of showing the sagittal axis signal 1202, the longitudinal axis signal 1204, and the transverse axis signal 1206 as changes in voltage relative to the longitudinal axis, these changes are scaled to represent changes in gravity measured in units of gravity, for example, gravity = 9.80991 m / s 2 , and changes in the sagittal axis signal 1202, the longitudinal axis signal 1204, and the transverse axis signal 1206 represent changes in the gravitational force applied on the corresponding axis measured in units.

[0140] Figure 14 It is depicted Figure 12 Graph 1400 of several features of the sagittal axis signal of FIG. The graph shows the start and end of the sit-to-stand transition, the peaks and valleys of the sit-to-stand transition, and the slope of the sit-to-stand transition. As discussed above, processing circuitry 160 may determine a body stability score based on the following, or any combination thereof: 1) the length of time it takes to transition from sit-to-stand until patient 14 takes their first step; 2) the number of peaks and / or valleys in the accelerometer signal within a predetermined time period; 3) the amplitude of the peak-to-valley amplitudes in the accelerometer signal during the sit-to-stand transition; 4) the time it takes for the peak-to-valley amplitudes in the accelerometer signal to reach a predetermined amplitude threshold; and 5) the slope of the sit-to-stand transition in the accelerometer signal.

[0141] Contemplated throughout are a medical device or system, method, and non-transitory computer-readable storage medium containing executable instructions for determining patient-specific body stability from accelerometer data.

[0142] For example, an implantable medical device (IMD) for determining patient-specific body stability from accelerometer data may include or comprise a communication circuit system configured to establish a communication link and transmit data between an internal corpus of the IMD and an external corpus of a computing device. Figure 9 Examples of such embodiments are discussed. The IMD may also include or comprise an accelerometer circuit system configured to generate a plurality of signals including a sagittal axis signal, a longitudinal axis signal, and a transverse axis signal. Figure 7 Examples of such embodiments are discussed above. The IMD may also include or comprise processing circuitry configured to: calculate a patient-specific body stability score associated with a sit-to-stand transition from at least one of the sagittal axis signal, the longitudinal axis signal, and the transverse axis signal; and, in response to a command, activate communication circuitry to transmit the patient-specific body stability score from the IMD to a computing device. Examples of such embodiments are discussed and illustrated above in conjunction with at least FIG. 16 .

[0143] Various aspects of these techniques may be implemented within one or more processors, including one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic circuitry, as well as any combination of such components, embodied in a programmer, such as a physician or patient programmer, an electrical stimulator, or other device. The term "processor" or "processing circuitry" may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry or any other equivalent circuitry.

[0144] In one or more instances, the functions described in this disclosure may be implemented in hardware, software, firmware, or any combination thereof. If performed in software, the functions may be stored as one or more instructions or codes on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media that form tangible, non-transitory media. Instructions may be executed by one or more processors, such as one or more DSPs, ASICs, FPGAs, general-purpose microprocessors, or other equivalent integrated or discrete logic circuit systems. Therefore, as used herein, the term "processor" may refer to any of the foregoing structures or any other structure suitable for implementing the techniques described herein.

[0145] In addition, in some aspects, the functionality described herein can be provided within dedicated hardware and / or software modules. Depicting different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be implemented by separate hardware or software components. Instead, the functionality associated with one or more modules or units can be performed by separate hardware or software components, or integrated into shared or separate hardware or software components. Likewise, the technology can be implemented entirely in one or more circuits or logic elements. The technology disclosed herein can be implemented in a variety of devices or equipment, including an IMD, an external programmer, a combination of an IMD and an external programmer, an integrated circuit (IC) or a group of ICs, and / or a discrete circuit system resident in the IMD and / or the external programmer.

[0146] Various aspects of the disclosure have been described. These and other aspects are within the scope of the following claims.

Claims

1. A device comprising: accelerometer circuitry configured to generate at least one signal; Memory; and processing circuitry coupled to the accelerometer circuitry and the memory, configured to: detecting a sit-to-stand transition of the patient based on the at least one signal; determining whether the patient was inactive for a predetermined period of time prior to the sit-to-stand transition; and If the patient has been inactive for at least the predetermined period of time prior to the sit-to-stand transition, A body stability score of the patient is then determined based on the at least one signal to indicate a likelihood that the patient may fall. 2 . The apparatus of claim 1 , wherein the processing circuitry is configured to determine the body stability score by measuring a time from the transition from sitting to the patient's first step based on the at least one signal.

3. The apparatus of claim 1 , wherein the processing circuitry is configured to determine the body stability score by measuring a number of peaks and valleys in the at least one signal during a predetermined time period associated with the sit-to-stand transition. 4 . The apparatus of claim 1 , wherein the processing circuitry is configured to determine the body stability score by measuring a peak-to-valley amplitude in the at least one signal. 5 . The apparatus of claim 1 , wherein the processing circuitry is configured to determine the body stability score by measuring the time it takes for a peak-to-valley amplitude to reach a predetermined amplitude threshold.

6. The apparatus of claim 1 , wherein the processing circuitry is further configured to continuously monitor the sit-to-stand transitions of the patient.

7. The apparatus of claim 1, wherein the processing circuit system is further configured to calculate average body stability from the determined body stability scores.

8. The apparatus of claim 7, wherein the processing circuitry is further configured to compare the average body stability score to a baseline body stability score.

9. The device according to claim 8, further comprising: a communications circuit system configured to establish a communications link with an external computing device, Wherein the processing circuit system is further configured to: send an alert from the communication circuit system to the external computing device if the difference between the average body stability score and the baseline body stability score is greater than a predetermined difference.

10. The apparatus of any of the above claims, wherein the processing circuitry is configured to determine, based on the at least one signal, whether the patient was inactive for a predetermined period of time prior to the sit-to-stand transition.

11. A method comprising: detecting a sit-to-stand transition of the patient based on at least one accelerometer signal; determining whether the patient was inactive for a predetermined period of time prior to the sit-to-stand transition; and If the patient was inactive for at least the predetermined period of time before the sitting, a body stability score of the patient is determined based on the at least one accelerometer signal to indicate a likelihood that the patient may fall.

12. The method of claim 11, wherein said determining said body stability score comprises measuring the time from said sitting to said patient taking a first step.

13. The method of claim 11, wherein the determining the body stability score comprises measuring a number of peaks and valleys in the at least one signal during a predetermined time period associated with the sit-to-stand transition.

14. The method of claim 11, wherein said determining the body stability score comprises measuring a peak-to-valley amplitude in the at least one signal.

15. A non-transitory computer-readable storage medium comprising instructions that, when executed by processing circuitry of a device, cause the device to: detecting a sit-to-stand transition of the patient based on at least one accelerometer signal; determining whether the patient was inactive for a predetermined period of time prior to the sit-to-stand transition; as well as If the patient was inactive for at least the predetermined period of time prior to the sit-to-stand transition, a body stability score for the patient is determined based on the at least one accelerometer signal to indicate a likelihood that the patient may fall.

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