Detecting implantable device rotation using respiratory signals

Abstract

A medical device having an acceleration sensor for generating acceleration data, at least two electrodes for generating electrocardiogram (ECG) data, a processor, and a memory is disclosed. A memory, which may be a non-transitory computer-readable medium, contains computer-executable instructions that, when executed by the processor, cause the processor to obtain acceleration data and ECG data from a first time range and a second time range different from the first range, generate respiration data based on the acceleration data, and transmit the respiration data to the processor. And determining that the orientation of the medical device has flipped during a second time range by comparing the respiratory data and ECG data for the first time range with the respiratory data and ECG data for the second time range.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Provisional Application No. 63 / 392,327, filed on July 26, 2022, the entire contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] Embodiments of the present disclosure relate to medical devices and systems for sensing physiological parameters. More specifically, embodiments of the present disclosure relate to determining whether the orientation of a medical device has changed. Background Art

[0004] Implantable medical device (IMD) can be configured to sense physiological parameters and / or provide treatment. The total available volume closed in the housing of IMD can be adjusted based on the consideration of patient comfort and performance. The example of IMD includes implantable cardiac monitor, implantable loop recorder and the like, which can be configured to be implanted subcutaneously in the patient's body, for monitoring one or more physiological parameters, such as, for example, the physiological parameters associated with the heart and / or lungs.

[0005] To facilitate a more comfortable and efficient experience, these devices can be designed to keep the overall size of the device as small as possible. Every year, these devices become smaller and include sensors with more and more capabilities.

[0006] Applicants have appreciated that in many cases the orientation of the sensor relative to the body can be an important input to the sensor and algorithms, and that due to their geometry, some IMDs have a relatively high probability of rotation in the body. Summary of the invention

[0007] Embodiments for detecting a change in orientation of an implantable medical device include, but are not limited to, the following exemplary embodiments.

[0008] As described herein, Example 1 is a medical device having an acceleration sensor for generating acceleration data, at least two electrodes for generating electrocardiogram (ECG) data, a processor, and a memory. The memory, which may be a non-transitory computer-readable medium, contains computer-executable instructions that, when executed by the processor, cause the processor to perform the following operations: obtain acceleration data and ECG data from a first time range and a second time range different from the first range, generate respiration data based on the acceleration data, and determine that the orientation of the medical device has flipped during the second time range by comparing the respiration data and ECG data of the first time range with the respiration data and ECG data of the second time range.

[0009] Example 2 is a medical device according to Example 1, such that determining that the medical device has been flipped over includes: identifying changes in respiratory data and ECG data from a first time range to a second time range; and based on the identified changes, determining that the medical device has been flipped over.

[0010] Example 3 is the medical device of Example 2, such that comparing the respiratory data and the ECG data comprises performing at least one of: a phase comparison analysis, a correlation analysis, a baseline comparison analysis, or a pattern recognition analysis.

[0011] Example 4 is a medical device according to any one of Examples 1 to 3, wherein the instructions are further configured to cause the processor to: in response to determining that the medical device has been flipped, update a heart sound generation algorithm, wherein the heart sound generation algorithm is configured to generate heart sound data based on acceleration data.

[0012] Example 5 is a medical device having an acceleration sensor for generating acceleration data, an impedance sensor for generating impedance data, a processor, and a memory. The memory, which may be a non-transitory computer-readable medium, contains computer-executable instructions that, when executed by the processor, cause the processor to perform the following operations: obtain acceleration data and impedance data from a first time range and a second time range different from the first range, generate respiration data based on the acceleration data, and determine that the orientation of the medical device has flipped during the second time range by comparing the respiration data and impedance data of the first time range with the respiration data and impedance data of the second time range.

[0013] Example 6 is a medical device according to Example 5, such that determining that the medical device has flipped over includes: identifying changes in respiratory data and impedance data from a first time range to a second time range; and based on the identified changes, determining that the medical device has flipped over.

[0014] Example 7 is the medical device of Example 6, wherein comparing the respiration data and the impedance data comprises performing at least one of: a phase comparison analysis, a correlation analysis, a baseline comparison analysis, or a pattern recognition analysis.

[0015] Example 8 is a medical device according to any one of Examples 5 to 7, such that the instructions are further configured to cause the processor to: in response to determining that the medical device has been flipped over, update a heart sound generation algorithm, wherein the heart sound generation algorithm is configured to generate heart sound data based on acceleration data.

[0016] Example 9 is a medical device according to any of the preceding examples, such that the instructions are further configured to cause the processor to: in response to determining that the medical device has been flipped over, perform recalibration of the medical device.

[0017] Example 10 is a medical device according to any of the preceding examples, such that the first time range and the second time range are between 5 seconds and 30 seconds.

[0018] Example 11 is a medical device according to any of the preceding examples, such that the instructions are further configured to cause the processor to: generate a warning notification to a user of the medical device in response to determining that the medical device has been flipped over.

[0019] Example 12 is a medical device according to any of the preceding examples, such that the instructions are further configured to cause the processor to: in response to determining that the medical device has flipped over, switch between a low-power communication link and a high-power communication link of the medical device.

[0020] Example 13 is a medical device according to any of the preceding examples, such that the instructions are further configured to cause the processor to: confirm that the respiration data is in a frequency range below about 1 Hz.

[0021] Example 14 is a processor-implemented method comprising: obtaining acceleration data from an acceleration sensor of a medical device during a first time range and a second time range different from the first range; obtaining (a) electrocardiogram (ECG) data from at least two electrodes of the medical device, or (b) impedance data from an impedance sensor of the medical device during the first time range and the second time range; generating respiration data based on the acceleration data; and determining that the orientation of the medical device has flipped during the second time range by comparing the respiration data and (a) ECG data or (b) impedance data of the first time range with the respiration data and (a) ECG data or (b) impedance data of the second time range.

[0022] Example 15 is a method according to Example 14, such that comparing the respiratory data and (a) ECG data or (b) impedance data includes: performing at least one of the following: phase comparison analysis, correlation analysis, baseline comparison analysis or pattern recognition analysis.

[0023] Example 16 is a system comprising a medical device having an acceleration sensor configured to generate acceleration data and at least two electrodes configured to generate electrocardiogram (ECG) data, a processor, and a memory. The memory contains computer executable instructions that, when executed by the processor, cause the processor to perform the following operations: obtain acceleration data and ECG data from a first time range and a second time range different from the first range, generate respiration data based on the acceleration data, and determine that the orientation of the medical device has flipped during the second time range by comparing the respiration data and ECG data of the first time range with the respiration data and ECG data of the second time range.

[0024] Example 17 is a system according to Example 16, such that the instructions are configured to cause the processor to determine that the medical device has flipped over by causing the processor to perform the following: identifying changes in respiratory data and ECG data from a first time range to a second time range; and based on the identified changes, determining that the medical device has flipped over.

[0025] Example 18 is a system according to Example 16, such that comparing the respiratory data and the ECG data includes performing at least one of: a phase comparison analysis, a correlation analysis, a baseline comparison analysis, or a pattern recognition analysis.

[0026] Example 19 is a system according to Example 16, such that the instructions are further configured to cause the processor to: in response to determining that the medical device has been flipped over, update a heart sound generation algorithm, wherein the heart sound generation algorithm is configured to generate heart sound data based on acceleration data.

[0027] Example 20 is a system according to Example 16, such that the instructions are further configured to cause the processor to: in response to determining that the medical device has been flipped over, perform recalibration of the medical device.

[0028] Example 21 is a system according to Example 16, such that the first time range and the second time range are between 5 seconds and 30 seconds.

[0029] Example 22 is a system according to Example 16, such that the instructions are further configured to cause the processor to: generate a warning notification to a user of the medical device in response to determining that the medical device has been flipped over.

[0030] Example 23 is a system according to Example 16, such that the instructions are further configured to cause the processor to: in response to determining that the medical device has flipped over, switch between a low-power communication link and a high-power communication link of the medical device.

[0031] Example 24 is the system of Example 16, wherein the instructions are further configured to cause the processor to: confirm that the respiration data is in a frequency range below about 1 Hz.

[0032] Example 25 is a system comprising a medical device having an acceleration sensor configured to generate acceleration data and an impedance sensor configured to generate impedance data, a processor, and a memory. The memory contains computer executable instructions that, when executed by the processor, cause the processor to perform the following operations: obtain acceleration data and impedance data from a first time range and a second time range different from the first range, generate respiration data based on the acceleration data, and determine that the orientation of the medical device has flipped during the second time range by comparing the respiration data and impedance data of the first time range with the respiration data and impedance data of the second time range.

[0033] Example 26 is a system according to Example 25, such that the instructions are configured to cause the processor to determine that the medical device has flipped over by causing the processor to perform the following: identifying changes in respiratory data and impedance data from a first time range to a second time range; and based on the identified changes, determining that the medical device has flipped over.

[0034] Example 27 is the system of Example 25, such that comparing the respiration data and the impedance data comprises performing at least one of: a phase comparison analysis, a correlation analysis, a baseline comparison analysis, or a pattern recognition analysis.

[0035] Example 28 is a system according to Example 25, such that the instructions are further configured to cause the processor to: in response to determining that the medical device has been flipped over, update a heart sound generation algorithm, wherein the heart sound generation algorithm is configured to generate heart sound data based on acceleration data.

[0036] Example 29 is a system according to Example 25, wherein the instructions are further configured to cause the processor to: in response to determining that the medical device has been flipped over, perform recalibration of the medical device.

[0037] Example 30 is a system according to Example 25, such that the first time range and the second time range are between 5 seconds and 30 seconds.

[0038] Example 31 is a system according to Example 25, such that the instructions are further configured to cause the processor to: generate a warning notification to a user of the medical device in response to determining that the medical device has been flipped over.

[0039] Example 32 is a system according to Example 25, such that the instructions are further configured to cause the processor to: in response to determining that the medical device has flipped over, switch between a low-power communication link and a high-power communication link of the medical device.

[0040] Example 33 is the system of Example 25, wherein the instructions are further configured to cause the processor to: confirm that the respiration data is in a frequency range below about 1 Hz.

[0041] Example 34 is a processor-implemented method comprising: obtaining acceleration data from an acceleration sensor of a medical device during a first time range and a second time range different from the first range; obtaining (a) electrocardiogram (ECG) data from at least two electrodes of the medical device, or (b) impedance data from an impedance sensor of the medical device during the first time range and the second time range; generating respiration data based on the acceleration data; and determining that the orientation of the medical device has flipped during the second time range by comparing the respiration data and (a) ECG data or (b) impedance data of the first time range with the respiration data and (a) ECG data or (b) impedance data of the second time range.

[0042] Example 35 is a method according to Example 34, such that comparing the respiratory data and (a) ECG data or (b) impedance data includes: performing at least one of the following: phase comparison analysis, correlation analysis, baseline comparison analysis or pattern recognition analysis.

[0043] Although multiple embodiments are disclosed, still other embodiments of the present disclosure will become apparent to those skilled in the art from the following detailed description, which shows and describes illustrative embodiments of the present disclosure. Therefore, the drawings and detailed description are to be regarded as illustrative in nature and not restrictive. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 is a schematic diagram depicting an illustrative medical system in accordance with an embodiment of the present disclosure.

[0045] Figure 2 is a block diagram depicting an illustrative computing device in accordance with an embodiment of the present disclosure.

[0046] Figure 3A and Figure 3B Different views of an implantable medical device according to an embodiment of the present disclosure are shown.

[0047] Figure 4 is a flow chart depicting an illustrative method of medical device operation according to an embodiment of the present disclosure.

[0048] Figure 5 is an example of a breathing data graph generated according to certain embodiments of the present disclosure before detecting that the device has been flipped over.

[0049] Figure 6 is an example of a breathing data graph generated according to certain embodiments of the present disclosure after detecting that the device has been flipped over.

[0050] Figure 7 is an example of the relationship between electrocardiogram (ECG) data and the respiratory waveform respiratory sinus arrhythmia (RSA).

[0051] Although the disclosed subject matter is amenable to various modifications and alternative forms, specific embodiments have been shown by way of example in the accompanying drawings and are described in detail below. However, it is not intended to limit the disclosure to the specific embodiments described. On the contrary, the disclosure is intended to cover all modifications, equivalents, and alternatives that fall within the scope of the disclosure as defined by the appended claims.

[0052] As used herein in connection with values ​​(e.g., orders of magnitude, degrees of measurement, and / or other qualitative and / or quantitative observations) used herein with respect to features (e.g., dimensions, measurements, attributes, components) and / or ranges thereof of tangible things (e.g., products, inventory) and / or intangible things (e.g., data, electronic representation of currency, accounts, information, portions of things (e.g., percentages, portions), calculations, data models, dynamic system models, algorithms, parameters) of tangible things (e.g., products, inventory) and / or intangible things (e.g., data, electronic representation of currency, accounts, information, portions of things (e.g., percentages, portions), calculations, data models, dynamic system models, algorithms, parameters) may be used interchangeably to refer to values, configurations, orientations, and / or other features that are equal to (or the same as) the stated value, configuration, orientation, and / or other features, or that are equal to (or the same as) reasonably close to the stated value, configuration, orientation, and / or other features, but that may differ by a reasonably small amount. The values, configurations, orientations, and / or other characteristics of a setting, orientation, and / or other feature differ by reasonably small amounts such as would be understood and readily determined by a person of ordinary skill in the relevant art to be due to measurement error, differences in calibration of measuring and / or manufacturing equipment; human error in reading and / or setting measurements; adjustments made in view of other measurements (e.g., measurements associated with other things) to optimize performance and / or structural parameters; specific implementation scenarios; inaccurate adjustment and / or manipulation of things, settings, and / or measurements by people, computing devices, and / or machines; system tolerances; control loops; machine learning; foreseeable changes (e.g., statistically insignificant changes, chaotic changes, system and / or model instabilities); preferences; and / or the like.

[0053] Although the term "block" can be used to represent different elements of exemplary adoption in this article, the term should not be interpreted as implying any requirements of the various blocks disclosed herein or a specific order therein or therebetween. Similarly, although the illustrative method can be represented by one or more drawings (e.g., flow charts, communication processes), the drawings should not be interpreted as implying any requirements of the various steps disclosed herein or a specific order therein or therebetween. However, some embodiments may require certain steps and / or certain orders between certain steps, as may be clearly described herein and / or as can be understood from the nature of the steps themselves (e.g., the performance of some steps may depend on the result of the previous step). In addition, a "set", "subset" or "group" of an item (e.g., input, algorithm, data value) may include one or more items, and similarly, a subset or subgroup of an item may include one or more items. "Multiple" means more than one. DETAILED DESCRIPTION

[0054] Embodiments of the present disclosure use various methods to determine an orientation change of an implantable medical device (IMD). The IMD can be configured to obtain acceleration data as well as electrocardiogram (ECG) data and / or impedance data, and analyze such data to determine whether the IMD has flipped. According to an embodiment, the IMD has flipped when its orientation relative to at least one axis (e.g., the longitudinal axis) has changed by approximately 180 degrees.

[0055] Figure 1 An illustrative medical system 100 is shown according to an embodiment of the present disclosure. Figure 1 As shown, medical system 100 includes an IMD 102 configured to be implanted in a subject 104, and an external monitoring device (EMD) 106 communicatively coupled to IMD 102 via a communication link 108. In the illustrated embodiment, medical system 100 is operably coupled to subject 104, and IMD 102 and EMD 106 are configured to communicate with each other via communication link 108.

[0056] IMD and EMD

[0057] Depending on the embodiment, IMD 102 may be a control device, a monitoring device, a pacemaker, an implantable cardioverter defibrillator (ICD), a cardiac resynchronization therapy (CRT) device, and / or the like, and may be an implantable medical device known in the art or later developed for providing therapeutic and / or diagnostic data about subject 104 and / or IMD 102. In various embodiments, IMD 102 may include defibrillation and pacing / CRT capabilities (e.g., a CRT-D device).

[0058] In an embodiment, the IMD 102 may be implanted subcutaneously in an implantation location or pocket in the patient's chest or abdomen and may be configured to monitor (e.g., sense and / or record) physiological parameters associated with the patient's heart. In an embodiment, the IMD 102 may be an implantable cardiac monitor (ICM) (e.g., an implantable diagnostic monitor (IDM), an implantable loop recorder (ILR)) configured to record physiological parameters such as, for example, one or more cardiac electrical signals, heart sounds, heart rate, blood pressure measurements, oxygen saturation, and / or the like.

[0059] In embodiments, IMD 102 may be configured to detect various physiological signals that may be used in conjunction with various diagnostic, therapeutic, and / or monitoring implementations. For example, IMD 102 may include sensors or circuits for detecting respiratory system signals, cardiac system signals, heart sounds, and / or signals associated with patient activity. In embodiments, IMD 102 may be configured to sense intrathoracic impedance, from which various respiratory parameters may be derived, including, for example, respiratory tidal volume and minute ventilation.

[0060] In an embodiment, a sensor and associated circuitry may be incorporated into the IMD 102 for detecting one or more body movement or body posture, body orientation, and / or body position related signals. For example, an accelerometer and / or a GPS device may be used to detect patient activity, patient position, body orientation, and / or torso position. According to an embodiment, for example, the IMD 102 may include an acceleration sensor 110 configured to generate an acceleration signal and / or acceleration data, which may include an acceleration signal, information derived from the acceleration signal, and / or the like. In an embodiment, the acceleration data includes acceleration measurements associated with movement of the IMD 102. In an embodiment, the acceleration sensor may be or include any acceleration sensor capable of generating measurements associated with its movement. As used herein, an "acceleration sensor" or "accelerometer" may be or include any type of accelerometer, gyroscope, magnetometer, inertial measurement unit (IMU), and / or any other type of sensor or sensor combination configured to measure changes in acceleration, angular velocity, and / or the like. According to an embodiment, acceleration data may be used to determine that IMD 102 has flipped over.

[0061] Derived parameters may also be monitored using IMD 102. For example, a sleep sensor may rely on measurements made by an implanted accelerometer that measures physical activity levels. The sleep sensor may estimate sleep patterns based on measured activity levels. Other derived parameters include, but are not limited to, functional capacity indices, autonomic tone indices, sleep quality indices, cough indices, anxiety indices, and cardiovascular health indices for calculation of quality of life indices that quantify the overall health and well-being of the subject.

[0062] In various embodiments, the EMD 106 can be a device configured to be portable to the subject 104, for example, by being integrated into a vest, a belt, a strap, a sticker; placed in a pocket, a purse, or a backpack; carried in the subject's hand; and / or the like, or otherwise operably (and / or physically) coupled to the subject 104. The EMD 106 can be configured to monitor (e.g., sense and / or record) physiological parameters associated with the subject 104 and / or provide therapy to the subject 104. For example, the EMD 106 can be or include a wearable cardiac defibrillator (WCD), such as a vest including one or more defibrillation electrodes. In embodiments, the EMD 106 can include any number of different therapeutic components such as, for example, a defibrillation component, a drug delivery component, a neurostimulation component, a neuromodulation component, a temperature regulation component, and / or the like. In embodiments, EMD 106 may include limited functionality, such as defibrillation shock delivery and communication capabilities, with arrhythmia detection, classification, and / or therapy command / control being performed by a separate device such as, for example, IMD 102 .

[0063] In an embodiment, the communication link 108 may be or include a wireless communication link, such as, for example, a short-range radio link, such as Bluetooth, IEEE 802.11, a proprietary wireless protocol, and / or the like. In an embodiment, for example, the communication link 108 may utilize a Bluetooth low energy radio or a similar protocol, and may utilize an operating frequency within the range of 2.40 to 2.48 GHz. The term "communication link" may refer to the ability to transmit a certain type of information in at least one direction between at least two devices, and should not be construed as being limited to a direct, persistent, or otherwise limited communication channel. That is, according to an embodiment, the communication link 108 may be a persistent communication link, an intermittent communication link, an ad-hoc communication link, and / or the like. The communication link 108 may refer to a direct communication between an IMD 102 and an EMD 106, and / or an indirect communication that travels between an IMD 102 and an EMD 106 via at least one other device (e.g., a repeater, a router, a hub). Communication link 108 may facilitate one-way and / or two-way communication between IMD 102 and EMD 106. Data and / or control signals may be transmitted between IMD 102 and EMD 106 to coordinate the functions of IMD 102 and / or EMD 106. In an embodiment, patient data may be downloaded periodically or on command from one or more of IMD 102 and EMD 106. A physician and / or patient may communicate with IMD 102 and EMD 106, for example, to obtain patient data or to initiate, terminate, and / or modify a record and / or treatment.

[0064] In an embodiment, MD 102 and / or EMD 106 can provide one or more of the following functions about a patient: sensing, data analysis, and treatment. For example, in an embodiment, IMD 102 and / or EMD 106 can be used to measure any number of various physiological, equipment, subjective, and / or environmental parameters associated with subject 104 using electrical, mechanical, and / or chemical devices. IMD 102 and / or EMD 106 can be configured to automatically collect data, collect data upon request (e.g., input provided by a subject, a clinician, another device), and / or any number of various combinations and / or modifications thereof. IMD 102 and / or EMD 106 can be configured to store data related to physiological, equipment, environmental, and / or subjective parameters, and / or transmit data to any number of other devices in system 100. In an embodiment, IMD 102 and / or EMD 106 can be configured to analyze data and / or act according to the analyzed data. For example, IMD 102 and / or EMD 106 may be configured to modify therapy, perform additional monitoring, and / or provide alarm indications based on analysis of the data.

[0065] In embodiments, IMD 102 and / or EMD 106 may be configured to provide therapy. Therapy may be provided automatically and / or upon request (e.g., by input from subject 104, a clinician, another device or process). IMD 102 and / or EMD 106 may be programmable in that various characteristics of their sensing, therapy (e.g., duration and interval), and / or communication may be changed through communication between devices 102 and 106 and / or other components of system 100.

[0066] Figure 1 The illustrative cardiac monitoring system 100 shown is not intended to suggest any limitation as to the scope of use or functionality of the embodiments of the present disclosure. The illustrative cardiac monitoring system 100 should not be interpreted as having any dependency or requirement relating to any single component or combination of components shown therein. Additionally, Figure 1 The various components depicted may, in embodiments, be integrated with various of the other components depicted therein (and / or components not shown), all of which are considered within the scope of the subject matter disclosed herein.

[0067] Figure 1 The various components depicted in the can operate together to form a monitoring system 100, which can be, for example, a computerized patient management and monitoring system. In embodiments, the system 100 can be designed to help monitor a subject's condition, manage a subject's treatment, and / or the like.

[0068] Any number of components of system 100 may be implemented using one or more computing devices. That is, for example, IMD 102 and / or EMD 106 may be implemented on one or more computing devices. Figure 2 2 is a block diagram depicting an illustrative computing device 200 according to an embodiment of the present disclosure. Computing device 200 may include any type of computing device suitable for implementing aspects of embodiments of the disclosed subject matter. Examples of computing devices include special-purpose computing devices or general-purpose computing devices, such as "workstations," "servers," "laptops," "desktops," "tablet computers," "handheld devices," "smartphones," "general-purpose graphics processing units (GPGPUs)," and the like, all of which are contemplated herein. Figure 1 and Figure 2 within the scope of (with reference to various components of system 100 and / or computing device 200).

[0069] In an embodiment, computing device 200 includes bus 210, which directly and / or indirectly couples one or more of the following devices: processor 220, memory 230, input / output (I / O) port 240, I / O component 250, one or more sensors 260 (e.g., Figure 1 1 and any other suitable sensor for performing the necessary measurements as disclosed herein, such as an impedance sensor, an optical sensor, or a sensor associated with an electrode) and a power source 270. Any number of additional components, different components, and / or combinations of components may also be included in the computing device 200. The I / O components 250 may include: presentation components configured to present information to a user, such as, for example, a display device, a speaker, a printing device, and / or the like; and / or input components, such as, for example, a microphone, a joystick, a satellite dish, a scanner, a printer, a wireless device, a keyboard, a pen, a voice input device, a touch input device, a touch screen device, an interactive display device, a mouse, and / or the like.

[0070] Bus 210 represents what may be one or more buses (such as, for example, an address bus, a data bus, or a combination thereof). Similarly, in an embodiment, computing device 200 may include multiple processors 220, multiple memory components 230, multiple I / O ports 240, multiple I / O components 250, multiple sensors 260 (e.g., accelerometers, electrodes, impedance sensors, etc.), and / or multiple power supplies 270. In addition, any number or combination of these components may be distributed and / or replicated across multiple computing devices.

[0071] In an embodiment, the memory 230 includes computer readable media in the form of volatile and / or non-volatile memory and may be removable, non-removable, or a combination thereof. Examples of media include Random Access Memory (RAM); Read Only Memory (ROM); Electrically Erasable Programmable Read Only Memory (EEPROM); Flash memory; Optical or holographic media; cassettes, tapes, disk storage or other magnetic storage devices; Data transmission; and / or any other medium that can be used to store information and can be accessed by a computing device (such as, for example, quantum state memory and / or the like). In an embodiment, the memory 230 is a non-transitory computer readable medium that stores computer executable instructions 280 for causing the processor 220 to implement aspects of embodiments of the system components discussed herein and / or perform aspects of embodiments of the methods and processes discussed herein.

[0072] Computer executable instructions 280 may include, for example, computer code, machine usable instructions, and the like, such as, for example, program components that are executable by one or more processors 220 associated with computing device 200. Program components may be programmed using any number of different programming environments, including various languages, development toolkits, frameworks, and / or the like. Some or all of the functionality contemplated herein may also or alternatively be implemented in hardware and / or firmware.

[0073] According to an embodiment, for example, the instructions 280 may be configured to be executed by the processor 220, and when executed, cause the processor 220 to perform certain processes. In some embodiments, the processor 220, the memory 230, and the instructions 280 are part of a controller (such as an application specific integrated circuit (ASIC), a field-programmable gate array (FPGA), and / or the like). Such a device may be used to perform the functions and steps described below.

[0074] Determine changes in the orientation of the IMD

[0075]

[0046] Embodiments of the present disclosure are directed to various methods for determining when IMD 102 has flipped while implanted in a patient's body. Figure 3A and Figure 3B An example IMD 102 is shown. Figure 3A and Figure 3BAs shown, IMD 102 includes a body 300 that defines an exterior shape of IMD 102 . Figure 3A and Figure 3B The IMD 102 in FIG. 1 extends longitudinally along a Z-axis 302, which extends through the Figure 3A and enter and leave the page Figure 3B . IMD 102 has a height along Y axis 304. IMD 102 has multiple exterior surfaces or sides. For example, Figure 3B Reference numerals are included for two of the sides of IMD 102. One side is indicated by reference numeral 306, and the opposite side is indicated by reference numeral 308.

[0076] In an embodiment, when IMD 102 has been flipped over, sides 306 and 308 have swapped positions, as shown in FIG. Figure 3B 102. Electrode 310 can be implemented at any suitable location inside IMD 102 or on the surface of IMD 102, as known in the art (only one electrode is shown in the figure for simplicity). For example, two or more electrodes 310 are implemented in IMD 102 to measure biopotential information, such as ECG data. Impedance sensor 312 can also be implemented in any suitable location inside IMD 102 as shown in the figure to perform impedance measurement. For example, when IMD 102 has been rotated about 180° around Z axis 302, IMD 102 can be considered to have flipped. In some embodiments, when the IMD has flipped along any of the aforementioned axes, the coordinates of the points on the surface of the IMD (or in some examples, inside the IMD) change accordingly. For example, when the IMD flips along the X axis, the Y component and Z component of the coordinate change, such as by changing the sign in each component (from positive to negative, or vice versa). This characteristic of the change of sign can be used to effectively determine or confirm that the IMD has flipped, as further explained herein. When the IMD is flipped while implanted in a patient, the orientation change can affect various aspects of the IMD. For example, as will be described in more detail below, flipping can affect one or more of the following: posture algorithms / determinations, heart sound detection, impedance measurements, optical measurements, and temperature measurements.

[0077] like Figure 4As shown, the process 400 according to some embodiments may include, in step 402, causing the processor to obtain acceleration data (including multiple acceleration measurements of IMD 102) from an acceleration sensor during a first time range and a second time range different from the first range. In addition, in some examples, at least two electrodes can be implemented to facilitate measurements for generating ECG data during the first time range and the second time range. In addition or alternatively, in some examples, an impedance sensor can be implemented to facilitate obtaining impedance measurement data during the first time range and the second time range.

[0078] The processor, in step 404, compares the acceleration data and the ECG and / or impedance data obtained during the first time range with the acceleration data and the ECG and / or impedance data obtained during the second time range. In some examples, respiration data can be extracted or determined from the acceleration data, and respiration data in different time ranges (along with ECG / impedance data acquired during the same range) can be compared to each other.

[0079] Based on the comparison result, the processor, in step 406, determines that the IMD has flipped between the first time range and the second time range. In some examples, determining that the IMD has flipped causes the processor to perform one or more of the following in step 408: applying corrections to the output of the monitoring process, such as updating the heart sound generation algorithm (which is used to generate heart sound data based on acceleration data obtained using an accelerometer) and / or performing recalibration of the IMD; generating an alert notification to a user (e.g., a patient, a physician, a caregiver using the IMD); and / or switching between a low-power communication link and a high-power communication link (e.g., which can be Bluetooth). For example, a low-power communication link can be used when the IMD is oriented so that its antenna requires less power to successfully transmit and receive wireless signals. This orientation can be when the antenna is facing or otherwise closer to the patient's skin on their chest compared to the opposite flip orientation. A high-power communication link can be used when the antenna is oriented so that the signal must pass through more tissue, etc. compared to the opposite flip orientation.

[0080] Figure 5 An exemplary respiration data graph is shown in a first time range before a device flip is detected. The respiration data graph compares an acceleration waveform 500 (solid line) with an impedance waveform 502 (dashed line) as generated according to the examples disclosed herein. The acceleration waveform 500 is representative of a respiration waveform because the acceleration waveform 500 generally forms a waveform curve having multiple peaks and minimums, each of which occurs according to an apparent phase as determined. Similarly, the impedance waveform 502 also follows the same or similar time length as the acceleration waveform 500 in each apparent phase, but the impedance waveform 502 is in antiphase with the acceleration waveform 500. For example, Figure 5 Each minimum point in the acceleration waveform 500 that occurs throughout the measurement is shown with broken vertical lines, and each vertical line coincides with a peak of the impedance waveform 502 .

[0081] Figure 6 Shown in Figure 5 An exemplary respiration data graph after a device flip is detected in a second time range following the first time range shown. The acceleration waveform 500 now has the same polarity as the impedance waveform 502, such that each minimum point in the acceleration waveform 500 generally coincides with a minimum point in the impedance waveform 502, as shown using vertical broken lines. This is caused by the acceleration waveform 500 reversing its polarity while the impedance waveform 502 remains constant, because flipping does not affect the impedance measurement.

[0082] In view of the above, a rollover of the device can be determined by detecting the change in the apparent phase of these waveforms relative to each other during the two different time frames in which they are measured. Figure 5 and Figure 6 In the example shown, by comparing the two graphs generated, the device is detected as measuring the Figure 5 After the data in the figure, but after the measurement used to generate Figure 6 The data in the graph has been flipped before.

[0083] Figure 7 The relationship between ECG data and a respiration waveform using respiratory sinus arrhythmia (RSA) is shown, RSA is a heart rate variability that is synchronized with respiration, by which the RR interval on the ECG (i.e., the time elapsed between two consecutive R waves of the QRS signal) shortens during inspiration and lengthens during expiration. The ECG data 700 is compared with a respiration waveform 702 associated with the ECG data 700, and it can be seen that an increase in respiration (e.g., during inspiration or when the patient inhales) corresponds to an increase in pulse frequency in the ECG data 700, while a decrease in respiration (e.g., during expiration or when the patient exhales) corresponds to a decrease in pulse frequency in the ECG data 700. Therefore, the region of more frequency (shown via box "A") can be used to estimate the peak of respiration (i.e., when the lungs are filled the most), while the region of less frequency (shown via box "B") can be used to estimate the nadir of respiration (i.e., when the lungs are filled the least).

[0084] In this example, when the device is turned over, the respiration waveform 702 flips or reverses in direction (because the respiration waveform 702 is based on acceleration measurements, which change polarity when the device is turned over), while the ECG data 700 remains unchanged. Therefore, when the processor detects that the respiration nadir according to the respiration waveform 702 coincides with the more frequency region according to the ECG data 700 (box "A"), the processor can determine that the device has been turned over.

[0085] According to some examples, the comparison (whether between acceleration data and impedance data, or between ECG data and respiratory data generated from the acceleration data) may involve determining two separate time ranges or spans, a first range and a second range, which follow the first range in chronological order. The first time range and the second time range may or may not be immediately adjacent to each other. In some examples, there may be a time span between the two ranges. Once the two ranges are determined, the data collected during the first range and the second range are compared to each other, and when there is an observable directional difference in the details (e.g., there is a change such as a reverse phase in the impedance data measurement), the processor determines that the IMD has flipped between the first time range and the second time range.

[0086] In some examples, the specific time span of the first time range and the second time range can be determined by the frequency at which the processor receives data from the IMD in question. For example, if the IMD transmits acceleration data and impedance data only during a predetermined time interval (e.g., 5 seconds, 10 seconds, 15 seconds, 20 seconds, 25 seconds, 30 seconds, 45 seconds, 1 minute, 2 minutes, 3 minutes, 5 minutes, 10 minutes, or any other suitable time or time range therebetween), and the measurement of such data is performed on a regular or semi-regular basis, such as once a day, 5 times a day, 10 times a day, 20 times a day, or any other suitable frequency therebetween. With a greater frequency, the detection of such a flip can be recorded, and appropriate actions can be taken more immediately. In some examples, the processor confirms that the respiratory data generated based on the acceleration data is correct by determining the frequency range of the generated respiratory data. If the frequency range is within a predetermined threshold, such as at or less than about 1 Hz, at or less than 0.5 Hz, or any other suitable value or range therebetween, the processor continues to perform the analysis as disclosed herein. Otherwise, the processor may measure again.

[0087] In some examples, comparing involves the processor performing at least one of: a phase comparison analysis, a correlation analysis, a baseline comparison analysis, or a pattern recognition analysis. It should be understood that any other type of comparison analysis may be performed using the same acquired data, as known in the art.

[0088] In the phase comparison analysis, the processor determines the apparent phase of the respiratory data from the data generated in the first time frame and the second time frame, and then compares the determined phases to calculate a difference between the apparent phases. If the calculated difference indicates an apparent phase shift of about 180° in the respiratory data or is greater than a predetermined threshold, the processor determines that the IMD has flipped. In some embodiments, the phase comparison analysis is performed by comparing the phase shift of the respiratory data over time.

[0089] In the correlation analysis, the processor determines a correlation between the respiratory data during the first time frame and the second time frame, for example by generating a correlation graph using the data from the two time frames. In some examples, if the correlation between the data acquired during the two time frames is approximately -1, a negative relationship is indicated (i.e., a higher value measured in the first time frame corresponds to a lower value measured in the second time frame, or vice versa), and the processor determines that the IMD has flipped.

[0090] In a baseline comparison analysis, a baseline marker (or point assumed to be a fixed basis of comparison) is determined in the data obtained during two time frames. The baseline marker may include a peak and / or a nadir of the waveform of the respiratory data. The baseline from the first time frame is then compared to the baseline from the second time frame, and if there is an observable difference, such as a difference in the baseline values ​​exceeding a predetermined threshold, the processor determines that the IMD has flipped.

[0091] In the pattern recognition analysis, the processor determines a pattern in the data obtained during the first time frame and another pattern in the data obtained during the second time frame. For example, the pattern can be determined using any suitable method (such as a pattern recognition algorithm as known in the art), and the two patterns are then compared to each other. If the patterns are at least substantially mirrored or inverted relative to each other, for example, one pattern has positive directivity and the other pattern has negative directivity in the same corresponding portion of each pattern, or vice versa, the processor determines that the IMD has flipped.

[0092] In some examples, the processor can obtain additional data to assist in determining that the IMD has flipped, or once a determination is made, use the additional data as confirmation that the medical device has flipped. For example, the additional data can include at least one of: magnetometer measurement data, impedance measurement data, optical sensor measurement data, or temperature measurement sensor data. These data and other suitable data (such as Bluetooth signal strength measurements) can be used by the processor to confirm that the IMD has flipped by comparing the additional data obtained during the first time range with the additional data obtained during the second time range.

[0093] The measurement data from the magnetometer can be used to provide additional orientation data to corroborate the acceleration data measured by the accelerometer. Additionally or alternatively, the magnetometer can determine the user's orientation by measuring the direction, strength, or relative change of the magnetic field at a specific location, so that when the IMD is flipped, the direction, strength, or relative change of the measured magnetic field at the same location can be changed. When this change exceeds a predetermined threshold, the processor can use this information to confirm that the IMD has been flipped.

[0094] Impedance measurement data, such as those obtained via impedance electrodes mounted on an IMD, can also be used to detect impedance measurements obtained from tissue surrounding a medical device. If the IMD flips, the impedance vector will change, causing the signal amplitude to jump, which may result in impaired impedance sensing. When the IMD flips, the impedance of the tissue or tissue path surrounding the IMD, or more specifically, the impedance of the tissue or tissue path in direct or indirect contact with the impedance electrodes mounted on the medical device may increase (e.g., the tissue between the impedance electrodes is fat tissue) or decrease (e.g., the structure between the electrodes is muscle tissue). When the change in impedance exceeds a threshold, the processor can use this information to confirm that the IMD has flipped.

[0095] Measurement data from an optical sensor mounted on the IMD can be used to determine conditions surrounding the IMD. Detecting flipping is also relevant for optical sensors because flipping may affect the accuracy of such sensors. For example, the intensity (or brightness) of ambient light and / or background radiation detected by the optical sensor can be used as a factor in determining the direction the optical sensor is facing; typically, in some examples, an implanted optical sensor detects a greater intensity of light or background radiation when facing forward or toward the chest than when facing backward or toward the back or spine. Therefore, when the brightness or background radiation increases or decreases beyond a predetermined threshold, the processor can use this information to confirm that the IMD has flipped because the optical sensor is now facing a different direction than when it was previously measured.

[0096] Temperature measurements, such as obtained from a thermometer, can also help determine that the IMD has flipped over. For example, when a thermometer mounted on the IMD is flipped over, there may be a temperature change because the thermometer may be closer to or farther away from organs that generate body heat, including but not limited to the heart. In addition, body heat may also be generated by the contraction of skeletal muscles, so depending on where the IMD is implanted, the IMD may also detect an increase in temperature when the thermometer is pointed at such a heat source. In any case, when the detected temperature change exceeds a predetermined threshold, the processor can use this information to confirm that the IMD has flipped over.

[0097] After the processor has determined that the IMD has flipped, there are multiple actions that the processor can choose to implement. Thus, in some embodiments, any number of different monitoring processes can be corrected to account for the fact that the orientation has changed from its assumed (or previously determined) orientation. Applying corrections to the outputs of the monitoring processes can be achieved by directly modifying the outputs and / or by recalibrating the monitoring processes to account for different (e.g., flipped) orientations of the IMD. In some embodiments, the monitoring processes can include, for example, a posture algorithm (e.g., an algorithm configured to monitor sleep tilt or orientation), a heart sound algorithm, and / or an impedance sensing process.

[0098] In some examples, the processor also applies a tag to the generated data to mark when the device is flipped so that it can be referenced later by a physician, for example, or alternatively by researchers and developers who wish to improve the IMD to reduce such flipping. In some examples, flipping causes the processor to switch between low-power and high-power Bluetooth connection links so that the appropriate power setting is used based on the current position of the IMD, which may be beneficial by, for example, reducing power when the IMD is facing forward or toward the chest to save battery power. In some examples, the processor can transmit an alert notification, such as an alarm, an email or text message, an automated phone call, etc., to notify a user (who may be a patient or a physician, a physician, or a caregiver responsible for monitoring the patient's health).

[0099] The advantages of implementing the aforementioned flip detection method include the increased accuracy of the determination that the IMD has flipped. By comparing the directionality of the generated respiratory data or acceleration data with the ECG data and / or impedance data, the processor can more accurately determine the occurrence of flipping. And when it does so, the processor can take appropriate actions to alert the user or apply corrections to the output of the monitoring process to improve its accuracy. As the complexity of the IMD increases (and the overall size decreases), it is important to accurately determine this change in orientation. In addition, for example, the determination of whether the IMD has flipped can be performed in a shorter period of time, such as less than a day, less than half a day, less than a few hours, less than an hour, or even less than half an hour, because the method disclosed herein requires fewer samples of respiratory data, acceleration data, ECG data and / or impedance data (as the case may be) in a shorter time frame to perform such determination, such as compared with other methods in the art, which may require at least a full day's worth of data samples to perform calculations with them in order to perform such determination.

[0100] The aforementioned methods may be applicable to any two or more signals that are expected to change polarity relative to each other when the device is flipped. In some examples, as mentioned above, respiration data combined with RSA may be implemented as part of an atrial fibrillation detection algorithm. Detecting flipping is relevant for optical sensors because flipping may affect the accuracy of the sensor, so implementing the aforementioned methods may improve the accuracy of optical sensors.

[0101] Various modifications and additions may be made to the exemplary embodiments discussed without departing from the scope of the disclosed subject matter. For example, while the above-described embodiments refer to specific features, the scope of the present disclosure also includes embodiments having different combinations of features and embodiments that do not include all of the described features. Therefore, the scope of the disclosed subject matter is intended to include all such substitutions, modifications, and variations as fall within the scope of the claims, together with all equivalents thereof.

Claims

1. A medical device comprising: an acceleration sensor configured to generate acceleration data; at least two electrodes configured to generate electrocardiogram (ECG) data; processor; as well as a memory containing computer executable instructions which, when executed by the processor, cause the processor to: obtaining the acceleration data and the ECG data from a first time range and a second time range different from the first range, generating respiration data based on the acceleration data, and By comparing the respiration data and the ECG data of the first time frame with the respiration data and the ECG data of the second time frame, it is determined that the orientation of the medical device has flipped during the second time frame.

2. The medical device according to claim 1, wherein: Determining that the medical device has been flipped over includes: identifying a change from the respiratory data and ECG data of the first time range to the second time range; and Based on the identified change, it is determined that the medical device has been flipped over.

3. The medical device according to claim 2, wherein: Comparing the respiratory data and the ECG data includes performing at least one of: a phase comparison analysis, a correlation analysis, a baseline comparison analysis, or a pattern recognition analysis.

4. The medical device according to any one of claims 1 to 3, wherein: The instructions are further configured to cause the processor to: In response to determining that the medical device has been flipped over, a heart sound generation algorithm is updated, wherein the heart sound generation algorithm is configured to generate heart sound data based on the acceleration data.

5. A medical device comprising: an acceleration sensor configured to generate acceleration data; an impedance sensor configured to generate impedance data; processor; as well as a memory containing computer executable instructions which, when executed by the processor, cause the processor to: obtaining the acceleration data and the impedance data from a first time range and a second time range different from the first range, generating respiration data based on the acceleration data, and By comparing the respiration data and impedance data of the first time frame with the respiration data and impedance data of the second time frame, it is determined that the medical device has flipped orientation during the second time frame.

6. The medical device according to claim 5, wherein: Determining that the medical device has been flipped over includes: identifying a change from the respiration data and impedance data of the first time range to the second time range; and Based on the identified change, it is determined that the medical device has been flipped over.

7. The medical device according to claim 6, wherein: Comparing the respiration data and the impedance data includes performing at least one of: a phase comparison analysis, a correlation analysis, a baseline comparison analysis, or a pattern recognition analysis.

8. The medical device according to any one of claims 5 to 7, wherein: The instructions are further configured to cause the processor to: In response to determining that the medical device has been flipped over, a heart sound generation algorithm is updated, wherein the heart sound generation algorithm is configured to generate heart sound data based on the acceleration data.

9. A medical device according to any preceding claim, wherein: The instructions are further configured to cause the processor to: In response to determining that the medical device has been flipped over, recalibration of the medical device is performed.

10. A medical device according to any preceding claim, wherein: The first time range and the second time range are between 5 seconds and 30 seconds.

11. A medical device according to any preceding claim, wherein: The instructions are further configured to cause the processor to: In response to determining that the medical device has been flipped over, an alert notification is generated to a user of the medical device.

12. A medical device according to any preceding claim, wherein: The instructions are further configured to cause the processor to: In response to determining that the medical device has been flipped over, switching between a low power communication link and a high power communication link of the medical device.

13. A medical device according to any preceding claim, wherein: The instructions are further configured to cause the processor to: confirm that the respiration data is in a frequency range below approximately 1 Hz.

14. A computer processor implemented method comprising: obtaining acceleration data from an acceleration sensor of the medical device during a first time range and a second time range different from the first range; obtaining (a) electrocardiogram (ECG) data from at least two electrodes of the medical device, or (b) impedance data from an impedance sensor of the medical device during the first time frame and the second time frame; generating respiration data based on the acceleration data; and By comparing the respiration data and (a) ECG data or (b) impedance data of the first time range with the respiration data and (a) ECG data or (b) impedance data of the second time range, it is determined that the orientation of the medical device has flipped during the second time range.

15. The method according to claim 14, wherein: Comparing the respiration data with (a) the ECG data or (b) the impedance data includes: At least one of the following is performed: a phase comparison analysis, a correlation analysis, a benchmark comparison analysis, or a pattern recognition analysis.

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