Detecting Orientation Changes in Implantable Medical Devices

Abstract

Embodiments of the present disclosure relate to detecting a change in orientation of an implantable medical device. In an exemplary embodiment, a medical device having a processor includes an acceleration sensor and a memory. The acceleration sensor is configured to generate acceleration data including a plurality of acceleration measurements. The memory includes instructions that, when executed by the processor, cause the processor to: obtain acceleration data from the acceleration sensor; and determine that the medical device flips based on the acceleration data.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Provisional Application No. 62 / 858,696, filed on June 7, 2019, the entire contents of which are incorporated herein by reference. 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] An implantable medical device (IMD) may be configured to sense physiological parameters and / or provide therapy. The total available volume enclosed within the housing of the IMD may be adjusted based on considerations of patient comfort and performance. Examples of IMDs include implantable cardiac monitors (ICMs), implantable loop recorders (ILRs), etc., which may be configured to be implanted subcutaneously in a patient's body for monitoring one or more physiological parameters, such as those associated with the heart and / or lungs.

[0005] To facilitate a more comfortable and effective experience, these devices can be designed to keep the overall volume of the device as small as possible. Every year, these devices are becoming smaller and include more and more functional sensors. In many cases, the orientation of the sensors relative to the body is an important input for many sensors and algorithms. Due to their geometry, some of these devices have a relatively high probability of rotation in the body. Summary of the invention

[0006] Embodiments for detecting a change in orientation of an implantable medical device include, but are not limited to, the following exemplary embodiments.

[0007] In Example 1, a medical device with a processor includes: an acceleration sensor configured to generate acceleration data, the acceleration data including multiple acceleration measurement values; and a memory having computer executable instructions recorded thereon, wherein the computer executable instructions are configured to, when executed by the processor, cause the processor to: obtain acceleration data from the acceleration sensor; and determine that the medical device has flipped over based on the acceleration data.

[0008] In Example 2, the medical device of Example 1, wherein the instructions are configured to cause the processor to determine that the medical device has flipped over by causing the processor to: generate a slope of acceleration data, the slope comprising axis values ​​of at least two axes relative to time; apply a moving average filter to the data to generate a first smoothed axis data set corresponding to the first axis and a second smoothed axis data set corresponding to the second axis; identify an intersection between the first smoothed axis data set and the second smoothed axis data set; and based on the identified intersection, determine that the medical device has flipped over.

[0009] In Example 3, the medical device of Example 1, wherein the instructions are configured to cause the processor to determine that the medical device has flipped by causing the processor to: generate a slope of acceleration data, the slope comprising axis values ​​of at least one axis relative to time; apply a moving average filter to the data to generate a first smoothed axis data set corresponding to a first axis of the at least one axis; identify a first sign change associated with the first smoothed axis data set; and based on the identified first sign change, determine that the medical device has flipped.

[0010] In Example 4, the medical device of Example 3, wherein the instructions are further configured to cause the processor to apply a moving average filter to the data to generate a second smooth axis data set corresponding to the second axis; identify a second sign change associated with the second smooth axis data set; and based on the identified first sign change and second sign change, determine that the medical device has flipped.

[0011] In Example 5, the medical device of any of Examples 1-4, wherein the instructions are further configured to cause the processor to apply a correction to an output of a monitoring process in response to determining that the medical device has flipped over.

[0012] In Example 6, the medical device of Example 5, wherein the monitoring process includes at least one of a posture algorithm, a heart sound algorithm, and an ICM impedance sensing process.

[0013] In Example 7, the medical device of any of Examples 5 or 6, wherein the instructions are configured to cause the processor to apply a correction to an output of the monitoring process by recalibrating the monitoring process to account for a flipped orientation of the medical device.

[0014] In Example 8, the medical device of any one of Examples 1-7, wherein the instructions are further configured to cause the processor to generate a notification in response to determining that the medical device has flipped over.

[0015] In Example 9, the medical device of Example 8, wherein the notification includes one or more suggestions for a response to the flipped medical device.

[0016] In Example 10, the medical device of any of Examples 1-9, wherein the instructions are configured to cause the processor to determine that the medical device has flipped over by causing the processor to: apply a trigonometric function to the acceleration data, and identify the flipping based on a result of the trigonometric function applied to the acceleration data.

[0017] In Example 11, a processor-implemented method performed by a processor of a medical device includes: obtaining acceleration data from an acceleration sensor; and determining that the medical device has flipped over based on the acceleration data.

[0018] In Example 12, the method of Example 11, wherein determining that the medical device has flipped includes: generating a slope of acceleration data, the slope including axis values ​​of at least two axes relative to time; applying a moving average filter to the data to generate a first smoothed axis data set corresponding to the first axis and a second smoothed axis data set corresponding to the second axis; identifying an intersection between the first smoothed axis data set and the second smoothed axis data set; and based on the identified intersection, determining that the medical device has flipped.

[0019] In Example 13, the method of Example 11, wherein determining that the medical device has flipped includes: generating a slope of acceleration data, the slope comprising axis values ​​of at least one axis relative to time; applying a moving average filter to the data to generate a first smoothed axis data set corresponding to a first axis of the at least one axis; identifying a first sign change associated with the first smoothed axis data set; and determining that the medical device has flipped based on the identified first sign change.

[0020] In Example 14, the method of Example 13 further includes: applying a moving average filter to the data to generate a second smooth axis data set corresponding to the second axis; identifying a second sign change associated with the second smooth axis data set; and determining that the medical device has flipped based on the identified first sign change and second sign change.

[0021] In Example 15, the method of any one of Examples 11-14 further comprises applying a correction to an output of the monitoring process in response to determining that the medical device has flipped over.

[0022] In Example 16, a medical device having a processor includes: an acceleration sensor configured to generate acceleration data, the acceleration data including multiple acceleration measurement values; and a memory having computer executable instructions embodied therein, the computer executable instructions being configured to cause the processor, when executed by the processor, to: obtain acceleration data from the acceleration sensor; and determine, based on the acceleration data, that the medical device has flipped over.

[0023] In Example 17, the medical device of Example 16, wherein the instructions are configured to cause the processor to determine that the medical device has flipped over by causing the processor to: generate a slope of acceleration data, the slope comprising axis values ​​of at least two axes relative to time; apply a moving average filter to the data to generate a first smoothed axis data set corresponding to the first axis and a second smoothed axis data set corresponding to the second axis; identify an intersection between the first smoothed axis data set and the second smoothed axis data set; and based on the identified intersection, determine that the medical device has flipped over.

[0024] In Example 18, the medical device of Example 16, wherein the instructions are configured to cause the processor to determine that the medical device has flipped by causing the processor to: generate a slope of acceleration data, the slope comprising axis values ​​of at least one axis relative to time; apply a moving average filter to the data to generate a first smoothed axis data set corresponding to a first axis of the at least one axis; identify a first sign change associated with the first smoothed axis data set; and based on the identified first sign change, determine that the medical device has flipped.

[0025] In Example 19, the medical device of Example 18, wherein the instructions are further configured to cause the processor to apply a moving average filter to the data to generate a second smooth axis data set corresponding to a second axis; identify a second sign change associated with the second set of smooth axis data; and based on the identified first sign change and second sign change, determine that the medical device has flipped.

[0026] In Example 20, the medical device of Example 16, wherein the instructions are further configured to cause the processor to correct an output of the monitoring process in response to determining that the medical device has flipped over.

[0027] In Example 21, the medical device of Example 20, wherein the monitoring process includes at least one of a posture algorithm, a heart sound algorithm, and an ICM impedance sensing process.

[0028] In Example 22, the medical device of Example 20, wherein the instructions are configured to cause the processor to apply a correction to an output of the monitoring process by recalibrating the monitoring process to account for a flipped orientation of the medical device.

[0029] In Example 23, the medical device of any of Example 20, wherein the instructions are further configured to cause the processor to generate a notification in response to determining that the medical device has flipped over.

[0030] In Example 24, the medical device of Example 23, wherein the notification includes one or more suggestions for a response to the flipped medical device.

[0031] In Example 25, the medical device of Example 16, wherein the instructions are configured to cause the processor to determine that the medical device has flipped over by causing the processor to: apply a trigonometric function to the acceleration data, and identify the flipping based on a result of the trigonometric function applied to the acceleration data.

[0032] In Example 26, a processor-implemented method performed by a processor of a medical device includes: obtaining acceleration data from an acceleration sensor; and determining that the medical device has flipped over based on the acceleration data.

[0033] In Example 27, the method of Example 26, wherein determining that the medical device has flipped includes: generating a slope of acceleration data, the slope including axis values ​​of at least two axes relative to time; applying a moving average filter to the data to generate a first smoothed axis data set corresponding to the first axis and a second smoothed axis data set corresponding to the second axis; identifying an intersection between the first smoothed axis data set and the second smoothed axis data set; and based on the identified intersection, determining that the medical device has flipped.

[0034] In Example 28, the method of Example 26, wherein determining that the medical device has flipped includes: generating a slope of acceleration data, the slope comprising axis values ​​of at least one axis relative to time; applying a moving average filter to the data to generate a first smoothed axis data set corresponding to a first axis of the at least one axis; identifying a first sign change associated with the first smoothed axis data set; and determining that the medical device has flipped based on the identified first sign change.

[0035] In Example 29, the method of Example 28 further includes: applying a moving average filter to the data to generate a second smooth axis data set corresponding to the second axis; identifying a second sign change associated with the second smooth axis data set; and determining that the medical device has flipped based on the identified first sign change and second sign change.

[0036] In Example 30, the method of Example 26 further comprises applying a correction to an output of the monitoring process in response to determining that the medical device has flipped over.

[0037] In Example 31, the method of Example 30, wherein the monitoring process includes at least one of a posture algorithm, a heart sound algorithm, and an ICM impedance sensing process.

[0038] In Example 32, one or more computer-readable media including computer-executable instructions embodied thereon are configured to be executed by a processor of a medical device, wherein the instructions are configured to cause the processor to: obtain acceleration data from an acceleration sensor; and determine that the medical device has flipped over based on the acceleration data.

[0039] In Example 33, the medium of Example 32, wherein the instructions are configured to cause the processor to determine that the medical device has flipped by causing the processor to: generate a slope of acceleration data, the slope comprising axis values ​​of at least two axes relative to time; apply a moving average filter to the data to generate a first smoothed axis data set corresponding to the first axis and a second smoothed axis data set corresponding to the second axis; identify an intersection between the first smoothed axis data set and the second smoothed axis data set; and based on the identified intersection, determine that the medical device has flipped.

[0040] In Example 34, the medium of Example 32, wherein the instructions are configured to cause the processor to determine that the medical device has flipped by causing the processor to: generate a slope of acceleration data, the slope comprising axis values ​​of at least one axis relative to time; apply a moving average filter to the data to generate a first smoothed axis data set corresponding to a first axis of the at least one axis; identify a first sign change associated with the first smoothed axis data set; and determine that the medical device has flipped based on the identified first sign change,

[0041] In Example 35, the medium of Example 32, wherein the instructions are further configured to cause the processor to apply a correction to an output of the monitoring process in response to determining that the medical device has flipped over.

[0042] Although multiple embodiments are disclosed, 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, rather than restrictive. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 is a schematic diagram depicting an illustrative medical system in accordance with an embodiment of the present disclosure.

[0044] Figure 2 is a block diagram depicting an illustrative computing device in accordance with an embodiment of the disclosure.

[0045] Figure 3 An illustrative graph depicting acceleration data axis values ​​in accordance with an embodiment of the present disclosure is depicted.

[0046] Figure 4A is a flow chart depicting an illustrative method of operation of a medical device according to an embodiment of the present disclosure.

[0047] Figure 4B is a flow chart depicting an illustrative method of determining that a medical device has been flipped over according to an embodiment of the present disclosure.

[0048] Figure 4Cis a flow chart depicting another illustrative method of determining that a medical device has been flipped over according to an embodiment of the present disclosure.

[0049] Although the disclosed subject matter may have 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, the purpose is not 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 defined by the appended claims.

[0050] As used herein in connection with values ​​(e.g., terms of size, measurement, and / or other degree of quantitative and / or quantitative observation used herein for characteristics (e.g., dimensions, measurements, attributes, ingredients, etc.)) and / or ranges of values, ranges of tangible items (e.g., products, inventory, etc.) and / or intangible things (e.g., data, electronic representations of currency, accounts, information, parts of things (e.g., percentages, fractions), calculations, data models, dynamic system models, algorithms, parameters, etc.), "about" and "approximately" are used interchangeably to refer to values, configurations, directions, and / or other characteristics that are equal to (or equivalent to) or are reasonably close to the stated value, configuration, direction, and / or other characteristics. other characteristics, but may differ in value, configuration, orientation, and / or other characteristics by reasonably small amounts, which, as will be understood and readily determined by a person of ordinary skill in the relevant art, are due to measurement errors, differences in calibration of measurement and / or manufacturing equipment; human error in reading and / or setting measurements; adjustments made based on other measurements (e.g., measurements related to other things) to optimize performance and / or structural parameters; specific implementations; 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, etc.); preferences; and / or the like.

[0051] Although the term "frame" can be used to represent different elements illustratively adopted herein, the term should not be interpreted as meaning any requirement of the various frames disclosed herein or a specific order in or between the various frames disclosed herein. Similarly, although the illustrative method can be represented by one or more drawings (e.g., flow charts, communication flows, etc.), the drawings should not be interpreted as meaning any requirement of the various steps disclosed herein or a specific order in or between the various steps disclosed herein. However, some embodiments may require certain steps and / or certain orders between certain steps, as clearly described herein and / or as can be understood from the nature of the steps themselves (e.g., the execution of certain 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, etc.) 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

[0052] Embodiments of the present disclosure include an implantable medical device (IMD) that includes a processor configured to determine a change in orientation of the IMD. That is, for example, in an embodiment, the processor of the IMD may be configured to obtain acceleration data (e.g., posture data) and process the data to determine whether the IMD flips. According to an embodiment, the IMD flips when the orientation of at least one axis changes by approximately 180 degrees.

[0053] 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 within the body of 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 operatively coupled to subject 104, and IMD 102 and EMD 106 are configured to communicate with each other via communication link 108. Subject 104 may be a human, a dog, a pig, and / or any other animal having physiological parameters that may be recorded. For example, in an embodiment, subject 104 may be a human patient.

[0054] 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, etc.). In an embodiment, for example, the communication link 108 may utilize a Bluetooth low energy radio (Bluetooth 4.1) or a similar protocol, and may utilize an operating frequency in the range of 2.40 to 2.48 GHz. The term "communication link" may refer to the ability to communicate with a certain type of information in at least one direction between at least two devices, and should not be understood as being limited to a direct, continuous or otherwise limited communication channel. That is, according to an embodiment, the communication link 108 may be a continuous communication link, an intermittent communication link, a self-organizing communication link, etc. The communication link 108 may refer to a direct communication between the IMD 102 and the EMD 106, and / or an indirect communication propagated between the IMD 102 and the EMD 106 via at least one other device (e.g., a repeater, a router, a hub, etc.). Communication link 108 may facilitate unidirectional and / or bidirectional 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 embodiments, patient data may be downloaded periodically or upon 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.

[0055] In embodiments, the IMD 102 and / or EMD 106 may provide one or more of the following functions with respect to a patient: sensing, data analysis, and treatment. For example, in embodiments, the IMD 102 and / or EMD 106 may be used to measure any number of various physiological, device, subjective, and / or environmental parameters associated with the object 104 using electrical, mechanical, and / or chemical devices. The IMD 102 and / or EMD 106 may be configured to automatically collect data, collect data upon request (e.g., input provided by an object, a clinician, another device, etc.), and / or any number of various combinations and / or modifications thereof. The IMD 102 and / or EMD 106 may be configured to store data associated with physiological, device, environmental, and / or subjective parameters and / or transmit data to any number of other devices in the system 100. In embodiments, the IMD 102 and / or EMD 106 may be configured to analyze data and / or take action based on 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.

[0056] 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., input by subject 104, a clinician, another device or process, etc.). IMD 102 and / or EMD 106 may be programmable such that various characteristics of their sensing, therapy (e.g., duration and interval), and / or communication may be altered through communication between devices 102 and 106 and / or other components of system 100.

[0057] Depending on the embodiment, IMD 102 may include any type of IMD, any number of different components of an implantable system, etc. For example, IMD 102 may include 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 to provide therapeutic and / or diagnostic data about subject 104 and / or IMD 102. In various embodiments, IMD 102 may include both defibrillation and pacing / CRT capabilities (e.g., a CRT-D device).

[0058] In an embodiment, the IMD 102 may be implanted subcutaneously within an implantation site 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), etc.) configured to record physiological parameters such as, for example, one or more cardiac electrical signals, heart sounds, heart rate, blood pressure measurements, oxygen saturation, etc.

[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, sensors and associated circuits may be incorporated into the IMD 102 to detect one or more body movements or body postures and / or position-related signals. For example, an accelerometer and / or a GPS device may be employed 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 the like. In an embodiment, the acceleration data includes acceleration measurements associated with the 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" may be or include any type of accelerometer, gyroscope, magnetometer, inertial measurement unit (IMU), and / or any other type of sensor or combination of sensors configured to measure changes in acceleration, angular velocity, and the like. According to an embodiment, the acceleration data may be used to determine that the IMD 102 has flipped.

[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 body activity levels. The sleep sensor may estimate sleep patterns based on measured activity levels. Other derived parameters include, but are not limited to, a functional capacity index, an autonomic tone index, a sleep quality index, a cough index, an anxiety index, and a cardiovascular health index, in order to calculate a range of life indicators that quantify the overall health and well-being of the subject.

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

[0063] In an embodiment, the EMD 106 may include sensing components such as, for example, one or more surface electrodes configured to obtain an electrocardiogram (ECG), one or more accelerometers configured to detect motion associated with the patient 104, one or more respiratory sensors configured to obtain respiratory information, one or more environmental sensors configured to obtain information about the external environment surrounding the patient 104 (e.g., temperature, air quality, humidity, carbon monoxide level, oxygen level, air pressure, light intensity, sound, etc.), etc. In an embodiment, the EMD 106 may be configured to measure parameters related to the human body, such as temperature (e.g., a thermometer), blood pressure (e.g., a sphygmomanometer), blood characteristics (e.g., glucose level), weight, physical strength, mental acuity, diet, cardiac characteristics, relative geographic location (e.g., a global positioning system (GPS)), etc.

[0064] According to an embodiment, EMD 106 can be configured to measure subjective and / or perceptual data from object 104. Subjective data is information related to the patient's feelings, perceptions and / or opinions, such as relative to objective physiological data. For example, EMD 106 can be configured to measure the response of the object to inquiries (such as "How do you feel?" and "How much pain do you have?"). EMD 106 can be configured to prompt object 104 and use visual and / or auditory prompts to record subjective data from object 104. In an embodiment, object 104 can press a coded response button or type an appropriate response on a keyboard. In an embodiment, subjective data can be collected by allowing object 104 to speak into a microphone and using speech recognition software to process subjective data.

[0065] Figure 1 The illustrative cardiac monitoring system 100 shown is not intended to suggest any limitation on 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 on any single component or combination of components shown therein. In addition, Figure 1 The various components depicted may be integrated in embodiments with various other components depicted in embodiments (and / or components not shown), all of which are considered within the scope of the subject matter disclosed herein.

[0066] Figure 1 The various components depicted in the drawings 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, etc. An illustrative patient management and monitoring system is the SYSTEM 100 of Boston Scientific Corporation of Natick, Massachusetts. Patient Management System. Illustrative aspects of a patient management and monitoring system are described in Mazar et al., ADVANCED PATIENT MANAGEMENT SYSTEM INCLUDING INTERROGATOR / TRANSCEIVER UNIT, US Pat. No. 6,978,182, the entire contents of which are incorporated herein by reference.

[0067] 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 1 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 dedicated computing devices or general-purpose computing devices, such as "workstations," "servers," "laptops," "desktops," "tablet computers," "handheld devices," "smartphones," "general-purpose graphics processing units (GPGPUs)," etc., with reference to various components of system 100 and / or computing device 200, all of which are considered Figure 1 and Figure 2 within the range.

[0068] In an embodiment, computing device 200 includes a bus 210 that directly and / or indirectly couples the following devices: processor 220, memory 230, input / output (I / O) port 240, I / O component 250, acceleration sensor 260 (e.g., Figure 1 ) and power supply 270. Any number of additional components, different components and / or combinations of components may also be included in computing device 200. I / O components 250 may include presentation components (such as, for example, a display device, a speaker, a printing device, etc.) 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, etc.) configured to present information to a user. Acceleration sensor 260 may be any type of sensor capable of measuring acceleration, such as an accelerometer, an inertial measurement unit (IMU), a magnetometer, etc.

[0069] The bus 210 represents one or more buses (such as, for example, an address bus, a data bus, or a combination thereof). Similarly, in an embodiment, the computing device 200 may include multiple processors 220, multiple memory components 230, multiple I / O ports 240, multiple I / O components 250, multiple acceleration sensors 260, and / or multiple power supplies 270. In addition, any number of these components or their combination may be distributed and / or replicated on multiple computing devices.

[0070] 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); electronically 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 quantum state memory, etc. In an embodiment, the memory 230 stores computer-executable instructions 280 for causing the processor 220 to implement aspects of embodiments of system components discussed herein and / or perform aspects of embodiments of methods and processes discussed herein.

[0071] Computer-executable instructions 280 may include, for example, computer code, machine-usable instructions, etc., such as 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 kits, frameworks, etc. Some or all of the functionality contemplated herein may also or alternatively be implemented in hardware and / or firmware.

[0072] According to an embodiment, for example, instructions 280 may be configured to be executed by processor 220 and, when executed, cause the processor to obtain acceleration data from acceleration sensor 260 and determine whether the IMD has flipped over based on the acceleration data. According to an embodiment, instructions 280 may be configured to cause processor 220 to determine that the medical device has flipped over by causing the processor to generate a slope of the acceleration data. An example of an illustrative graph 300 including a generated slope according to an embodiment of the subject matter disclosed herein is shown in FIG. Figure 3 Shown in.

[0073] As shown, the graph 300 includes axis values ​​of at least one axis plotted against time. In the illustrated example, the graph 300 includes a vertical graph axis 302 corresponding to the accelerometer axis values ​​and a horizontal graph axis 304 corresponding to time (in days). A first axis value set 306 corresponding to the X-axis is plotted against time, a second axis value set 308 corresponding to the Y-axis is plotted against time, and a third axis value set 310 corresponding to the Z-axis is plotted against time.

[0074] According to an embodiment, the illustrated axis value sets 306, 308, and 310 may be smoothed axis data sets. That is, for example, in an embodiment, the instructions 280 may be configured to cause the processor 220 to apply a moving average filter to the data to generate at least a first smoothed axis data set corresponding to the first axis, a second smoothed axis data set corresponding to the second axis, and / or a third smoothed axis data set corresponding to the third axis. As shown, the processor 220 may be configured to Figure 3 The intersection of the first set of smoothed axis values ​​306 and the second set of smoothed axis values ​​308 is identified at the time point marked by circle 312 in FIG. The processor 220 can determine that the medical device has been flipped based on the identified intersection point.

[0075] Additionally or alternatively, the processor 220 may use one or more trigonometric functions to identify flipping around a particular axis (e.g., the x-axis, the y-axis, or the z-axis). For example, to identify flipping around the x-axis, the processor 220 may use Figure 3 The y-axis values ​​and z-axis values ​​shown use, for example, a 4-quadrant inverse tangent function, i.e., θ=atan2d(y,z) to calculate the angle. If the IMD 102 is not flipped, then this angle should be relatively constant. However, if the IMD 102 is flipped, then this angle should change by approximately 180°. In an embodiment, if the angle changes by more than a threshold amount (e.g., greater than 90°, 110°, 130°, 150°, 170°, etc.), the processor 220 can identify a flip around the y-axis. Similarly, when the angle θ=atan2d(x,z) changes by more than a threshold amount (e.g., greater than 90°, 110°, 130°, 150°, 170°, etc.), the processor 220 can identify a flip around the y-axis. Also, when the angle θ=atan2d(x,y) changes by more than a threshold amount (e.g., greater than 90°, 110°, 130°, 150°, 170°, etc.), the processor 220 can identify a flip about the z-axis. In at least some embodiments, other trigonometric functions are used to calculate the angles between the x-axis values ​​and the y-axis values, between the x-axis values ​​and the z-axis values, and between the y-axis values ​​and the z-axis values.

[0076] As another example of using trigonometric functions to identify flipping, the processor 220 can calculate the angle between any two vectors in three-dimensional space. For example, the processor 220 can use, for example, θ=cos-1(v1·v2) / (|v1||v2|) to calculate the angle between the smoothed vector v1=(x1,y1,z1) at the first time point and the smoothed vector v2=(x2,y2,z2) at the second time point. Here, xi, yi, zi are the axis values ​​at the time point, v1·v2 is the dot product of v1 and v2, that is, v1·v2=x1x2+y1y2+z1z2, and |vi|=sqrt(xi2+yi2+zi2). In an embodiment, the first time point may occur before the second time point, so the processor 220 can identify how the vector changes over time. For example, if no flipping occurs, the angle between the vectors at the two time points will be approximately zero. However, if a flipping occurs, the angle between the vectors at the two time points will be approximately 180°. In an embodiment, the processor 220 may identify a rollover if the angle is greater than a threshold amount (eg, greater than 90°, 110°, 130°, 150°, 170°, etc.).

[0077] However, in at least some embodiments, the processor 220 may not calculate the cos-1 of (v1·v2) / (|v1||v2|), and instead the processor 220 may identify a rollover based on (v1·v2) / (|v1||v2|). For example, if cos-1(v1·v2) / (|v1||v2|) is approximately zero, then (v1·v2) / (|v1||v2|) will be approximately +1. Conversely, if cos-1(v1·v2) / (|v1||v2|) is approximately 180°, then (v1·v2) / (|v1||v2|) will be approximately -1. Thus, instead of calculating cos-1(v1·v2) / (|v1||v2|), processor 220 may calculate (v1·v2) / (|v1||v2|) and determine when (v1·v2) / (|v1||v2|) exceeds a particular threshold (e.g., 0 is equivalent to 90°, -0.707 is equivalent to 135°, etc.) to identify rollover of IMD 102. In at least some other embodiments, when the patient is at rest, the processor may calculate (v1·v2) instead of (v1·v2) / (|v1||v2|) to determine rollover because (|v1||v2|) will be approximately 1 (G).

[0078] Alternatively or additionally, processor 220 may identify a first sign change associated with first smoothed axis data set 306, a second sign change associated with second smoothed axis data set 308, and / or a change in a third sign associated with third smoothed set 310. That is, processor 220 identifies a time point 314 at which the first axis value set, the second axis value set, and / or the third axis value set becomes negative. In an embodiment, instructions 280 may be configured to cause the processor to determine that the medical device has flipped based on the identified first sign change, the second sign change, and / or the third sign change. According to an embodiment, in response to determining that IMD 102 has flipped, processor 220 may be further configured to generate a notification to a clinician and / or to a patient that IMD 102 may have moved within the capsular bag, and to suggest that the clinician and / or the patient perform a body calibration procedure to account for the new orientation.

[0079] According to an embodiment, in response to determining that the medical device has flipped, the instructions 280 may be further configured to cause the processor 220 to apply a correction to the output of the monitoring process. In an embodiment, for example, the monitoring process may include a posture algorithm, a heart sound algorithm, a medical device impedance sensing process, etc. According to an embodiment, the instructions 280 may be configured to cause the processor 220 to apply a correction to the output of the monitoring process by recalibrating the monitoring process to take into account the flip direction of the medical device. In an embodiment, the processor 220 may be configured to send a notification about the flip to a clinician and / or patient, and may include one or more suggestions for a response to the flip.

[0080] Figure 2 The illustrative computing device 200 shown in FIG. Figure 3 The graph 300 shown in is not intended to impose any limitation on the scope of use or functionality of the embodiments of the present disclosure. The illustrative computing device 200 and the graph 300 should not be interpreted as having any dependency or requirement on any single component or combination of components shown therein. In addition, Figure 2 The various components depicted may be integrated in embodiments with various other components depicted in the embodiments (and / or components not shown), all of which are considered to be within the scope of the present disclosure.

[0081] Figure 4A is a flow chart depicting an illustrative method 400 of medical device operation (eg, physiological monitoring) according to an embodiment of the subject matter disclosed herein. According to an embodiment, the method 400 may be performed by Figure 1 The system 100 depicted in and / or Figure 2 200. For example, in an embodiment, the illustrative method 400 may be performed by a mobile device having an acceleration sensor (e.g., an accelerometer and / or a magnetometer), a processor, and a memory, as described herein.

[0082] An embodiment of method 400 includes obtaining acceleration data from an acceleration sensor (block 402) and determining that the medical device has flipped over based on the acceleration data (block 404). According to an embodiment, the acceleration data may include, for example, posture data captured by an acceleration sensor (e.g., an accelerometer, an inertial measurement unit (IMU), a magnetometer, etc.). In an embodiment, analyzing the acceleration data over time may reveal changes in the orientation of the device. That is, for example, in an embodiment, obtaining the acceleration data may include sampling the output of the acceleration sensor at specified time intervals. For example, the acceleration data may include sampling the output of the acceleration sensor multiple times per second, once per second, once per minute, once every two minutes, once every three minutes, once every four minutes, once every five minutes, once every six minutes, once every seven minutes, once every eight minutes, once every nine minutes, once every ten minutes, once every eleven minutes, etc.

[0083] like Figure 4A As shown, method 400 may further include applying a correction to the output of the monitoring process (box 406). According to an embodiment, the orientation of the sensor in the medical device (and therefore the orientation of the medical device itself) can be an assumption or input to any number of decision-making processes, monitoring processes, treatment processes, etc. For example, a posture determination algorithm can rely on a fixed orientation. That is, for example, if the medical device has been flipped, the sleep tilt algorithm may be interrupted. Similarly, the heart sound algorithm can rely on a fixed device orientation. For example, in an embodiment, the heart sound algorithm can be configured to detect the S2 waveform and use its peak as a reference marker. Flipping the medical device will invert the waveform, and the heart sound algorithm may select a data range of less accurate S3 measurements, which will reduce the accuracy of the heart logic. The device impedance sensing process may also depend on a fixed device orientation. That is, for example, if the medical device is flipped, the impedance vector will change, resulting in a jump in the amplitude of the signal, which may cause impedance sensing to be impaired.

[0084] Thus, in embodiments, any number of different monitoring processes may be corrected to account for the fact that the device orientation has changed from its assumed (or previously determined) orientation. Applying corrections to the outputs of the monitoring processes may be accomplished by directly modifying the outputs and / or by recalibrating the monitoring processes to account for the different (e.g., flipped) orientations of the medical device. In embodiments, the monitoring processes may include, for example, a posture algorithm (e.g., an algorithm configured to monitor sleep tilt), a heart sound algorithm, and / or a medical device impedance sensing process.

[0085] Figure 4B 4 is a flow chart depicting an illustrative method 408 of determining that a medical device has been flipped, according to an embodiment of the subject matter disclosed herein. According to an embodiment, the method 400 may be performed by Figure 1 The system 100 depicted in and / or Figure 2 200. For example, in an embodiment, the illustrative method 400 may be performed by a mobile device having an acceleration sensor (e.g., an accelerometer and / or a magnetometer), a processor, and a memory, as described herein.

[0086] like Figure 4B As shown, method 408 may include generating a slope of acceleration data (box 410), the graph including axis values ​​of at least two axes plotted relative to time; applying a moving average filter to the data to generate a first smoothed axis data set corresponding to the first axis and a second smoothed axis data set corresponding to the second axis (box 412); identifying an intersection of the first smoothed axis data set and the second smoothed axis data set (box 414); and based on the identified intersection, determining that the medical device has flipped (box 416).

[0087] Figure 4C is another flow chart depicting another illustrative method 418 of determining that a medical device has been flipped in accordance with an embodiment of the subject matter disclosed herein. The method 418 may be used instead of or in addition to Figure 4B According to an embodiment, method 418 may be performed by Figure 1 The system 100 depicted in and / or Figure 2 200. For example, in an embodiment, the illustrative method 400 may be performed by a mobile device having an acceleration sensor (e.g., an accelerometer and / or a magnetometer), a processor, and a memory, as described herein.

[0088] like Figure 4C As shown, method 418 may include generating a slope of acceleration data, the slope including axis values ​​of at least one axis relative to time (box 420); applying a moving average filter to the data to generate a first smoothed axis data set corresponding to the first axis, a second smoothed axis data set corresponding to the second axis, and / or a third smoothed axis data set corresponding to the third axis (box 422); identifying a first sign change associated with the first smoothed axis data set, identifying a second sign change associated with the second smoothed axis data set, and / or identifying a third sign change associated with the third smoothed axis data set (box 424); and determining that the medical device has flipped based on the identified first sign change, second sign change, and / or third sign change (box 426). In an embodiment, box 424 may additionally or alternatively include using a trigonometric function on the axis data, as described above with respect to Figure 3 described.

[0089] Various modifications and additions may be made to the exemplary embodiments discussed without departing from the scope of the disclosed subject matter. For example, although the above-described embodiments relate 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. That is, for example, an embodiment may include one or more filters and / or other components that help interpret sensed physiological signals in the presence of some interference, some of which is caused by the energy supply current sharing a portion of the physical sensing path. Therefore, the scope of the disclosed subject matter is intended to cover all such substitutions, modifications, and variations that fall within the scope of the claims and all equivalents thereof.

Claims

1. A medical device having a processor, the medical device comprising: an acceleration sensor configured to generate acceleration data, the acceleration data comprising a plurality of acceleration measurements; as well as a memory having computer-executable instructions embodied therein, the computer-executable instructions being configured to, when executed by the processor, cause the processor to: Obtaining the acceleration data from the acceleration sensor; determining, based on the acceleration data, that the medical device has flipped over; and causing the processor to apply a correction to an output of a monitoring process to account for a flipped orientation of the medical device in response to determining that the medical device has flipped over, wherein the monitoring process includes a heart sound algorithm or an impedance sensing process, and The instructions are configured to cause the processor to determine that the medical device has flipped over by causing the processor to: generating a slope of the acceleration data, the slope comprising axis values ​​of at least one axis relative to time; applying a moving average filter to the data to generate a first smoothed axis data set corresponding to a first axis of the at least one axis; identifying a first sign change associated with the first smoothed axis data set; and Based on the identified first sign change, it is determined that the medical device has been flipped over.

2. The medical device according to claim 1, wherein: The instructions are further configured to cause the processor to apply the moving average filter to the data to generate a second smoothed axis data set corresponding to a second axis; identify a second sign change associated with the second smoothed axis data set; and determine that the medical device has flipped based on the identified first sign change and second sign change.

3. The medical device according to claim 1, wherein: The instructions are configured to cause the processor to apply a correction to an output of the monitoring process by recalibrating the monitoring process.

4. The medical device according to any one of claims 1 to 3, wherein: The instructions are further configured to cause the processor to generate a notification in response to determining that the medical device has been flipped over.

5. The medical device according to claim 4, wherein: The notification includes one or more suggestions for a response to the flipped medical device.

6. The medical device according to any one of claims 1 to 3, wherein: The instructions are configured to cause the processor to determine that the medical device has flipped over by causing the processor to apply a trigonometric function to the acceleration data and identify flipping over based on a result of the trigonometric function applied to the acceleration data.

7. A processor-implemented method executed by a processor of a medical device, the method comprising: Obtain acceleration data from an acceleration sensor; determining, based on the acceleration data, that the medical device has flipped over; In response to determining that the medical device has been flipped, applying a correction to an output of the monitoring process to account for the flipped orientation of the medical device, wherein the monitoring process includes a heart sound algorithm or an impedance sensing process, and Wherein, determining that the medical device has been flipped includes: generating a slope of the acceleration data, the slope comprising axis values ​​of at least two axes relative to time; applying a moving average filter to the data to generate a first smoothed axis data set corresponding to the first axis and a second smoothed axis data set corresponding to the second axis; identifying an intersection between the first smoothed axis data set and the second smoothed axis data set; and Based on the identified intersection point, it is determined that the medical device has been flipped over.

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