Medical device systems and methods for determining fall risk in response to detecting body position movement

Abstract

The present disclosure relates to devices, systems, and techniques for monitoring a patient condition. In some examples, a medical device system includes a medical device including a set of sensors. Additionally, the medical device system includes processing circuitry configured to identify a time of an event corresponding to the patient based on at least one signal of a set of signals, and set a time window based on the time of the event. Additionally, the processing circuitry is configured to save a set of data including one or more signals of the set of signals to a fall risk database in memory, such that the fall risk database can be analyzed in order to determine a fall risk score corresponding to the patient, where the set of data corresponds to the time window.

Description

Technical Field

[0001] This disclosure generally relates to medical device systems, and more specifically to medical device systems configured to monitor patient parameters. Background Technology

[0002] Some types of medical devices can be used to monitor one or more physiological parameters of a patient. Such devices may include, or may be part of a system that includes sensors that detect signals associated with these physiological parameters. Values ​​determined based on these signals can be used to help detect changes in the patient's condition, assess the effectiveness of treatment, or broadly evaluate the patient's health. Summary of the Invention

[0003] Generally, this disclosure relates to devices, systems, and techniques for monitoring a patient's fall risk. In some cases, fall risk can indicate the likelihood that a patient will fall within a time period following a reference time. As used herein, the term "fall" refers to an involuntary change of body position caused by gravity, such as falling from a standing position or from a sitting position. Medical device systems can determine a patient's fall risk by monitoring one or more patient parameters over a period of time and analyzing the responses of those parameters to certain events. For example, a medical device system can determine how one or more patient parameters change in response to changes in body position (e.g., sit-to-stand movement) or in response to cardiac events (e.g., premature ventricular contractions (PVCs)) and determine the patient's fall risk based on the determined changes.

[0004] An implantable medical device (IMD) may include one or more electrodes configured to measure a patient's electrogram (EGM). In some cases, the EGM may indicate ventricular depolarization (e.g., R wave) and the patient's heart rate. Additionally, the IMD may determine tissue perfusion based on impedance sensed via the electrodes and / or oxygen saturation using an optical sensor. A processing circuitry system may determine the patient-associated pulse conduction time (PTT) based on the EGM, impedance, measured oxygen saturation, or any combination thereof. PTT is related to blood pressure. Therefore, the processing circuitry system may be configured to use the PTT measurement performed by the IMD as a representation of the patient's blood pressure. In this way, the processing circuitry system may be configured to track the patient's blood pressure and heart rate over a period of time.

[0005] Additionally, as an example, the IMD may include a 3-axis accelerometer that generates accelerometer signals indicative of the patient's posture, activity level, gait, and body angles. The processing circuitry can use these accelerometer signals to perform fall risk analysis. For example, the processing circuitry may be configured to identify one or more body position changes and determine how one or more patient parameters change in response to each of the one or more body position changes. For example, it may be expected that the patient's heart rate and blood pressure will increase in response to a sit-to-stand movement performed by the patient. If the increase in blood pressure and heart rate following the patient's sit-to-stand movement is less than the expected increase, the processing circuitry may determine that the patient has an increased risk of fall. Alternatively or concurrently, the processing circuitry may determine that the patient has an increased risk of fall in response to changes in the patient's gait over time, changes in the patient's standing speed over time, or any other determinable change in the patient parameters over time.

[0006] The technology disclosed herein can provide one or more advantages. For example, it may be advantageous to create one or more “bins,” such as a sit-to-stand bin, a lie-to-sit bin, and a lie-to-stand bin, for evaluating data collected by the IMD to determine a patient’s fall risk. The processing circuitry system can analyze heart rate and blood pressure (and / or other parameters) after each sit-to-stand movement in the sit-to-stand bin, analyze heart rate and blood pressure after each lie-to-sit movement in the lie-to-sit bin, and analyze each lie-to-stand movement in the lie-to-stand bin. Based on the analysis of each bin, such as based on significant parameter value changes over time in one or more bins, the processing circuitry system can determine the patient’s fall risk. Separating data into different bins for different movements or events facilitates a more meaningful comparison of patient responses to events over time and a more accurate identification of fall risk.

[0007] In some examples, the medical device system includes a medical device comprising a set of sensors configured to sense a set of signals, wherein the set of sensors includes motion sensor signals configured to generate motion sensor signals indicative of a patient's movement. Additionally, the medical device system includes a processing circuitry configured to: identify the time of an event corresponding to the patient based on at least one of the signals in the set; set a time window based on the time of the event; and store a set of data including one or more of the signals in a fall risk database in memory, enabling analysis of the fall risk database to determine a fall risk score corresponding to the patient, wherein the set of data corresponds to the time window.

[0008] In some examples, one method includes: sensing a set of signals by a medical device including a set of sensors, wherein the set of sensors includes motion sensor signals configured to generate motion sensor signals indicative of a patient's movement, wherein the set of signals includes motion sensor signals; identifying, by a processing circuitry system, the time of an event corresponding to the patient based on at least one of the signals in the set; and setting a time window by the processing circuitry system based on the time of the event. Additionally, the method includes storing a set of data including one or more of the signals in a fall risk database in memory by the processing circuitry system, enabling analysis of the fall risk database to determine a fall risk score corresponding to the patient, wherein the set of data corresponds to the time window.

[0009] In some examples, a non-transitory computer-readable medium includes instructions for causing one or more processors to sense a set of signals from a medical device including a set of sensors, wherein the set of sensors includes motion sensor signals configured to generate motion sensor signals indicative of a patient's movement, wherein the set of signals includes motion sensor signals and identifies the time of an event corresponding to the patient based on at least one of the signals. Additionally, the instructions cause one or more processors to set a time window based on the time of the event; and to save a set of data including one or more of the signals to a fall risk database in memory, enabling analysis of the fall risk database to determine a fall risk score corresponding to the patient, wherein the set of data corresponds to the time window.

[0010] This summary is intended to provide an overview of the subject matter described in this disclosure. It is not intended to provide an exclusive or exhaustive explanation of the systems, apparatus, and methods described in detail in the following drawings and specification. Further details of one or more examples of this disclosure are set forth in the drawings and the following detailed description. Other features, objectives, and advantages will be apparent from the specification, drawings, and claims. Attached Figure Description

[0011] Figure 1 This is a conceptual diagram illustrating the environment of an exemplary medical device system for a patient, combining one or more technologies according to this disclosure.

[0012] Figure 2 This illustrates one or more technologies according to the present document. Figure 1 A conceptual diagram of an exemplary configuration of an implantable medical device (IMD) for a medical device system.

[0013] Figure 3 This illustrates one or more technologies according to the present document. Figure 1 and Figure 2 A functional block diagram of an exemplary configuration of an IMD.

[0014] Figure 4A and Figure 4B The invention illustrates one or more techniques that can be substantially similar to those described herein. Figures 1 to 3 An IMD may include one or more additional exemplary IMDs but may include one or more additional features.

[0015] Figure 5 This is a block diagram illustrating an exemplary configuration of components of an external device according to one or more technologies of this disclosure.

[0016] Figure 6 This is a block diagram illustrating an exemplary system according to one or more technologies described herein, the exemplary system including an access point, a network, an external computing device such as a server, and one or more other computing devices that may be coupled to an IMD, external devices, and processing circuitry system via the network.

[0017] Figure 7 It is a graph showing accelerometer signal curves and physiological parameter curves according to one or more techniques described herein.

[0018] Figure 8 This is a flowchart illustrating exemplary operations for generating data that can be analyzed to determine a fall risk score, according to one or more techniques disclosed herein. Detailed Implementation

[0019] This disclosure describes techniques for monitoring a patient's fall risk. For example, an implantable medical device (IMD) can measure a set of patient parameters over a period of time. A processing circuitry system can identify one or more events associated with the patient, such as one or more body position changes and / or one or more cardiac events, based on one or more signals generated by the IMD. The processing circuitry system can analyze how at least one of the patient parameters in the set responds to each of the one or more events. The processing circuitry system can determine the patient's fall risk based on the patient parameters' response to the identified body position changes.

[0020] Falls can be a significant challenge for some patients. Therefore, it may be beneficial to monitor one or more patient parameters, such as those indicated by signals collected by a medical device, to determine if a patient is at risk of falling in the future. In some cases, falls are caused by arrhythmias, such as cardiac arrest, tachycardia, bradycardia, or atrial fibrillation (AF), or any combination thereof. For example, a patient with bradycardia may have abnormally low blood pressure or an abnormally low heart rate. Therefore, a patient with bradycardia may experience dizziness when standing, walking, or otherwise exerting force. In some cases, this dizziness may cause the patient to fall. Other conditions or illnesses such as heart failure, chronic obstructive pulmonary disease (COPD), epilepsy, and dementia (e.g., Parkinson's disease, multiple sclerosis, and Alzheimer's disease) can also cause falls. Furthermore, older patients may have an increased risk of falls.

[0021] In any case, it may be advantageous to analyze signals recorded by one or more medical devices to track a patient's fall risk. If it is determined that a patient will have an increased risk of falling, the processing circuitry system may output an alarm, output a treatment recommendation, and / or cause one or more medical devices to deliver treatment to the patient. In some examples, the processing circuitry system monitors and stores one or more parameters, including blood pressure, heart rate, tissue perfusion, motion data, and gyroscopic data. The processing circuitry system may analyze one or more parameters to determine the patient's fall risk.

[0022] Figure 1 This is a conceptual diagram illustrating an environment of an exemplary medical device system 2 incorporating one or more technologies according to this disclosure, and a patient 4. The example technologies can be used with an IMD 10, which can be used with an external device 12 and... Figure 1 At least one of the other devices not shown in the diagram performs wireless communication. Processing circuitry system 14 in Figure 1 The diagram is conceptually shown as a processing circuitry system separate from, but potentially including, the IMD 10 and / or the external device 12. Generally, the techniques disclosed herein can be performed by one or more devices of a system, such as a processing circuitry system 14 including one or more devices that provide signals, or a processing circuitry system of one or more devices that does not include sensors but still analyzes signals using the techniques described herein. For example, another external device ( Figure 1 (Not shown) may include at least a portion of processing circuitry system 14, and the other external device is configured to communicate remotely with IMD 10 and / or external device 12 via a network.

[0023] In some examples, IMD 10 can be implanted outside the chest cavity of patient 4 (e.g., subcutaneous implantation). Figure 1 (As described in the pectoral muscle location). IMD 10 may be located near or directly below the sternum at the level of the patient's heart, for example, at least partially within the heart contour. In some examples, IMD 10 uses LINQ, available from Medtronic plc, Dublin, Ireland. ™ In the form of an insertable cardiac monitor (ICM).

[0024] Clinicians sometimes diagnose patients with medical conditions based on one or more observed physiological signals collected by physiological sensors such as electrodes, optical sensors, chemical sensors, temperature sensors, acoustic sensors, and motion sensors. In some cases, clinicians apply non-invasive sensors to patients to sense one or more physiological signals when the patient makes a medical appointment at the clinic. However, in some examples, physiological markers of a patient's condition (e.g., irregular heartbeat and long-term respiratory trends) are rare or difficult to observe over a relatively short period. Therefore, in these examples, clinicians may not be able to observe the physiological markers needed to diagnose a patient with a medical condition while monitoring one or more of the patient's physiological signals during the medical appointment. Additionally, it may be advantageous to monitor one or more patient parameters over an extended period (e.g., days, weeks, or months), enabling the analysis of one or more parameters to identify one or more changes or trends within that extended period. Figure 1 In the example shown, IMD 10 was implanted in patient 4 to continuously record one or more physiological signals of patient 4 over an extended period of time.

[0025] In some examples, IMD 10 includes one or more sensors configured to detect physiological signals of patient 4. For example, IMD 10 includes a set of electrodes ( Figure 1 (Not shown in the image). This set of electrodes is configured to detect one or more signals associated with the cardiac and / or pulmonary function of patient 4. In some examples, IMD 10 may sense an electrogram (EGM) via this set of electrodes. The EGM may represent one or more physiological electrical signals corresponding to the heart of patient 4. For example, the EGM may indicate ventricular depolarization (R wave), atrial depolarization (P wave), ventricular repolarization (T wave), and other events. Information related to the aforementioned events, such as the timing of the separation of one or more events, can be used for various purposes, such as determining whether an arrhythmia is occurring and / or predicting whether an arrhythmia may occur. In some examples, IMD 10 may be configured to detect a tissue impedance signal via this set of electrodes. The tissue impedance signal may represent the impedance value between one or more electrodes in this set of electrodes and the subcutaneous tissue of patient 4. Tissue impedance can be applied for various purposes, such as determining whether an arrhythmia is occurring and / or predicting whether an arrhythmia may occur.

[0026] In addition, the IMD 10 may additionally or alternatively include one or more optical sensors, motion sensors (e.g., accelerometers), temperature sensors, chemical sensors, pressure sensors, or any combination thereof. Such sensors can detect one or more physiological parameters indicative of the patient's condition.

[0027] For example, IMD 10 includes one or more accelerometers. The accelerometers of IMD 10 can collect accelerometer signals reflecting the amplitude of motion of patient 4. In some cases, the accelerometers can collect triaxial accelerometer signals indicating the movement of patient 4 in three-dimensional Cartesian space. For example, the accelerometer signals may include a vertical axis accelerometer signal vector, a horizontal axis accelerometer signal vector, and a front axis accelerometer signal vector. The vertical axis accelerometer signal vector may represent the acceleration of patient 4 along the vertical axis, the horizontal axis accelerometer signal vector may represent the acceleration of patient 4 along the horizontal axis, and the front axis accelerometer signal vector may represent the acceleration of patient 4 along the front axis. In some cases, the vertical axis extends substantially along the torso of patient 4 from the neck to the waist, the horizontal axis extends perpendicularly to the vertical axis across the chest of patient 4, and the front axis extends outward from and through the chest of patient 4, said front axis being perpendicular to the vertical and horizontal axes.

[0028] IMD 10 may include an optical sensor. In some cases, the optical sensor may include two or more light emitters and one or more light detectors. The optical sensor may perform one or more measurements to determine the tissue oxygenation of patient 4. For example, the optical sensor may perform one or more tissue oxygen saturation (StO2) measurements. In some examples, StO2 may represent a weighted average between arterial oxygen saturation (SaO2) and venous oxygen saturation (SvO2). In some examples, the optical sensor may perform one or more pulse oximetry (SpO2) measurements. In some cases, SpO2 may represent an approximation of SaO2. Trends in oxygen saturation (e.g., StO2, SaO2, SvO2, and SpO2) may indicate one or more patient conditions, such as heart failure, sleep apnea, or COPD. For example, a steady decline in StO2 values ​​over a period of time may indicate an increased risk of worsening heart failure in a patient. In this way, the IMD can perform several StO2 measurements over a period of time (e.g., hours, days, weeks, or months), and the processing circuitry can use the data from the StO2 measurements to identify trends in StO2 values. Based on the identified trends, in some cases, the processing circuitry can identify medical conditions present in the patient or monitor conditions known to exist in the patient.

[0029] During a corresponding StO2 measurement, the light emitter of an optical sensor can output light to a tissue region near the IMD, the light containing a first set of frequency components. One or more photodetectors can sense the light containing a second set of frequency components. A processing circuitry is configured to compare the first set of frequency components with the second set of frequency components to identify a StO2 value corresponding to the corresponding StO2 measurement, where the StO2 value represents the ratio of oxygen-saturated hemoglobin located in the tissue region to the total amount of hemoglobin located in the tissue region.

[0030] External device 12 may be a computing device configured for use in a setting such as a home, clinic, or hospital, and may also be configured to communicate with IMD 10 via wireless telemetry. For example, external device 12 may be coupled to a remote patient monitoring system, such as Carelink, available from Medtronic in Dublin, Ireland. ® In some examples, external device 12 may include a programmer, an external monitor, or a consumer device such as a smartphone or tablet computer.

[0031] In other examples, external device 12 may be a separate application within a larger workstation or another multi-functional device, rather than a dedicated computing device. For example, the multi-functional device may be a laptop computer, tablet computer, workstation, one or more servers, cellular phone, personal digital assistant, or another computing device that can operate an application that enables the computing device to operate as a secure device.

[0032] When the external device 12 is configured for use by a clinician, it can be used to transmit instructions to the IMD 10. Exemplary instructions may include requests to set electrode combinations for sensing and any other information that can be programmed into the IMD 10. The clinician can also configure and store operating parameters of the IMD 10 within the IMD 10 with the assistance of the external device 12. In some examples, the external device 12 assists the clinician in configuring the IMD 10 by providing a system for identifying potentially beneficial operating parameter values.

[0033] Regardless of whether the external device 12 is configured for use by a clinician or a patient, the external device 12 is configured to communicate wirelessly with the IMD 10 and optionally with another computing device ( Figure 1 (Not shown in the image). For example, the external device 12 can communicate via near-field communication technology (e.g., inductive coupling or other communication technology that can operate within a range of less than 10cm-20cm) and far-field communication technology (e.g., radio frequency (RF) telemetry according to 802.11 or Bluetooth). ® The standard set, or other communication technologies that can operate within a range greater than that of near-field communication technologies, can be used for communication.

[0034] In some examples, the processing circuitry 14 may include one or more processors configured to implement functions and / or processing instructions for execution within the IMD 10, external device 12, one or more other devices, or any combination thereof. For example, the processing circuitry 14 may be able to process instructions stored in memory. The processing circuitry 14 may include, for example, a microprocessor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or equivalent discrete or integrated logic circuitry, or a combination of any of the foregoing devices or circuits. Therefore, the processing circuitry 14 may include any suitable structure, whether hardware, software, firmware, or any combination thereof, to perform the functions of the processing circuitry 14 described herein.

[0035] Processing circuitry 14 can refer to a processing circuitry system located within any combination of IMD 10 and external device 12. In some examples, processing circuitry 14 can be entirely located within the housing of IMD 10. In other examples, processing circuitry 14 can be entirely located within the housing of external device 12. In still other examples, processing circuitry 14 can be located within IMD 10, external device 12, and... Figure 1 Within any combination of another device or group of devices not shown. Therefore, the techniques and capabilities attributed herein to processing circuit system 14 are also attributed to IMD 10, external device 12, and Figure 1 Any combination of other devices not shown in the diagram.

[0036] Memory ( Figure 1 (Not shown) can be configured to store information within the medical device system 2 during operation. The memory may include a computer-readable storage medium or a computer-readable storage device. In some examples, the memory includes one or both of short-term memory and long-term memory. The memory may include, for example, random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), magnetic disk, optical disk, flash memory, or electrically programmable memory (EPROM) or electrically erasable programmable memory (EEPROM). In some examples, the memory is used to store program instructions executed by the processing circuitry system 14.

[0037] The memory may refer to memory located within either or both of the IMD 10 and the external device 12. In some examples, the memory may be entirely located within the housing of the IMD 10. In other examples, the memory may be entirely located within the housing of the external device 12. In still other examples, the memory may be located within the IMD 10, the external device 12, and... Figure 1Within any combination of another device or group of devices not shown. Therefore, the techniques and capabilities attributed herein to memory are attributed to IMD 10, external device 12, and Figure 1 Any combination of other devices not shown in the diagram.

[0038] Figure 1 The medical device system 2 may be an example of a system for collecting a set of signals according to one or more techniques of this disclosure. For example, the IMD 10 may collect EGM via one or more electrodes. The processing circuitry system 14 may include an EGM analysis circuitry system configured to determine one or more parameters or events of the EGM (e.g., P wave, R wave, and T wave). A cardiac signal analysis circuitry system that may be implemented as part of the processing circuitry system 14 may perform signal processing techniques to extract information indicating one or more parameters or events of the EGM. The EGM measured via one or more electrodes of the IMD 10 may include noise introduced into the EGM by various cardiac and non-cardiac sources. The IMD 10 may include a pre-filtering circuitry system configured to eliminate or reduce at least some of the noise in the EGM.

[0039] The processing circuitry 14 can identify one or more changes in body position of the patient 4 based on accelerometer signals collected by the accelerometer of the IMD 10. Examples of body position changes that the processing circuitry 14 can identify in the accelerometer signals include sit-to-stand movements, lie-to-sit movements, lie-to-stand movements, stand-to-sit movements, stand-to-lie movements, sit-to-lie movements, and other body position changes. For example, the processing circuitry 14 can identify changes in the amplitude of one or more axes of the accelerometer signal and identify one or more body position changes based on these changes.

[0040] In some examples, the processing circuitry 14 can identify the body angle of the patient 4. As used herein, the term "body angle" can refer to the angle of the patient 4's torso. For example, an IMD 10 can be implanted in the torso of the patient 4. Thus, the accelerometer signal generated by the IMD 10 reflects the body angle of the patient's torso. In some cases, a zero-degree body angle can represent the body angle when the torso is parallel to the ground (e.g., when the patient 4 is lying down). In some cases, a ninety-degree body angle can represent the body angle when the torso is perpendicular to the ground (e.g., when the patient 4 is standing or when the patient 4 is sitting in an upright position). Thus, the processing circuitry 14 can be able to identify when the patient 4 is leaning forward while standing or bending forward while sitting by recognizing changes in the body angle indicated by the accelerometer signal.

[0041] The processing circuitry 14 can be configured to determine the gait of patient 4 based on accelerometer signals collected by IMD 10 while patient 4 is walking or running. For example, the processing circuitry 14 can be configured to determine the stride rate of patient 4 when taking a step. Alternatively, the processing circuitry 14 can be configured to calculate a stability score corresponding to the gait of patient 4 based on the accelerometer signals.

[0042] Therefore, the IMD 10 can be configured to measure a set of physiological parameters and generate accelerometer signals indicating one or more aspects of the patient 4's physical position and movement. The processing circuitry 14 can be configured to analyze the data collected by the IMD 10 to determine one or more states and risk factors of the patient 4, such as the risk of the patient 4 falling within a time period after the current time. For example, the processing circuitry 14 can maintain a fall risk database that stores multiple sets of data, each corresponding to a corresponding movement of body position or change of body angle. The processing circuitry 14 can update the fall risk database on a rolling basis. For example, when the processing circuitry 14 detects a movement of body position or a change of body angle, it can add a set of data to the multiple sets of data stored in the fall risk database. In some cases, the processing circuitry 14 can remove one or more sets of data from the fall risk database.

[0043] Processing circuitry system 14 can analyze multiple sets of data currently stored in a fall risk database to determine a fall risk score associated with patient 4. In some examples, processing circuitry system 14 can analyze multiple sets of data currently stored in the fall risk database periodically (e.g., hourly, daily, weekly, or any other time interval). Alternatively or additionally, processing circuitry system 14 can analyze multiple sets of data currently stored in the fall risk database in response to receiving instructions for analyzing data (e.g., from external device 12 or another user device). In any case, when processing circuitry system 14 determines a fall risk score associated with patient 4, processing circuitry system 14 can determine the fall risk score based on one or more sets of data currently stored in the fall risk database. In some examples, the fall risk score may be referred to as a "fall risk index".

[0044] Each set of data stored in the fall risk database may include one or more portions of a signal measured by the IMD 10, other implanted devices, other external devices, or any combination thereof. For example, the IMD 10 may collect one or more of an accelerometer signal, EGM, one or more tissue oxygenation signals (StO2 and / or SpO2), and one or more other signals. When the IMD 10 collects a signal, it may collect a series of samples corresponding to the respective signal, and this series of samples may represent the signal itself. Thus, a “portion” of a signal may represent a collection of consecutive samples of the signal. Each set of data stored in the fall risk database may include a portion of each signal in a set of signals, where each corresponding portion corresponds to a corresponding time window. In some examples, the time window corresponds to the time during which the processing circuitry 14 detects a change in body position or body angle in the accelerometer signal.

[0045] The processing circuitry 14 can be configured to identify changes in the patient's body position based on accelerometer signals collected by the IMD 10. Additionally, the processing circuitry 14 can be configured to identify the time or period in which the body position change occurs. The processing circuitry 14 can identify the type of body position movement. For example, the processing circuitry 14 can determine that the body position movement represents a sit-to-stand movement, a lie-to-sit movement, a lie-to-stand movement, a stand-to-sit movement, a stand-to-lie movement, a sit-to-lie movement, or another type of movement. Alternatively or additionally, the processing circuitry 14 can identify the type of body position movement based on changes in body angle associated with the body position movement. In one example, the processing circuitry 14 can determine that the body position movement represents a change from a 40-degree body angle to a 90-degree body angle.

[0046] The processing circuitry system 14 can set a time window based on the time or time period in which the body position change occurs. For example, the processing circuitry system 14 can set the time window to begin at a first time and end at a second time, wherein the first time and the second time are identified relative to the time or time period in which the body position change occurs. In some examples, the first time represents the time at which the corresponding body position change begins. In some examples, the first time represents the time at which the corresponding body position change ends. In some examples, the first time represents the time between the time at which the body position change begins and the time at which the body position change ends. In some examples, the first time is a predetermined amount of time before the time or time period in which the corresponding body position change occurs. In some examples, the first time is a predetermined amount of time after the time or time period in which the corresponding body position change occurs. In some examples, the second time is a predetermined amount of time after the time or time period in which the corresponding body position change occurs, wherein the second time is after the first time. In any case, the time window may include at least a portion of time after the corresponding body position movement.

[0047] In some cases, the processing circuitry 14 may save a set of data, including one or more signals corresponding to the time of movement associated with the corresponding body position, to a memory stored in a storage device. Figure 1 A fall risk database (not shown) is used. This allows the processing circuitry system 14 to analyze the fall risk database to determine the latest fall risk score corresponding to patient 4. This set of data may include a set of signal portions. Each signal portion in this set of signal portions corresponds to a corresponding signal collected by IMD 10 or another device, and each signal portion in this set of signal portions includes data corresponding to a time window selected by the processing circuitry system 14 based on the time or time period in which the change in body position occurred. For example, this set of data may include a portion of the accelerometer signal from a first time to a second time, a portion of the EGM collected by IMD 10 from a first time to a second time, a portion of the tissue impedance signal collected by IMD 10 from a first time to a second time, and a portion of the tissue oxygenation signal collected by IMD 10 from a first time to a second time.

[0048] The fall risk database may include multiple sets of data, each corresponding to a specific body position movement, and may include multiple "libraries" configured to store one or more of these sets. For example, each of the multiple libraries may be associated with a corresponding category among multiple classifications. When the processing circuitry system 14 identifies a change in body position, it may assign one or more classifications to the identified change in body position. In some examples, the multiple classifications may include a sit-to-stand classification, a lie-to-sit classification, a lie-to-stand classification, a stand-to-sit classification, a stand-to-lie-down classification, and a sit-to-lie-down classification. In some examples, the multiple classifications may include one or more body angle change classifications, wherein each of these classifications is associated with a first range of body angles and a second range of body angles. A body angle change from the first body angle to the second body angle is considered to conform to a body angle change classification if the first body angle falls within the first range of body angles associated with the classification and if the second body angle falls within the second range of body angles associated with the classification. In some examples, the multiple classifications may include one or more other types of classifications, such as a time-of-day classification.

[0049] In one example, when the processing circuitry 14 detects a lying-standing movement representing a change in body angle from zero to ninety degrees, the processing circuitry 14 can assign a lying-standing category and a corresponding body angle change category to the detected lying-standing movement. When the processing circuitry 14 generates a set of data corresponding to the lying-standing movement, the processing circuitry 14 can save the set of data to each of a plurality of libraries associated with the category assigned to the lying-standing movement.

[0050] For each set of data generated in response to the detection of a change in body position, the processing circuitry system 14 may save that set of data to each of a plurality of libraries associated with a category assigned to the detected change in body position. This allows the processing circuitry system 14 to select one or more libraries from the plurality of libraries for fall risk analysis. That is, the processing circuitry system 14 may determine a fall risk score based on one or more libraries from the plurality of libraries. In some examples, the processing circuitry system 14 may calculate a fall risk sub-score for each of the one or more libraries and calculate a fall risk score based on each corresponding fall risk sub-score. In other words, the processing circuitry system 14 may determine a fall risk evidence level for each of a plurality of measures. A “measure” may correspond to a library in the fall risk database. A “level of evidence” may refer to the amount of evidence that patient 4 is at risk of falling. The processing circuitry system 14 may determine an overall fall risk score for patient 4 based on the fall risk evidence level corresponding to each of the plurality of measures.

[0051] The processing circuitry system 14 can be configured to identify one or more patient parameters based on physiological signals measured by the IMD 10 or other devices. In some examples, the processing circuitry system 14 can be configured to determine the patient's heart rate based on EGM signals measured via one or more electrodes of the IMD 10. In some examples, the processing circuitry system 14 can determine the patient's blood pressure or one or more values ​​corresponding to the patient's blood pressure based on EGM, impedance signals, tissue perfusion signals (e.g., collected by optical sensors), or any combination thereof. Additionally or alternatively, the processing circuitry system 14 is capable of: determining the velocity of one or more body position movements detected in accelerometer signals; identifying the stability of gait identified in accelerometer signals; determining one or more tissue perfusion values ​​identified in optical signals sensed by the IMD 10 via optical sensors; determining one or more other patient parameters based on signals collected by the IMD 10 or other devices; or any combination thereof.

[0052] In some examples, to determine the heart rate of patient 4, processing circuitry system 14 may determine the heart rate based on two or more R waves detected in the EGM collected by IMD 10. For example, the EGM may include one or more R waves, each R wave representing ventricular depolarization of patient 4's heart. The rate of the R waves in the EGM may represent the heart rate of patient 4. Therefore, processing circuitry system 14 may determine the heart rate of patient 4 over a period of time by determining the rate of the R waves in the EGM over that period. In some examples, processing circuitry system 14 may determine the amount of time between a first R wave and a second R wave that is consecutive to the first wave. Based on the amount of time between the first R wave and the second R wave, processing circuitry system 14 may determine the heart rate of patient 4 during the second R wave. Processing circuitry system 14 may calculate the corresponding heart rate for each pair of consecutive R waves in the EGM. Therefore, processing circuitry system 14 may monitor the heart rate of patient 4 over time.

[0053] It is expected that patient 4's blood pressure and / or heart rate will increase in response to body position movements such as sit-to-stand movements. If patient 4's blood pressure and / or heart rate do not increase by at least the expected amount in response to body position movements, patient 4 may experience dizziness shortly after completing a sit-to-stand movement. In some examples, this dizziness may lead to patient 4 losing consciousness and / or falling. Therefore, it may be advantageous for processing circuitry system 14 to analyze sets of data corresponding to each sit-to-stand movement detected in the accelerometer signal. That is, processing circuitry system 14 may analyze a sit-to-stand library in a fall risk database to determine patient 4's fall risk score. When processing circuitry system 14 determines that the increase in heart rate and blood pressure in response to the corresponding sit-to-stand movement has decreased over a period of time or has decreased to below the expected threshold increase, processing circuitry system 14 may determine that the patient is at risk of falling in the future. In addition to or as an alternative to blood pressure and heart rate, processing circuitry system 14 may analyze one or more other patient parameters indicated by the corresponding library of data within the fall risk database to determine patient 4's fall risk score.

[0054] While one or more techniques described herein include generating datasets for fall risk analysis based on the detection of changes in body position and / or changes in body angle, the processing circuitry system 14 may also generate datasets for fall risk analysis based on one or more other events. For example, the processing circuitry system 14 may generate a dataset in response to the detection of a cardiac event such as a premature ventricular contraction (PVC) and save that dataset to a fall risk database. In some examples, the processing circuitry system 14 may assign one or more categories to a dataset generated in response to the detection of a cardiac event and save that dataset to one or more libraries in the fall risk database corresponding to the assigned categories. The processing circuitry system 14 may then analyze one or more libraries to determine a fall risk score for patient 4.

[0055] As discussed above, the processing circuitry 14 may monitor accelerometer signals for one or more body position movements of the patient 4 (e.g., sit-to-stand, lie-to-sit, lie-to-stand, stand-to-sit, stand-to-lie, and sit-to-lie). Alternatively, the processing circuitry 14 may monitor the accelerometer signals to determine the walking distance traveled by the patient 4. In some examples, the processing circuitry 14 may determine the walking distance traveled by the patient 4 after each detected body position movement and store that information in memory.

[0056] The processing circuitry 14 can determine the amount of time taken by the patient 4 to complete each body position movement detected by the processing circuitry 14, based on accelerometer signals measured by the IMD 10. For example, in the case of sit-to-stand movements, the processing circuitry 14 can determine the first time when the patient 4 begins to stand and the second time when the patient 4 finishes standing, based on the accelerometer signals. The difference between the first and second times represents the time taken by the patient 4 to complete the sit-to-stand movement. The processing circuitry 14 can store the amount of time taken to complete each detected body position movement in a fall risk database. Subsequently, the processing circuitry 14 can analyze the amount of time taken by the patient 4 to complete one or more body position changes over a period of time. For example, the processing circuitry 14 can analyze several sit-to-stand movements over a week and determine that the patient 4 is gradually taking longer to complete these sit-to-stand movements. In this example, the processing circuitry 14 can determine that the patient 4 has an increased risk of fall. In some examples, the patient 4 can perform five sit-to-stand (FTSTS) tests, and the processing circuitry 14 can store the amount of time taken by the patient 4 to complete each sit-to-stand movement of the FTSTS.

[0057] The accelerometer signals collected by IMD 10 can indicate one or more changes in body angles (e.g., one or more trunk angular displacements (TADs)). Alternatively or concurrently, when patient 4 walks or runs, the accelerometer signals can indicate patient 4's gait speed (e.g., 4-meter gait speed (4MGS)) and gait stability. Processing circuitry 14 can determine gait stability by tracking the variability of the accelerometer signals over a period of time. Greater variability in the accelerometer signals can indicate greater gait instability in patient 4 and a greater risk of fall.

[0058] The processing circuitry 14 measures the amount of time that patient 4 takes to begin moving (e.g., to begin walking) after completing standing, each time patient 4 stands. This time between standing and moving may be referred to herein as Extended Timed Standing and Walking (ETGUG). If the amount of time patient 4 takes to begin moving after standing increases throughout the entire sit-to-stand movement sequence detected in the accelerometer signal, the processing circuitry 14 can determine that the risk of patient 4 falling is increasing compared to an example where the amount of time patient 4 takes to begin moving after standing remains the same or decreases throughout the entire sit-to-stand movement sequence. For example, when patient 4 pauses after standing, this may indicate that patient 4 is experiencing dizziness after standing, which increases the risk of falling compared to when patient 4 is not experiencing dizziness after standing.

[0059] Relative to the detection of body position movement in the accelerometer signal, the processing circuitry 14 is configured to monitor blood pressure (e.g., PTT), heart rate, changes in tissue perfusion, or any combination thereof, within a time window corresponding to the detection of body position movement (e.g., a time window extending from 5 to 60 seconds after the body position movement). Specifically, the processing circuitry 14 may monitor heart rate recovery and blood pressure recovery following an upright posture challenge.

[0060] In some examples, the processing circuitry 14 may calculate multiple PTT intervals to track the patient 4's blood pressure over a period of time. For example, the processing circuitry 14 is configured to identify multiple PTT intervals against a set of data stored in a fall risk database. In some examples, each of the multiple PTT intervals represents the amount of time between a depolarization indicated by a corresponding portion of the EGM and an impedance characteristic indicated by a corresponding portion of the impedance signal, which occurs after the corresponding depolarization indicated by the corresponding portion of the EGM and before a subsequent depolarization. In some examples, each of the multiple PTT intervals represents the amount of time between a depolarization indicated by a corresponding portion of the EGM and a tissue oxygenation characteristic indicated by a corresponding portion of a tissue oxygenation signal (e.g., StO2 and / or SpO2), which occurs after the corresponding depolarization indicated by the corresponding portion of the EGM and before a subsequent depolarization.

[0061] In any case, PTT can represent a parameter indicating the amount of time it takes for blood to flow from the heart to peripheral locations within the cardiovascular system. For example, EGM indicates the time it takes for the ventricles of patient 4 to contract, forcing blood out of the heart. The impedance signal measured by IMD 10 indicates tissue perfusion in the tissues near IMD 10. Tissue perfusion increases as blood flows toward the tissues near IMD 10 (e.g., during a pulse caused by blood flowing out of patient 4's heart). Furthermore, tissue oxygenation (e.g., StO2 and / or SpO2) increases as blood flows toward the tissues near IMD 10 (e.g., during a pulse). Therefore, the amount of time between the R wave detected in the EGM and the impedance signal characteristic detected in the impedance signal collected by IMD 10 can represent the PTT interval. Alternatively, the amount of time between the R wave detected in the EGM and the tissue oxygenation characteristic detected in the tissue oxygenation signal can represent the PTT interval. The PTT interval can be inversely proportional to blood pressure. For example, a shorter PTT interval indicates a larger blood flow velocity, which indicates higher blood pressure. Alternatively, a longer PTT interval indicates a lower blood flow rate, which suggests lower blood pressure. In any case, a relationship may exist between the patient's blood pressure and the PTT interval. Therefore, the PTT interval value can be used by the processing circuitry system 14 as a substitute or proxy for the blood pressure value.

[0062] The processing circuitry 14 can determine the PTT change value corresponding to a time window associated with a given set of data based on multiple PTT intervals corresponding to that set of data. The PTT change value represents the change between one or more PTT intervals in a first segment of the corresponding time window and one or more PTT intervals in a second segment of the time window. The processing circuitry 14 can calculate a fall risk score corresponding to patient 4 based on the PTT change values ​​corresponding to one or more sets of data stored in a fall risk database.

[0063] Although in one example the IMD 10 takes the form of an ICM, in other examples the IMD 10 takes the form of any combination of an implantable cardioverter defibrillator (ICD), pacemaker, cardiac resynchronization therapy device (CRT-D), neuromodulation device, left ventricular assist device (LVAD), implantable sensor, orthopedic device, or drug pump with intravascular or extravascular leads, as an example. Furthermore, although described in the context of an IMD, the techniques disclosed herein can be implemented by systems that additionally or alternatively include one or more external sensor devices, such as wearable patient monitors, smartwatches, or Fitbits.

[0064] Figure 2 This illustrates one or more technologies according to the present document. Figure 1 A conceptual diagram of an exemplary configuration of the IMD 10 of the medical device system 2. Figure 2 In the example shown, IMD 10 may include a leadless, subcutaneously implantable monitoring device having a housing 15, a proximal electrode 16A, and a distal electrode 16B. The housing 15 may further include a first main surface 18, a second main surface 20, a proximal end 22, and a distal end 24. In some examples, IMD 10 may include one or more additional electrodes 16C, 16D located on one or both main surfaces 18, 20 of IMD 10. The housing 15 encapsulates and protects the electronic circuitry located within IMD 10 from fluids such as bodily fluids. In some examples, an electrical feedthrough provides electrical connections between electrodes 16A to 16D and antenna 26 to the circuitry within housing 15. In some examples, electrode 16B may be formed from an uninsulated portion of the conductive housing 15.

[0065] exist Figure 2 In the example shown, IMD 10 is defined by length L, width W, and thickness or depth D. In this example, IMD10 is in the form of an elongated rectangular prism, where the length L is significantly greater than the width W, and where the width W is greater than the depth D. However, other configurations of IMD 10 are envisioned, such as where the relative proportions of length L, width W, and depth D are... Figure 2 The configurations shown and described differ from those described above. In some examples, the geometry of the IMD 10 may be selected, for example, the width W is greater than the depth D, to allow the IMD 10 to be inserted under the patient's skin using a minimally invasive procedure and to remain in the desired orientation during insertion. Alternatively, the IMD 10 may include radial asymmetry (e.g., a rectangular shape) along the longitudinal axis of the IMD 10, which may help to maintain the device in the desired orientation after implantation.

[0066] In some examples, the spacing between the proximal electrode 16A and the distal electrode 16B can range from about 30 mm to 55 mm, about 35 mm to 55 mm, or about 40 mm to 55 mm, or more generally from about 25 mm to 60 mm. Generally, the IMD 10 can have a length L of about 20 mm to 30 mm, about 40 mm to 60 mm, or about 45 mm to 60 mm. In some examples, the width W of the main surface 18 can range from about 3 mm to 10 mm, and can be any single width or width range between about 3 mm and 10 mm. In some examples, the depth D of the IMD 10 can range from about 2 mm to 9 mm. In other examples, the depth D of the IMD 10 can range from about 2 mm to 5 mm, and can be any single depth or depth range between about 2 mm and 9 mm. In any such example, the IMD 10 is compact enough to be implanted in the subcutaneous space in the pectoral muscle region of the patient 4.

[0067] According to examples of this disclosure, the IMD 10 can have a geometry and dimensions designed for ease of implantation and patient comfort. The volume of the example IMD 10 described in this disclosure can be 3 cubic centimeters (cm³). 3 or smaller, 1.5cm 3 Or smaller or any volume in between. Furthermore, in Figure 2 In the example shown, the proximal end 22 and the distal end 24 are rounded to reduce discomfort and irritation to surrounding tissues once implanted under the skin of patient 4.

[0068] exist Figure 2 In the example shown, when the IMD 10 is inserted into the patient 4, the first principal surface 18 of the IMD 10 faces outward toward the skin, while the second principal surface 20 faces inward toward the muscle tissue of the patient 4. Therefore, the first principal surface 18 and the second principal surface 20 can face in a direction along the sagittal axis of the patient 4 (see...). Figure 1 And because of the size of the IMD 10, the orientation can be maintained during implantation.

[0069] When the IMD 10 is subcutaneously implanted in patient 4, the proximal electrode 16A and distal electrode 16B can be used to sense cardiac EGM (e.g., cardiac ECG). In some examples, the processing circuitry of the IMD 10 can also determine whether the cardiac EGM of patient 4 indicates an arrhythmia or other abnormality, and can be used to assess whether the patient 4's medical condition (e.g., heart failure, sleep apnea, or COPD) has changed. The cardiac EGM can be stored in the memory of the IMD 10. In some examples, data derived from the EGM can be transmitted via integrated antenna 26 to another medical device, such as external device 12. In some examples, the IMD 10 can also use one or both of electrodes 16A and 16B to collect one or more impedance signals (e.g., subcutaneous tissue impedance) during impedance measurements performed by the IMD 10. In some examples, such impedance values ​​detected by the IMD 10 can reflect impedance values ​​associated with contact between electrodes 16A, 16B and the target tissue of patient 4. Additionally, in some examples, the communication circuitry of the IMD 10 can use electrodes 16A and 16B for tissue conductance communication (TCC) with external device 12 or another device.

[0070] exist Figure 2In the example shown, the proximal electrode 16A is located near the proximal end 22 of the IMD 10, while the distal electrode 16B is located near the distal end 24 of the IMD. In this example, the distal electrode 16B is not limited to a flat, outward-facing surface, but can extend from the first main surface 18 around the circular edge 28 or end surface 30 and extend into the second main surface 20 in a three-dimensional curved configuration. As shown, the proximal electrode 16A is located on the first main surface 18 and is substantially flat and outward-facing. However, in other examples not shown here, both the proximal electrode 16A and the distal electrode 16B can be configured similarly to... Figure 2 The proximal electrode 16A shown, or both, can be configured similarly to Figure 2 The distal electrode 16B is shown. In some examples, additional electrodes 16C and 16D may be positioned on one or both of the first main surface 18 and the second main surface 20, such that the IMD 10 includes a total of four electrodes. Any one of electrodes 16A-16D may be formed of a biocompatible conductive material. For example, any one of electrodes 16A-16D may be formed of stainless steel, titanium, platinum, iridium, or alloys thereof. Furthermore, the electrodes of the IMD 10 may be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may also be used.

[0071] exist Figure 2 In the example shown, the proximal end 22 of the IMD 10 includes a head assembly 32 having one or more of a proximal electrode 16A, an integrated antenna 26, an anti-migration protrusion 34, and a suture hole 36. The integrated antenna 26 is located on the same main surface as the proximal electrode 16A (e.g., a first main surface 18) and may be integral with the head assembly 32. In other examples, the integrated antenna 26 may be formed on a main surface opposite the proximal electrode 16A, or in other examples, the integrated antenna may be incorporated within the housing 15 of the IMD 10. The antenna 26 may be configured to transmit or receive electromagnetic signals for communication. For example, the antenna 26 may be configured via inductive coupling, electromagnetic coupling, tissue conductivity, near-field communication (NFC), radio frequency identification (RFID), or Bluetooth. ® Wi-Fi ® Or other proprietary or non-proprietary wireless telemetry communication schemes to transmit signals to or receive signals from the programmer. Antenna 26 can be coupled to the communication circuitry of IMD 10 that can drive antenna 26 to transmit signals to external device 12, and can transmit signals received from external device 12 to the processing circuitry of IMD 10 through the communication circuitry.

[0072] IMD 10 may include several features for maintaining its proper position once it is subcutaneously implanted in the patient. For example, such as Figure 2As shown, the housing 15 may include an anti-migration protrusion 34 positioned near the integrated antenna 26. The anti-migration protrusion 34 may include a plurality of ridges or protrusions extending away from the first main surface 18 and may help prevent longitudinal movement of the IMD 10 after implantation in the patient 4. In other examples, the anti-migration protrusion 34 may be located on the main surface opposite the proximal electrode 16A and / or the integrated antenna 26. Additionally, in Figure 2 In the illustrated example, the head assembly 32 includes a suture hole 36, which provides another means of securing the IMD 10 to the patient to prevent movement after insertion. In the illustrated example, the suture hole 36 is located near the proximal electrode 16A. In some examples, the head assembly 32 may include a molded head assembly made of polymer or plastic material, which may be integrated with or detached from the main portion of the IMD 10.

[0073] Electrodes 16A and 16B can be used to sense cardiac EGM, as described above. In some examples, in addition to or in place of electrodes 16A and 16B, additional electrodes 16C and 16D can be used to sense subcutaneous tissue impedance. In some examples, the processing circuitry of IMD 10 can determine the impedance value of patient 4 based on signals received from at least two of electrodes 16A to 16D. For example, the processing circuitry of IMD 10 can generate one of a current or voltage signal, deliver the signal through two or more selected electrodes 16A to 16D, and measure the other of the resulting current or voltage. The processing circuitry of IMD 10 can determine the impedance value based on the delivered current or voltage and the measured voltage or current.

[0074] exist Figure 2In the example shown, IMD 10 includes a light emitter 38 positioned on the housing 15 of IMD 10, and a proximal light detector 40A and a distal light detector 40B (collectively, "light detector 40"). Light detector 40A may be positioned at a distance S from the light emitter 38, while the distal light detector 40B may be positioned at a distance S+N from the light emitter 38. In other examples, IMD 10 may include only one of light detectors 40A and 40B, or may include additional light emitters and / or additional light detectors. In summary, the light emitter 38 and light detectors 40A and 40B may include optical sensors that can be used in the techniques described herein to determine the StO2 or SpO2 value of patient 4. Although the light emitter 38 and photodetectors 40A, 40B are described herein as being positioned on the housing 15 of the IMD 10, in other examples, one or more of the light emitter 38 and photodetectors 40A, 40B may be positioned on the housing of another type of IMD within the patient 4, such as a transvenous, subcutaneous, or extravascular pacemaker or ICD, or connected to such a device via leads. The light emitter 38 includes a light source, such as an LED, which can emit light of one or more wavelengths within the visible (VIS) and / or near-infrared (NIR) spectrum. For example, the light emitter 38 may emit light of one or more of about 660 nanometers (nm), 720 nm, 760 nm, 800 nm, or any other suitable wavelength.

[0075] In some examples, the technique used to determine StO2 may involve using a light emitter 38 to emit light of one or more VIS wavelengths (e.g., approximately 660 nm) and one or more NIR wavelengths (e.g., approximately 850 nm–890 nm). The combination of VIS and NIR wavelengths can help the processing circuitry of the IMD 10 distinguish between oxyhemoglobin and deoxyhemoglobin in the tissue of patient 4, because as hemoglobin becomes less oxygenated, the attenuation of VIS light increases and the attenuation of NIR light decreases. By comparing the amount of VIS light detected by photodetectors 40A and 40B with the amount of NIR light detected by photodetectors 40A and 40B, the processing circuitry of the IMD 10 can determine the relative amounts of oxyhemoglobin and deoxyhemoglobin in the tissue of patient 4. For example, if the amount of oxyhemoglobin in the tissue of patient 4 decreases, the amount of VIS light detected by photodetectors 40A and 40B increases, and the amount of NIR light detected by photodetectors 40A and 40B decreases. Similarly, if the amount of oxyhemoglobin in the tissue of patient 4 increases, the amount of VIS light detected by photodetectors 40A and 40B decreases, and the amount of NIR light detected by photodetectors 40A and 40B increases.

[0076] like Figure 2As shown, light emitter 38 can be positioned on headrest assembly 32; however, in other examples, one or both of photodetectors 40A, 40B can be additionally or alternatively positioned on headrest assembly 32. In some examples, light emitter 38 can be positioned on a middle section of IMD 10, such as the portion between proximal end 22 and distal end 24. Although light emitter 38 and photodetectors 40A, 40B are shown positioned on the first main surface 18, they can also alternatively be positioned on the second main surface 20. In some examples, the IMD can be implanted such that when the IMD 10 is implanted, light emitter 38 and photodetectors 40A, 40B face inward toward the muscles of patient 4, which can help minimize interference from background light from outside the patient 4's body. Photodetectors 40A, 40B can include glass or sapphire windows, as shown below. Figure 4B The aforementioned portion may be positioned beneath the portion of the housing 15 of the IMD 10 made of glass, sapphire, or other transparent or translucent material.

[0077] During the technique used to determine the StO2 value of patient 4, light emitter 38 can emit light toward a target site in patient 4. When IMD 10 is implanted in patient 4, the target site can typically include the interstitial space surrounding IMD 10. Light emitter 38 can emit light directionally because it can direct signals to one side of IMD 10, such as when light emitter 38 is positioned on one side of IMD 10 including the first master surface 18. The target site can include subcutaneous tissue adjacent to IMD 10 in patient 4.

[0078] Techniques for determining StO2 values ​​can be based on the optical properties of blood-perfused tissues, which vary according to the relative amounts of oxyhemoglobin and deoxyhemoglobin in the tissue microcirculation. These optical properties are at least partly attributed to the different optical absorption spectra of oxyhemoglobin and deoxyhemoglobin. Therefore, the oxygen saturation level of patient tissues can affect the amount of light absorbed by the blood in the tissue adjacent to IMD 10 and the amount of light reflected by the tissue. Photodetectors 40A and 40B can each receive light reflected from the light emitter 38 and generate electrical signals indicating the intensity of the light detected by photodetectors 40A and 40B. The processing circuitry of IMD 10 can then evaluate the electrical signals from photodetectors 40A and 40B to determine the StO2 value of patient 4.

[0079] In some examples, the difference between the electrical signals generated by photodetectors 40A and 40B can enhance the accuracy of the StO2 value determined by IMD 10. For example, because tissue absorbs some of the light emitted by light emitter 38, the intensity of light reflected by the tissue decreases with increasing distance (and amount of tissue) between light emitter 38 and photodetectors 40A and 40B. Therefore, because photodetector 40B is farther from light emitter 38 (distance S+N) than photodetector 40A (distance S), the intensity of light detected by photodetector 40B should be less than the intensity of light detected by photodetector 40A. Since detectors 40A and 40B are very close to each other, the difference between the intensity of light detected by photodetector 40A and the intensity of light detected by photodetector 40B should be attributed solely to the difference in distance from light emitter 38. In some examples, in addition to the electrical signals themselves, the processing circuitry of IMD 10 can use the difference between the electrical signals generated by photodetectors 40A and 40B to determine the StO2 value of patient 4.

[0080] In some examples, the IMD 10 may include one or more additional sensors, such as one or more accelerometers. Figure 2 (Not shown in the image). Such accelerometers can be 3D accelerometers configured to generate signals indicative of one or more types of patient movement, such as the patient's whole body movement (e.g., motion), patient posture, movement associated with heartbeat, or coughing, rales, or other respiratory abnormalities. One or more parameters (e.g., impedance, EGM) monitored by the IMD 10 may fluctuate in response to changes in one or more of these types of movement. For example, changes in parameter values ​​may sometimes be attributed to increased patient movement (e.g., exercise or other physical movement compared to immobility) or to changes in patient posture, without necessarily to changes in the medical condition. Therefore, in some methods for identifying or tracking the medical condition of patient 4, it may be advantageous to consider such fluctuations when determining whether changes in parameters indicate changes in the medical condition.

[0081] In some examples, the IMD 10 can perform SpO2 measurements using a light emitter 38 and a photodetector 40. For example, the IMD 10 can perform SpO2 measurements by emitting light using the light emitter 38, using one or more VIS wavelengths, one or more NIR wavelengths, or a combination of one or more VIS wavelengths and one or more NIR wavelengths. By comparing the amount of VIS light detected by photodetectors 40A and 40B with the amount of NIR light detected by photodetectors 40A and 40B, the processing circuitry of the IMD 10 can determine the relative amounts of oxyhemoglobin and deoxyhemoglobin in the patient's tissue. For example, if the amount of oxyhemoglobin in the patient's tissue decreases, the amount of VIS light detected by photodetectors 40A and 40B increases, and the amount of NIR light detected by photodetectors 40A and 40B decreases. Similarly, if the amount of oxyhemoglobin in the tissue of patient 4 increases, the amount of VIS light detected by photodetectors 40A and 40B decreases, and the amount of NIR light detected by photodetectors 40A and 40B increases.

[0082] While both SpO2 and StO2 measurements can employ IMD 10 optical sensors (e.g., light emitter 38 and photodetector 40) to emit and sense light, SpO2 measurements may consume significantly more energy than StO2 measurements. In some examples, SpO2 measurements may consume up to three orders of magnitude (1,000 times) more power than StO2 measurements. This inconsistency in energy consumption is due to factors including the fact that SpO2 measurements may require light emitter 38 to be activated for up to 30 seconds, while StO2 measurements may require light emitter 38 to be activated for up to 5 seconds. Additionally, SpO2 measurements may require sampling rates up to 70 Hz, while StO2 measurements may require sampling rates up to 4 Hz.

[0083] Figure 3 This illustrates one or more technologies according to the present document. Figure 1 and Figure 2 A functional block diagram of an exemplary configuration of IMD 10. (See attached diagram.) Figure 3 As seen, IMD 10 includes electrodes 16A-16D (collectively referred to as "electrodes 16"), antenna 26, processing circuitry system 50, sensing circuitry system 52, communication circuitry system 54, memory 56, switching circuitry system 58, sensor 62 including photodetector 40 and motion sensor 42, and power supply 64. Memory 56 is configured to store a fall risk database 66 including libraries 68A-68N (collectively referred to as "library 68"). Although memory 56 is shown as storing the fall risk database 66, one or more other memories may additionally or alternatively store at least a portion of the fall risk database 66. For example, Figure 1The memory of the external device 12 may be configured to store at least a portion of the fall risk database 66. In some examples, another memory may be configured to store at least a portion of the fall risk database 66.

[0084] The processing circuitry system 50 may include a fixed-function circuitry system and / or a programmable processing circuitry system. The processing circuitry system 50 may include, for example, a microprocessor, DSP, ASIC, FPGA, equivalent discrete or integrated logic circuitry system, or a combination of any of the foregoing devices or circuitry systems. Therefore, the processing circuitry system 50 may include any suitable structure, whether hardware, software, firmware, or any combination thereof, to perform the functions assigned herein to IMD 10. In some examples, the processing circuitry system 50 may represent... Figure 1 At least a portion of the processing circuitry 14 is required, but this is not necessary. In some examples, the processing circuitry 50 may be integrated with... Figure 1 The processing circuit system 14 is separate.

[0085] Sensing circuitry 52 and communication circuitry 54 can be selectively coupled to electrode 16 via switching circuitry 58, which can be controlled by processing circuitry 50. Sensing circuitry 52 can monitor signals from electrode 16 to monitor cardiac electrical activity (e.g., to generate EGM) and / or subcutaneous tissue impedance, which indicates at least some aspects of cardiac activity and / or respiratory patterns in patient 4. Sensing circuitry 52 can also monitor signals from sensor 62, which may include a photosensor 40, a motion sensor 42, and any additional sensors that can be positioned on IMD 10. In some examples, sensing circuitry 52 may include one or more filters and amplifiers for filtering and amplifying signals received from electrode 16 and / or sensor 62.

[0086] The communication circuitry 54 may include any suitable hardware, firmware, software, or any combination thereof for communicating with another device, such as external device 12, or another device or sensor, such as a pressure sensing device. Under the control of the processing circuitry 50, the communication circuitry 54 may receive downlink telemetry from external device 12 or another device, and transmit uplink telemetry to said device, via, for example, an internal or external antenna of antenna 26. Additionally, the processing circuitry 50 may communicate with external devices (e.g., external device 12) and devices such as Medtronic CareLink developed by Medtronic Ltd. of Dublin, Ireland. ® Computer networks, such as networks, communicate with networked computing devices.

[0087] Clinicians or other users can retrieve data from the IMD 10 using external device 12 or by using another local or networked computing device configured to communicate with the processing circuitry 50 via the communication circuitry 54. Clinicians can also use external device 12 or another local or networked computing device to program the parameters of the IMD 10.

[0088] In some examples, memory 56 includes computer-readable instructions that, when executed by processing circuitry system 50, cause IMD 10 and processing circuitry system 50 to perform various functions attributed herein to IMD 10 and processing circuitry system 50. Memory 56 may include one or both of short-term memory and long-term memory. Memory may include, for example, RAM, DRAM, SRAM, magnetic disk, optical disk, flash memory, or various forms of EPROM or EEPROM. In some examples, memory is used to store program instructions executed by processing circuitry system 50.

[0089] Memory 56 is configured to store at least a portion of a fall risk database 66. The fall risk database 66 includes multiple sets of data. In some examples, each set of data may correspond to an event detected in the data collected by IMD 10. For example, at least one set of data may correspond to a change in body position or body angle detected in accelerometer signals collected by IMD 10. Alternatively, at least one set of data may correspond to a cardiac event detected in one or more signals (e.g., EMG, impedance signals, optical signals, or any combination thereof) collected by IMD 10.

[0090] In some examples, each of the multiple sets of data includes a corresponding portion of one or more signals, wherein the corresponding portion of the one or more signals corresponds to a corresponding time window. For example, a first set of data may include a set of signals corresponding to a first time window, and a second set of data may include a set of signals corresponding to a second time window, wherein the first time window is different from the second time window. The first set of data may include at least one of the same signals as the second set of data. Therefore, the first set of data and the second set of data may include at least one overlapping signal, but the first set of data corresponds to the first time window, and the second set of data corresponds to the second time window.

[0091] The fall risk database 66 includes libraries 68. In some examples, each library in library 68 may correspond to one or more categories. A set of data may be categorized into library 68 based on one or more categories associated with it. For example, library 68A may be associated with the sitting-to-standing category, and library 68B may be associated with the lying-to-standing category. Libraries 68C-68N may each be associated with one or more categories. When a first set of data is associated with the sitting-to-standing category, memory 56 may store the first set of data in library 68A. In some examples, the processing circuitry (e.g., Figure 1 The processing circuitry system 14) can analyze one or more libraries 68 to determine a fall risk score associated with patient 4.

[0092] Power source 64 is configured to deliver operating power to components of IMD 10. Power source 64 may include a battery and power generation circuitry for generating operating power. In some examples, the battery is rechargeable to allow for long-term operation. In some examples, recharging is achieved through near-side inductive interaction between an external charger and an inductive charging coil within external device 12. Power source 64 may include any one or more of a variety of battery types, such as nickel-cadmium batteries and lithium-ion batteries. Non-rechargeable batteries may be selected to last for several years, while rechargeable batteries may be inductively charged from an external device, for example, on a daily or weekly basis.

[0093] Figure 4A and Figure 4B Two additional exemplary IMDs are shown according to one or more technologies described herein, the exemplary IMDs being compatible with... Figures 1 to 3 The IMD 10 is basically similar but may include one or more additional features. Figure 4A and Figure 4B The components do not have to be drawn to scale; instead, they can be enlarged to show details. Figure 4A This is a top-view block diagram of an example configuration of IMD 10A. Figure 4B This is a block diagram of a side view of an exemplary IMD 10B, which may include the insulating layer described below.

[0094] Figure 4A It shows that it can be basically similar to Figure 1 A conceptual diagram of another exemplary IMD 10 of IMD 10A. Besides Figures 1 to 3 In addition to the components shown, Figure 4A The example of IMD 10 shown may also include a body portion 72 and an attachment plate 74. The attachment plate 74 may be configured to mechanically couple the head assembly 32 to the body portion 72 of the IMD 10A. The body portion 72 of the IMD 10A may be configured to accommodate... Figure 3One or more of the internal components of the IMD 10 shown are included, such as the internal components of the processing circuitry 50, the sensing circuitry 52, the communication circuitry 54, the memory 56, the switching circuitry 58, the internal components of the sensor 62, and the power supply 64. In some examples, the body portion 72 may be formed of one or more of titanium, ceramic, or any other suitable biocompatible material.

[0095] Figure 4B It shows that it can include essentially similar Figure 1 A conceptual diagram of an exemplary IMD 10B component of IMD 10. Besides... Figure 1-3 In addition to the components shown, Figure 4B The example of the IMD 10B shown may also include a wafer-level insulating cover 76, which can help isolate electrical signals transmitted between the electrodes 16A-16D and / or photodetectors 40A, 40B on the housing 15B and the processing circuitry 50. In some examples, the insulating cover 76 may be positioned over the open housing 15 to form a housing for the components of the IMD 10B. One or more components of the IMD 10B (e.g., antenna 26, light emitter 38, photodetectors 40A, 40B, processing circuitry 50, sensing circuitry 52, communication circuitry 54, switching circuitry 58, and / or power supply 64) may be formed on the underside of the insulating cover 76, for example, using flip-chip technology. The insulating cover 76 may be flipped onto the housing 15B. When flipped and placed onto the housing 15B, the components of the IMD 10B formed on the underside of the insulating cover 76 may be positioned within a gap 78 defined by the housing 15B.

[0096] Figure 5 This is a block diagram illustrating an exemplary configuration of components of an external device 12 according to one or more technologies of this disclosure. Figure 5 In the example, external device 12 includes a processing circuitry 80, a communication circuitry 82, a memory 84, a user interface 86, and a power supply 88. Memory 84 is configured to store a fall risk database 66, including library 68. Although memory 86 is shown as storing the fall risk database 66, one or more other memories may additionally or alternatively store at least a portion of the fall risk database 66. For example, memory 56 of IMD 10 may be configured to store at least a portion of the fall risk database 66. In some examples, another memory may be configured to store at least a portion of the fall risk database 66.

[0097] The processing circuitry system 80 may include fixed-function circuitry systems and / or programmable processing circuitry systems. The processing circuitry system 80 may include, for example, a microprocessor, DSP, ASIC, FPGA, equivalent discrete or integrated logic circuitry systems, or a combination of any of the foregoing devices or circuitry systems. Therefore, the processing circuitry system 80 may include any suitable structure, whether in hardware, software, firmware, or any combination thereof, to perform the functions attributed herein to external device 12. In some examples, the processing circuitry system 80 may represent... Figure 1 At least a portion of the processing circuitry 14 is required, but this is not necessary. In some examples, the processing circuitry 50 may be integrated with... Figure 1 The processing circuit system 14 is separate.

[0098] The communication circuit system 82 may include any suitable hardware, firmware, software, or any combination thereof for communicating with another device (such as IMD 10). Under the control of the processing circuit system 80, the communication circuit system 82 can receive downlink telemetry from IMD 10 or another device, and send uplink telemetry to it.

[0099] In some examples, memory 84 includes computer-readable instructions that, when executed by processing circuitry 80, cause external device 12 and processing circuitry 80 to perform various functions attributed herein to IMD 10 and processing circuitry 80. Memory 84 may include one or both of short-term memory and long-term memory. Memory may include, for example, RAM, DRAM, SRAM, magnetic disk, optical disk, flash memory, or various forms of EPROM or EEPROM. In some examples, memory is used to store program instructions executed by processing circuitry 80. Memory 84 may be used by software or applications running on external device 12 to temporarily store information during program execution. In some examples, fall risk database 66 may include one or more sets of data received from IMD 10 and categorized into library 68.

[0100] The data exchanged between the external device 12 and the IMD 10 may include operating parameters. The external device 12 may transmit data including computer-readable instructions that, when implemented by the IMD 10, can control the IMD 10 to change one or more operating parameters and / or export collected data. For example, the processing circuitry 80 may transmit an instruction to the IMD 10 requesting it to output collected data (e.g., data corresponding to one or both of ECG and accelerometer signals) to the external device 12. Furthermore, the external device 12 can receive the collected data from the IMD 10 and store it in memory 84. Alternatively, the processing circuitry 80 may export an instruction to the IMD 10 requesting it to update the electrode assembly for stimulation or sensing.

[0101] Users such as clinicians or patients 4 can interact with external device 12 through user interface 86. User interface 86 includes a display (not shown), such as an LCD or LED display or other type of screen, which the processing circuitry system 80 can utilize to present information related to IMD 10 (e.g., EGM signals, impedance signals, motion signals, fall risk scores associated with patient 4 over time, or any combination thereof, obtained from at least one electrode or at least one combination of electrodes). Additionally, user interface 86 may include input mechanisms for receiving input from the user. Input mechanisms may include any one or more of the following: buttons, a keypad (e.g., an alphanumeric keypad), a peripheral pointing device, a touchscreen, or another input mechanism that allows the user to navigate and input through the user interface presented by the processing circuitry system 80 of external device 12. In other examples, user interface 86 may also include an audio circuitry system for providing auditory notifications, instructions, or other sounds to patient 4, receiving voice commands from patient 4, or both. Memory 84 may contain instructions for operating user interface 86 and for managing power supply 88.

[0102] Power source 88 is configured to deliver operating power to components of external device 12. Power source 88 may include a battery and a power generation circuit for generating operating power. In some examples, the battery is rechargeable to allow for long-term operation. Recharging can be achieved by electrically coupling power source 88 to a bracket or plug connected to an alternating current (AC) outlet. Alternatively, recharging can be achieved through near-end inductive interaction between an external charger and an inductive charging coil within external device 12. In other examples, conventional batteries (e.g., nickel-cadmium or lithium-ion batteries) may be used. Furthermore, external device 12 may be directly coupled to an AC outlet for operation.

[0103] Figure 6This is a block diagram illustrating an example system according to one or more techniques described herein. The example system includes an access point 90, a network 92, an external computing device such as a server 94, and one or more other computing devices 100A to 100N, which can be coupled to an IMD 10, an external device 12, and a processing circuitry system 14 via the network 92. In this example, the IMD 10 can communicate with the external device 12 via a first wireless connection and with the access point 90 via a second wireless connection using a communication circuitry system 54. Figure 6 In the example, access point 90, external device 12, server 94 and computing device 100A–100N are interconnected and can communicate with each other via network 92.

[0104] Access point 90 may include a device connected to network 92 via any of a variety of connections, such as dial-up, digital subscriber line (DSL), or cable modem connections. In other examples, access point 90 may be coupled to network 92 via different forms of connection, including wired or wireless connections. In some examples, access point 90 may be a user device that can be co-located with the patient, such as a tablet or smartphone. As discussed above, IMD 10 may be configured to transmit data to external device 12, such as one or more sets of data to be analyzed in fall risk analysis. Furthermore, access point 90 may, for example, periodically or in response to commands from the patient or network 92, query IMD 10 to retrieve parameter values ​​determined by the processing circuitry system 50 of IMD 10 or other operational or patient data from IMD 10. Access point 90 may then transmit the retrieved data to server 94 via network 92.

[0105] In some cases, server 94 can be configured to provide a secure storage site for data already collected from IMD 10 and / or external device 12. In some cases, server 94 can aggregate data in web pages or other documents for viewing by trained professionals (e.g., clinicians) via computing devices 100A–100N. Figure 6 One or more aspects of the system shown can be used in a manner similar to Medtronic CareLink, developed by Medtronic, a company based in Dublin, Ireland. ® The network provides general network technologies and functions for implementation.

[0106] Server 94 may include processing circuitry system 96. Processing circuitry system 96 may include fixed-function circuitry system and / or programmable processing circuitry system. Processing circuitry system 96 may include any one or more microprocessors, controllers, DSPs, ASICs, FPGAs, or equivalent discrete or analog logic circuitry systems. In some examples, processing circuitry system 96 may include multiple components (such as any combination of one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs), as well as other discrete or integrated logic circuitry systems. The functionality attributed herein to processing circuitry system 96 may be embodied in software, firmware, hardware, or any combination thereof. In some examples, by way of example, processing circuitry system 96 may perform one or more techniques described herein based on one or more sets of data received from IMD 10.

[0107] Server 94 may include memory 98. Memory 98 includes computer-readable instructions that, when executed by processing circuitry 96, cause IMD 10 and processing circuitry 96 to perform various functions attributed to IMD 10 and processing circuitry 96 herein. Memory 98 may include any volatile, non-volatile, magnetic, optical, or electrical medium, such as RAM, ROM, NVRAM, EEPROM, flash memory, or any other digital medium.

[0108] In some examples, one or more of the computing devices 100A-100N (e.g., device 100A) may be tablet computers or other smart devices located with the clinician, which the clinician can program to receive alerts and / or query IMD 10. For example, the clinician may access data corresponding to any one or more of EGM, impedance signals, tissue perfusion signals, accelerometer signals, and other types of signals collected by IMD 10 through device 100A when patient 4 is between clinician visits, to examine the status of medical conditions such as fall risk scores. In some examples, the clinician may input instructions for medical interventions for patient 4 into an app in device 100A, such as based on a fall risk score determined by IMD 10, external device 12, processing circuitry system 14, or any combination thereof, or based on other patient data known to the clinician. Device 100A can then transmit instructions for medical intervention to another computing device 100A to 100N (e.g., device 100B) located on patient 4 or patient 4's caregiver. For example, such instructions for medical intervention may include instructions to change medication dosage, timing, or selection, to schedule a clinician visit, or to seek medical care. In another example, device 100B may generate an alert for patient 4 based on the status of patient 4's fall risk score determined by IMD 10, external device 12, processing circuitry system 14, or any combination thereof, which allows patient 4 to proactively seek medical care before receiving instructions for medical intervention. In this way, patient 4 can be authorized to take action as needed to address his or her medical condition, which can help improve patient 4's clinical outcomes.

[0109] Figure 7 These are graphs illustrating accelerometer signal curves 710 and physiological parameter curves 720 according to one or more techniques described herein. Figure 7 As observed, the accelerometer signal curve 710 changes from accelerometer signal value A1 to accelerometer signal value A2 during the time interval from time T1 to time T2. Subsequently, the physiological parameter curve 720 increases from parameter value P1 to parameter value P2 during the time interval from time T3 to time T4.

[0110] The change in accelerometer signal value A1 to A2 in accelerometer signal graph 710 can represent the patient 4's body position movement or body angle movement. In some examples, accelerometer signal graph 710 can represent a graph of one axis of the triaxial accelerometer signal generated by motion sensor 42. In some examples, accelerometer signal graph 710 can indicate the magnitude of the vectors of two or more axes of the accelerometer signal generated by motion sensor 42. In any case, accelerometer signal graph 710 can be substantially constant at accelerometer signal value A1 before time T1, and accelerometer signal graph 710 can be substantially constant at accelerometer signal value A2 after time T2, where value A2 is lower than value A1. Processing circuitry system 14 can determine that the change in accelerometer signal from A1 to A2 represents a change in body position, and processing circuitry system 14 can generate a set of data including one or more signals indicating physiological parameters represented by physiological parameter graph 720.

[0111] In some cases, in response to a change in accelerometer signal curve 710 from accelerometer signal value A1 to accelerometer signal value A2, physiological parameter curve 720 increases from parameter value P1 to parameter value P2. Since the change from accelerometer signal value A1 to accelerometer signal value A2 can represent a change in the body position of patient 4, the physiological parameter change from parameter value P1 to parameter value P2 can occur in response to a body position movement detected by the processing circuitry system 14 in the accelerometer signal curve 710. In some examples, the processing circuitry system 14 can analyze the physiological parameter change from parameter value P1 to parameter value P2 to determine whether the physiological parameter change meets the expected parameter change. For example, the "expected" parameter change can represent the minimum change in parameter value in response to a change in body position indicated by the accelerometer signal curve 710 from accelerometer signal value A1 to accelerometer signal value A2. When the difference between P1 and P2 is greater than the expected parameter change, the processing circuit system 14 can determine that the parameter change from P1 to P2 meets the expected parameter change. When the difference between P1 and P2 is not greater than the expected parameter change, the processing circuit system 14 can determine that the parameter change from P1 to P2 does not meet the expected parameter change.

[0112] In some examples, the physiological parameter graph 720 may indicate parameters such as blood pressure, heart rate, tissue perfusion, motion data, or gyroscopic data. When a change in body position, indicated by a change from accelerometer signal value A1 to accelerometer signal value A2, represents a sit-to-stand or lie-to-stand body movement, it can be expected that some physiological parameters such as blood pressure and heart rate will increase to compensate for the increased effort associated with these body position movements. Additionally, the processing circuitry 14 may determine the variation in the time taken by patient 4 to complete a body position movement (e.g., the time from T1 to T2) across a series of body position movements of the same type. The processing circuitry 14 may determine a fall risk score associated with patient 4 based on the change in the amount of time taken to complete a body position movement. For example, patient 4 may complete a series of sit-to-stand movements. If the amount of time taken by patient 4 increases from the beginning of the sequence to the end of the sit-to-stand movement sequence, the processing circuitry 14 may determine that patient 4 has an increased risk of fall.

[0113] Figure 8 This is a flowchart illustrating exemplary operations for generating data that can be analyzed to determine a fall risk score, according to one or more techniques disclosed herein. Figure 8 about Figures 1 to 6 The IMD 10, external device 12, and processing circuit system 14 are described. However, Figure 8 The technology can be implemented by different components of the IMD 10, external devices 12, processing circuitry 14, or by additional or alternative medical device systems. Processing circuitry 14 in... Figure 1 The diagram is conceptually shown as a processing circuitry system separate from, but potentially including, the IMD 10 and / or the external device 12. Generally, the techniques disclosed herein can be performed by one or more devices of a system, such as a processing circuitry system 14 including one or more devices that provide signals, or a processing circuitry system of one or more devices that does not include sensors but still analyzes signals using the techniques described herein. For example, another external device ( Figure 1 (Not shown) may include at least a portion of processing circuitry system 14, and the other external device is configured to communicate remotely with IMD 10 and / or external device 12 via a network.

[0114] The processing circuitry system 14 can identify one or more events based on data collected by the IMD 10 and / or other medical devices or sensors. When identified by the processing circuitry system 14, these events can represent reference points for analyzing the data collected by the IMD 10 and / or other devices. For example, the processing circuitry system 14 can determine the time of occurrence of each identified event and generate a set of data corresponding to each corresponding event. Additionally, the processing circuitry system 14 can classify each identified event according to one or more categories and classify the set of data corresponding to each event into one or more libraries associated with the one or more categories assigned to the corresponding event. The processing circuitry system 14 can then analyze these data sets to determine a fall risk score.

[0115] The processing circuitry 14 can identify an event (802) based on one or more signals collected by the medical device. In some examples, the one or more signals include accelerometer signals, impedance signals (e.g., subcutaneous impedance signals, intrathoracic impedance signals, and / or intracardiac impedance signals), and the event represents a body position movement event identified by the processing circuitry 14 in the accelerometer signal. In some examples, the one or more signals include EGM, impedance signals (e.g., subcutaneous impedance signals, intrathoracic impedance signals, and / or intracardiac impedance signals), tissue oxygenation signals, or any combination thereof, and the event represents a cardiac event identified by the processing circuitry 14 in one or more signals. In some examples, the event represents another type of event identified in the accelerometer signal, EGM, impedance signal, tissue oxygenation signal, another signal, or any combination thereof.

[0116] The processing circuitry 14 can identify the time or time period of an event (804). For example, when the event is a change in body position, the processing circuitry 14 can determine the start time and end time of the change in body position from the accelerometer signal. These times represent the time period during which the change in body position occurs. When the event is a cardiac event, the processing circuitry 14 can determine the start time and end time of the cardiac event. These times represent the time period during which the cardiac event occurs.

[0117] The processing circuitry 14 can set a time window (806) based on the time or time period of the event. In some examples, the time window may include at least a portion of time after the time or time period in which the event occurred. In some examples, the entire time window is after the time or time period in which the event occurred. In some examples, at least a portion of the time window occurs before the time or time window in which the event occurred. The processing circuitry 14 can assign one or more categories to the event (808). The one or more categories may indicate an aspect of the detected event. For example, the one or more categories may include at least one type of body position movement, at least one type of body angle change, at least one time of day during which the event was detected, or at least one type of cardiac event.

[0118] The processing circuitry system 14 can save a set of data, including at least some of one or more signals, to a fall risk database in memory, enabling analysis of the fall risk database to determine a fall risk score (810) corresponding to patient 4. For example, the processing circuitry system 14 can save a portion of one or more signals collected by IMD 10 or another medical device, where that portion of each corresponding signal corresponds to a time window selected by the processing circuitry system 14. The processing circuitry system 14 can save this set of data to one or more libraries of the fall risk database based on one or more categories assigned to events associated with it. This allows the processing circuitry system 14 to analyze the one or more libraries to determine a fall risk score associated with patient 4.

[0119] The techniques described in this disclosure can be implemented, at least in part, in hardware, software, firmware, or any combination thereof. For example, aspects of these techniques can be implemented in one or more processors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic QRS circuit systems, and any combination of such components embodied in external devices, such as doctor or patient programmers, simulators, or other devices. The terms “processor” and “processing circuit system” can generally refer to any of the aforementioned logic circuit systems, alone or in combination with other logic circuit systems, or any other equivalent circuit system, alone or in combination with other digital or analog circuit systems.

[0120] For each aspect implemented in software, at least some of the functions of the systems and apparatus described in this disclosure can be embodied in instructions on a computer-readable storage medium, such as RAM, DRAM, SRAM, magnetic disk, optical disk, flash memory, or various forms of EPROM or EEPROM. The instructions can be executed to support one or more aspects of the functions described in this disclosure.

[0121] Additionally, in some aspects, the functions described herein can be housed within dedicated hardware and / or software modules. Describing different features as modules or units is intended to highlight different functional aspects and does not necessarily imply that such modules or units must be implemented by separate hardware or software components. Rather, the functions associated with one or more modules or units can be performed by separate hardware or software components, or integrated into common or separate hardware or software components. Furthermore, this technology can be fully implemented in one or more circuit or logic elements. The technology disclosed herein can be implemented in a variety of devices or apparatuses, including IMDs, external programmers, combinations of IMDs and external programmers, integrated circuits (ICs), or a set of ICs and / or discrete circuit systems residing in IMDs and / or external programmers.

Claims

1. A medical device system, comprising: A medical device comprising a set of sensors configured to sense a set of signals, wherein the set of sensors includes a motion sensor configured to generate motion sensor signals indicative of a patient’s movement, wherein the set of signals includes the motion sensor signals. as well as Processing circuitry system, the processing circuitry system being configured to: The timing of a motor event corresponding to the patient is identified based on at least one of the signals in the set. Set a time window in response to the time when the motion event is identified; as well as A set of data, including one or more of the aforementioned signals, is stored in a fall risk database in memory, enabling analysis of the fall risk database to determine a fall risk score corresponding to the patient, wherein the set of data corresponds to the time window. The processing circuit system is further configured as follows: Assign one or more categories from a set of classifications to the identified motion events; and The set of data is saved to one or more libraries within the fall risk database, wherein each of the one or more libraries corresponds to one of the one or more categories assigned to the movement event.

2. The medical device system of claim 1, wherein the processing circuitry is further configured to analyze data stored in the fall risk database to determine a fall risk score corresponding to the patient, wherein the fall risk score represents the probability that the patient will fall within a time period after the current time.

3. The medical device system of claim 2, wherein, in order to analyze the data stored in the fall risk database, the processing circuitry is configured to: For each of the one or more signals included in the set of data, determine the change of the corresponding signal during the time window; and The fall risk score corresponding to the patient is calculated based on the determined changes corresponding to each of the one or more signals.

4. The medical device system of claim 2, wherein the fall risk database stores multiple sets of data, wherein the multiple sets of data include the set of data, the set of data including the one or more signals corresponding to the time window, wherein each set of data in the multiple sets of data is associated with a corresponding time window and a corresponding category in a set of categories, and wherein, in order to analyze the data stored in the fall risk database, the processing circuitry system is configured to: Select one or more sets of data from the plurality of data stored in the fall risk database, wherein the one or more sets of data are associated with a first category in the set of categories; and The fall risk score corresponding to the patient is calculated based on one or more sets of data associated with the first category.

5. The medical device system of claim 4, wherein, in order to analyze the data stored in the fall risk database, the processing circuitry is further configured to: Select one or more sets of data from the multiple sets of data that correspond to each category in the set of categories; For each set of data associated with a category in the set of categories, calculate a fall risk sub-score associated with the corresponding category; as well as The fall risk score corresponding to the patient is calculated based on the corresponding fall risk sub-score for each category in the set of categories.

6. The medical device system of claim 1, wherein the processing circuit system is further configured to: The motion event is identified based on at least one of the signals in the set of signals.

7. The medical device system of claim 6, wherein the set of classifications includes one or more body position change type classifications, and wherein the processing circuitry system is configured to: Based on the motion sensor signals, identify the type of body position change associated with the motion event; and The body position change type classification in one or more body position change type classifications is assigned to the motion event based on the determined body position change type.

8. The medical device system of claim 7, wherein the one or more body position change type classifications include sitting-standing, lying-standing, lying-sitting, standing-sitting, standing-lying, and sitting-lying categories.

9. The medical device system of claim 6, wherein the processing circuit system is configured to: The motion event is identified as a change in body angle based on the motion sensor signal, wherein the set of classifications includes one or more categories of body angle change types; The patient's body angle before the change in body angle is determined based on the motion sensor signal; The patient's body angle after the change in body angle is determined based on the motion sensor signal; and The body angle classification in one or more body angle classifications is assigned to the identified body angle change based on the patient's body angle before the change in body position and the patient's body angle after the change in body position.

10. The medical device system of claim 6, wherein the processing circuitry is configured to: The motion event is identified as a cardiac event based on the set of signals, wherein the set of classifications includes one or more cardiac event type classifications; Determine the type of cardiac event identified; as well as The cardiac event type classification in one or more cardiac event type classifications is assigned to the identified cardiac event based on the determined type of the cardiac event.

11. The medical device system of claim 10, wherein the one or more cardiac event type classifications include ventricular premature beats (PVC), atrial fibrillation (AF), and ventricular fibrillation.

12. The medical device system of claim 1, wherein the medical device further comprises: One or more electrodes, wherein the one or more electrodes are configured to: Generate an electrogram (EGM), the EGM representing one or more electrical signals corresponding to the patient's heart, wherein the set of signals includes the EGM; and Generate tissue impedance signals, wherein the set of signals includes the tissue impedance signals, and The processing circuit system is further configured as follows: The set of data is saved to the fall risk database, the set of data including a portion of the EGM corresponding to the time window, a portion of the tissue impedance signal corresponding to the time window, and a portion of the motion sensor signal corresponding to the time window.

13. The medical device system of claim 12, wherein the fall risk database stores multiple sets of data including the set of data, and wherein, in order to analyze the data stored in the fall risk database, the processing circuitry is configured to: Select one or more sets of data stored in the plurality of data sets, wherein each set of data selected includes a corresponding portion of the motion sensor signal, a corresponding portion of the EGM, and a corresponding portion of the tissue impedance signal; and The fall risk score corresponding to the patient is calculated based on one or more selected sets of data.

14. The medical device system of claim 1, wherein the time window includes at least a portion of time following the time or period after the occurrence of the motion event.

15. The medical device system of claim 1, wherein at least a portion of the time window occurs before the time or period in which the motion event occurs.

16. A medical method comprising: A set of signals is sensed by a medical device comprising a set of sensors, wherein the set of sensors includes a motion sensor configured to generate motion sensor signals indicative of the patient’s movement, wherein the set of signals includes the motion sensor signals; The processing circuitry system identifies the timing of the patient's motor events based on at least one of the set of signals; The processing circuit system sets a time window in response to the time of recognizing the motion event; as well as The processing circuitry system saves a set of data, including one or more of the set of signals, into a fall risk database in memory, enabling analysis of the fall risk database to determine a fall risk score corresponding to the patient, wherein the set of data corresponds to the time window, wherein saving the set of data further includes: assigning one or more categories from a set of classifications to the identified movement event; and saving the set of data into one or more libraries from a plurality of libraries within the fall risk database, wherein each of the one or more libraries corresponds to one of the one or more categories assigned to the movement event.

17. A non-transitory computer-readable medium, the non-transitory computer-readable medium comprising instructions for causing one or more processors to perform the following operations: A set of signals is sensed by a medical device comprising a set of sensors, wherein the set of sensors includes a motion sensor configured to generate motion sensor signals indicative of the patient’s movement, wherein the set of signals includes the motion sensor signals; The timing of a motor event corresponding to the patient is identified based on at least one of the signals in the set. Set a time window in response to the time when the motion event is identified; as well as A set of data, including one or more of the said set of signals, is saved to a fall risk database in memory, enabling analysis of the fall risk database to determine a fall risk score corresponding to the patient, wherein the set of data corresponds to the time window, wherein the operation of saving the set of data further includes: assigning one or more of the set of categories to the identified movement event; and saving the set of data to one or more of a plurality of libraries within the fall risk database, wherein each of the one or more libraries corresponds to one of the one or more categories assigned to the movement event.

Images