Body stability measurement using pulse transit time

Abstract

One example medical device system and method includes an accelerometer circuit configured to generate at least one signal, a memory, and a processing circuit coupled to the accelerometer circuit and the memory. The processing circuit is configured to determine a first plurality of pulse transit times (PTTs), determine a sit-to-stand transition based on the at least one accelerometer signal, determine a second plurality of PTTs after the sit-to-stand transition based on the sit-to-stand transition occurring, and determine a likelihood that a person, e.g., a patient, can have fallen based on the first plurality of PTTs and the second plurality of PTTs.

Description

TECHNICAL FIELD

[0001] The present disclosure relates generally to systems for monitoring cardiovascular health, and more particularly to systems configured to monitor body stability and predict the likelihood that a person, such as a patient, can fall based on measured cardiovascular metrics. BACKGROUND

[0002] Implantable medical devices (IMDs) include implantable pacemakers and implantable cardioverter-defibrillators (ICDs) and non-therapeutic insertable cardiac monitors (such as Medtronic's LINQ® TM ), as well as external (e.g., wearable medical devices) that record electrocardiogram (EGM) signals for sensing cardiac events (e.g., P-waves and R-waves). Such devices detect episodes of bradycardia, tachycardia, and / or fibrillation from sensed cardiac events, and some devices respond to episodes according to the need for pacing therapy or high-voltage anti-tachyarrhythmia shocks (e.g., cardioversion or defibrillation shocks). These and other medical devices can include or be part of a system that includes sensors that generate other physiology-based signals, such as signals that vary based on patient movement or activity, pulse transit time (PTT), cardiovascular pressure, blood oxygen saturation, edema, or thoracic impedance.

[0003] PTT can be used to determine a measure of pulse wave velocity (PWV). PTT indicates the time it takes for a pulse wave (e.g., a pulse wave of an ECG signal) to travel an estimable distance in a patient. In such instances, the estimable distance traveled by the pulse wave can be divided by the determined PTT value to arrive at a PWV value. SUMMARY

[0004] In general, the present disclosure relates to techniques for determining an increased likelihood that a patient can fall based on measured PTT. More specifically, the present disclosure contemplates a medical system and method that monitors a patient's sit-to-stand transition (e.g., a patient's transition from a sitting position to a standing position) and measures the patient's PTT before and after the sit-to-stand transition. When transitioning from a sitting position to a standing position, a person can experience a change in blood pressure. This change in blood pressure can cause the person to feel dizzy or faint. PTT can be a proxy for blood pressure. By measuring the PTT before and after the sit-to-stand transition, the system and techniques of the present disclosure can determine a likelihood that the patient can fall and facilitate a change in the patient's therapy. PWV values can be used in place of or in addition to PTT values when assessing a patient's body stability, including the patient's likelihood of falling.

[0005] Comparisons of the current values of PTT to corresponding baseline values can be used to determine a status of a patient's fall likelihood. In the techniques described herein, one or more IMDs or external devices can determine PTT and transmit an indication of patient body stability or patient fall likelihood to a remote computer or other device external to the patient. The remote computer or other device can then communicate instructions for medical intervention, such as instructions for changing a medication regimen or physical therapy, to a user device used by the patient or caregiver. In addition to or in lieu of transmitting instructions, the remote computer can control one or more IMDs to deliver therapy, such as stimulation of the heart or a nerve or delivery of medication by a drug pump. In this way, a patient's therapy can be modified as needed to mitigate the risk of the patient falling.

[0006] In some examples, a system is disclosed that includes an accelerometer circuit configured to generate at least one signal; a memory; and a processing circuit coupled to the accelerometer circuit and the memory, the processing circuit configured to: determine a first plurality of pulse transit times of a patient prior to a sit-to-stand transition of the patient; determine, based on the at least one accelerometer signal, whether a sit-to-stand transition of the patient occurred; determine, based on the sit-to-stand transition occurring, a second plurality of pulse transit times of the patient after the sit-to-stand transition of the patient; and determine a likelihood that the patient will fall based on the first plurality of pulse transit times and the second plurality of pulse transit times.

[0007] In other examples, a method is disclosed that includes determining, by a processing circuit, a first plurality of pulse transit times of a patient prior to a sit-to-stand transition of the patient; determining, by the processing circuit and based on at least one accelerometer signal, whether a sit-to-stand transition of the patient occurred; determining, by the processing circuit and based on the sit-to-stand transition occurring, a second plurality of pulse transit times of the patient after the sit-to-stand transition of the patient; and determining a likelihood that the patient will fall based on the first plurality of pulse transit times and the second plurality of pulse transit times.

[0008] In other examples, a non-transitory computer-readable storage medium containing instructions that, when executed by a processing circuit of a device, cause the device to: determine a first plurality of pulse transit times of a patient prior to a sit-to-stand transition of the patient; determine, based on at least one accelerometer signal, whether a sit-to-stand transition of the patient occurred; determine a second plurality of pulse transit times of the patient after the sit-to-stand transition of the patient; and determine a likelihood that the patient will fall based on the first plurality of pulse transit times and the second plurality of pulse transit times.

[0009] 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 apparatus and methods described in detail within the accompanying drawings and the following description. The details of one or more aspects of the disclosure are set forth in the accompanying drawings and the description below. BRIEF DESCRIPTION OF DRAWINGS

[0010] Figure 1 is a conceptual diagram illustrating an example of a medical device system including a leadless implantable medical device and an external device in conjunction with a patient.

[0011] Figure 2 is a conceptual diagram illustrating an example configuration of a leadless implantable medical device of the medical device system of Figure 1

[0012] Figure 3 is a functional block diagram illustrating an example configuration of the leadless implantable medical device of Figure 1

[0013] Figure 4A and 4B is a block diagram illustrating another example leadless implantable medical device that is substantially similar to the implantable medical device of Figure 1

[0014] Figure 5 is a block diagram illustrating an example external device.

[0015] Figure 6 is a block diagram illustrating an example system including an external device, such as a server, and one or more computing devices of the leadless implantable medical device of Figure 1 and the external device of Figure 1

[0016] Figure 7 is a flowchart illustrating an example technique for determining a likelihood of a patient falling.

[0017] Figure 8 is a flowchart illustrating an example technique for an external device to determine medical intervention instructions based on a fall risk.

[0018] Figure 9 is a conceptual diagram illustrating sagittal, vertical, and transverse axes in a three-dimensional coordinate system.

[0019] Figure 10 is a plot illustrating sagittal, vertical, and transverse axis signals produced by an accelerometer during a series of sit-stand and stand-sit movements. DETAILED DESCRIPTION

[0020] ​​​​A medical device system according to certain features or aspects of the present disclosure includes an accelerometer circuit configured to generate a plurality of signals including a sagittal (frontal) axis signal, and a processing circuit configured to detect a sit-to-stand transition and calculate a plurality of PTTs before and after the sit-to-stand transition. The system can determine differences between the plurality of PTTs taken after the sit-to-stand transition and PTTs taken before the sit-to-stand transition, monitor these differences over time to determine a likelihood of a patient falling. Such embodiments can provide, among other things, an objective measure of health change (or lack thereof) to help guide therapy, as PTT measures around a sit-to-stand transition can help determine whether health is improving, declining, or stable. While not limited as such, an understanding of various aspects of the present disclosure can be gained from the discussion below in connection with the drawings. While the present disclosure can provide examples, including identification of medical devices that can be configured to implement the techniques described herein, these identifications are not meant to be limiting. Any device having an accelerometer and / or configured to measure PTTs can be used to implement the techniques of the present disclosure.

[0021] Figure 1 An example medical device system 2 according to devices and methods of certain examples described herein is shown in the context of a patient 4 and a heart 6. The example techniques can be used with a leadless subcutaneously implantable medical device (IMD) 10 that can wirelessly communicate with an external device 12. In some embodiments, the IMD 10 is implanted subcutaneously outside of the thoracic cavity of the patient 4 (e.g., subcutaneously implanted Figure 1 The IMD 10 can be located near the sternum, near the level of the heart 6 or just below the level of the heart 6, e.g., at least partially within the cardiac silhouette. In some examples, the IMD 10 can take the form of a Reveal LINQ™ insertable cardiac monitor (ICM), available from Medtronic pic of Dublin, Ireland. The external device 12 can be a computing device configured for use in a setting such as a home, clinic, or hospital, and can also be configured to communicate with the IMD 10 via wireless telemetry. For example, the external device 12 can be coupled to a remote patient monitoring system, such as Carelink®, available from Medtronic pic of Dublin, Ireland. In some examples, the external device 12 can include a programmer, an external monitor, or a consumer device such as a smart phone or tablet.

[0022] IMD 10 may include multiple electrodes and one or more optical sensors that collectively detect signals that enable processing circuitry (e.g., IMD 10) to determine the PTT value of patient 4 before and after a sitting-to-standing transition, and based on such values, determine the likelihood of patient 4 falling. In some instances, the processing circuitry of IMD 10 may determine that a sitting-to-standing transition has occurred based on accelerometer signals. In some instances, the processing circuitry of IMD 10 may also use ECG signals detected by multiple electrodes to determine the PTT value of patient 4 before and after a sitting-to-standing transition. In other instances, the processing circuitry of IMD 10 may use signals detected by one or more optical sensors positioned on the surface of IMD 10 to determine the PTT value combined with the ECG signal before and after a sitting-to-standing transition.

[0023] Although not necessarily Figure 1 As illustrated in the examples, however, a medical device system configured to implement the techniques of this disclosure may include one or more implanted or external medical devices in addition to or in place of IMD 10. For example, the medical device system may include a pressure-sensing IMD, a vascular ICD, an extravascular ICD, a pacemaker, or other external devices. One or more such devices may generate accelerometer signals and include processing circuitry configured to perform, in whole or in part, the techniques described herein for determining patient stability based on data generated by the accelerometer. The implanted devices may communicate with each other and / or with external device 12, and one of the implanted or external devices may ultimately calculate PTT from at least one of the sagittal, vertical, and transverse axis signals precisely before and after a sitting-to-standing transition.

[0024] For example, accelerometer signals aligned with the patient's sagittal axis can be used to determine sitting-to-standing transitions. This is because 3D accelerometers, such as those in an IMD 10 implanted in the chest, remain relatively stationary throughout the implant's lifespan. This fixed chest position provides the opportunity to monitor upper body changes that occur during various activities. When a patient enters and leaves a chair, reproducible upper body movements (similar to "bending" movements) can be identified using signals generated by the accelerometers.

[0025] After determining the PTT values ​​of patient 4 before and after the sitting-to-standing transition, the processing circuitry of, for example, IMD 10 can calculate a first metric based on a first plurality of PTTs (e.g., the PTT before the sitting-to-standing transition), such as the mean, median, or mode of the first plurality of PTTs. The processing circuitry can also calculate a second metric based on a second plurality of PTTs (e.g., the PTT after the sitting-to-standing transition).

[0026] In some instances, the processing circuitry can also calculate a difference measure between the first and second measures based on the PTT values ​​before and after the sitting-to-standing transition, and compare the difference measure with a corresponding baseline value of the difference measure stored, for example, in the memory of IMD 10, to determine the difference between them. If the difference between one or more PTT difference measures and their corresponding baseline values ​​meets a threshold, the processing circuitry can determine that patient 4 is more likely to fall relative to the time when the baseline value was established.

[0027] Alternatively, the processing circuitry of IMD 10 can calculate the slope of the PTT after the sitting-to-standing transition. For example, the processing circuitry of IMD 10 can compare the PTT slope after the sitting-to-standing transition with a corresponding baseline value (e.g., stored in the memory of IMD 10) to determine the difference. If the difference between the PTT slope after the sitting-to-standing transition and the corresponding baseline meets a threshold, the processing circuitry can determine that patient 4 is more likely to fall relative to the time when the baseline value was established.

[0028] Regardless of whether any such difference meets the threshold, IMD 10 can then wirelessly transmit data associated with the difference measure, the PTT slope after the seating position change, and / or the PTT value to external device 12. IMD 10 can transmit the data associated with the PTT value to external device 12 at predetermined intervals (e.g., daily, weekly, or at any other desired period), or can transmit the data associated with the difference measure, the PTT slope after the seating position change, and / or the PTT value upon request from a user on external device 12.

[0029] In some instances, IMD 10 can be configured to perform a learning phase after implantation in patient 4, wherein IMD 10 determines the baseline value of the difference measure and / or the slope of PTT after sitting-to-standing transitions in patient 4 based on values ​​collected by IMD 10 over a period of time, and stores the baseline value in the memory of IMD 10. For example, IMD 10 can measure PTT before and after each period of sitting (e.g., a week or longer) to determine the baseline value during a period of stable physiological condition in patient 4.

[0030] In other instances, clinicians can select baseline values ​​for patient 4 rather than determining them. Such lists or tables of baseline values ​​can be presented by an application on the clinician's tablet or other smart device, or obtained from a centralized database. Once the clinician has selected appropriate baseline values ​​for patient 4, they can use the application to store these values ​​in IMD 10.

[0031] The baseline and threshold values ​​associated with patient 4 can be updated periodically. For example, IMD 10 can undergo new learning phases daily, weekly, monthly, quarterly, yearly, or at the end of any other suitable period. A new learning phase can generate new values ​​associated with one or more baseline values ​​and thresholds for patient 4. In other instances, clinicians can program IMD10 to update such values ​​as needed, such as after patient 4 experiences a fall.

[0032] In some instances, IMD 10 can determine a baseline value based on the median, mean, or mode PTT values ​​collected during training. In other instances, IMD 10 can reject outliers collected during training before determining the baseline value. In some instances, the baseline value can indicate the difference between the PTT before and after the sitting-to-standing transition. In addition to determining the baseline value for patient 4, IMD 10 or the clinician can also determine a threshold for patient 4 and store the threshold in the memory of IMD 10.

[0033] IMD 10 can determine thresholds for each of multiple different baseline values. For example, during IMD 10 training, in some instances, IMD 10 can automatically correlate a specific threshold with a specific baseline value for patient 4. In some instances, statistical process control (SPC) of the baseline can be used to determine the threshold for comparison with the current value (e.g., the corresponding current difference measure). In such instances, the threshold can be used to detect acute changes in patient 4's fall risk. In other instances, point of change analysis (CPA) can be applied to determine if there is a significant change in the slope of the PTT after a sit-to-stand transition, with time-series slope values ​​from the baseline slope values ​​(e.g., different slopes measured over time). A significant change in the slope of the PTT after a sit-to-stand transition with time-series slope values ​​from the baseline slope values ​​can indicate a chronic change in patient 4's fall risk. For example, SPC and / or CPA can be performed by IMD 10, external device 12, or external device 94 ( Figure 6 ) Execution. In other instances, clinicians may choose to program the IMD 10 to apply a relatively higher or lower threshold than that selected by the processing circuitry of the IMD 10, based on other considerations known to the clinician.

[0034] Regardless of whether the threshold is determined by the processing circuitry of the IMD 10 during training or by a clinician, such thresholds can be updated once or multiple times after IMD 10 implantation. For example, the threshold can be updated after patient 4 experiences a fall. Alternatively, the threshold can be updated at the end of a time period (e.g., weekly, monthly, or annually after IMD 10 implantation). Such updates to the threshold can be performed automatically by the processing circuitry of the IMD 10 or manually by a clinician. In any such instance, the updated threshold can be determined based on the trend of PTT (post-traumatic stress test) during the previous time period. In this way, the thresholds used in the techniques described herein can be modified as needed to account for changes in patient 4's health.

[0035] External device 12 can be used to program commands or operating parameters into IMD 10 to control its functions (e.g., when configured as a programmer for IMD 10). In some instances, external device 12 can be used to query IMD 10 to retrieve data, including device operating data and physiological data accumulated in the IMD's memory. This query can occur automatically according to a schedule or in response to commands from a remote or local user. Programmers, external monitors, and consumer devices are examples of external devices 12 that can be used to query IMD 10. Examples of communication technologies used by IMD 10 and external device 12 include radio frequency (RF) telemetry, which may be via Bluetooth. An RF link established via a wireless local area network or Medical Implantable Communication Service (MICS). In some instances, external device 12 may include a user interface configured to allow clinicians to interact remotely with IMD 10.

[0036] Medical System 2 is an example of a medical device system configured to determine a patient's risk of falling by monitoring the PTT (post-sitting transition) before and after sitting. The techniques described herein can be performed by the processing circuitry of the device in Medical System 2 (e.g., the processing circuitry of IMD 10). Additionally or alternatively, the techniques described herein can be performed wholly or partially by the processing circuitry of external device 12 and / or by the processing circuitry of one or more other implanted or external devices or servers (not shown). Examples of one or more other implanted or external devices may include intravenous, subcutaneous, or extravascular pacemakers or implantable cardioverter-defibrillators (ICDs), blood analyzers, external monitors, or drug pumps. Communication circuitry for each device in System 2 allows the devices to communicate with each other. Furthermore, while optical sensors and electrodes are described herein as being positioned on the housing of IMD 10, in other instances, such optical sensors and / or electrodes may be positioned on the housing of another device (e.g., an intravenous, subcutaneous, or extravascular pacemaker or ICD) implanted inside or outside the patient 4, or connected to such a device via one or more leads. For example, electrodes or one or more optical sensors for detecting signals associated with PTT can be positioned on one or more external monitoring devices (e.g., wearable monitors). In such instances, one or more of the pacemaker / ICD and one or more external monitoring devices may include processing circuitry configured to receive signals from the electrodes or optical sensors on the respective devices and / or communication circuitry configured to transmit signals from the electrodes or optical sensors to another device (e.g., external device 12) or a server.

[0037] Figure 2 to 4B It shows Figure 1 The various aspects and instance layouts of IMD 10. For example, Figure 2 The physical configuration of an instance of IMD10 is conceptually shown. Figure 3 This is a block diagram illustrating the instance functional configuration of IMD 10. Figure 4A and 4B Additional views are shown illustrating the physical and functional configuration of an IMD 10 instance. It should be understood that, as referenced below... Figure 2 to 4B Any instance of the described IMD 10 can be used to implement the techniques described herein for determining the fall risk of patient 4.

[0038] Figure 2 It is shown Figure 1 A conceptual diagram of an IMD 10 instance configuration. Figure 2In the illustrated example, IMD 10 may comprise a leadless, subcutaneously implantable monitoring device having a housing 14, a proximal electrode 16A, and a distal electrode 16B. The housing 14 may further comprise a first main surface 18, a second main surface 20, a proximal end 22, and a distal end 24. In some embodiments, 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 14 encapsulates the electronic circuitry within IMD 10 and protects the circuitry contained therein from fluids such as bodily fluids. In some embodiments, an electrical feedthrough provides electrical connections between electrodes 16A to 16D and antenna 26 to the circuitry within housing 14. In some embodiments, electrode 16B may be formed from an uninsulated portion of the conductive housing 14.

[0039] exist Figure 2 In the example shown, IMD 10 is defined by a length L, a width W, and a thickness or depth D. In this example, IMD 10 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 also conceivable, for example, where the relative proportions of the length L, width W, and depth D differ. Figure 2 Those described and shown. In some instances, 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.

[0040] In some instances, the spacing between the proximal electrode 16A and the distal electrode 16B can range from about 30 to 55 mm, about 35 to 55 mm, or about 40 to 55 mm, or more generally from about 25 to 60 mm. Generally, the IMD 10 can have a length L of about 20 to 30 mm, about 40 to 60 mm, or about 45 to 60 mm. In some instances, the width W of the main surface 18 can range from about 3 to 10 mm, and can be any single width or width range between about 3 and 10 mm. In some instances, the depth D of the IMD 10 can range from about 2 to 9 mm. In other instances, the depth D of the IMD 10 can range from about 2 to 5 mm, and can be any single depth or depth range between about 2 and 9 mm. In any such instance, the IMD 10 is compact enough to be implanted in the subcutaneous space of the patient's pectoral muscle region.

[0041] According to examples of this disclosure, the IMD 10 can have a geometry and size designed for ease of implantation and patient comfort. Examples of the IMD 10 described in this disclosure can have a volume of 3 cubic centimeters (cm³) or less, 1.5 cm³ or less, 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.

[0042] 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 and second principal surfaces 18, 20 can face in a direction along the sagittal axis of the patient 4 (see...). Figure 1 Furthermore, due to the size of the IMD 10, this orientation can be maintained during implantation.

[0043] 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 signals (e.g., ECG signals). In the techniques described herein, the processing circuitry of the IMD 10 can determine the PTT value in part based on the cardiac ECG signal, as further described below. The cardiac ECG signal can be stored in the memory of the IMD 10, and data derived from the cardiac ECG signal can be transmitted via the integrated antenna 26 to another medical device, such as external device 12.

[0044] exist Figure 2 In the example shown, the proximal electrode 16A is closely adjacent to the proximal end 22, and the distal electrode 16B is closely adjacent to the distal end 24 of the IMD 10. 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 the 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 2The distal electrode 16B is shown. In some instances, 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.

[0045] exist Figure 2 In the illustrated example, the proximal end 22 of the IMD 10 includes a head assembly 32 having one or more proximal electrodes 16A, an integrated antenna 26, anti-migration protrusions 34, and a suture hole 36. The integrated antenna 26 is located on the same main surface (e.g., a first main surface 18) as the proximal electrodes 16A and may be integral with the head assembly 32. In other examples, the integrated antenna 26 may be formed on the main surface opposite the proximal electrodes 16A, or in other examples, it may be incorporated within the housing 14 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), Bluetooth, etc. The antenna 26 can be connected to the communication circuit of the IMD 10, which can drive the antenna 26 to send signals to the external device 12, and can also send signals received from the external device 12 to the processing circuit of the IMD 10 via the communication circuit.

[0046] IMD 10 may include several features for holding IMD 10 in place when subcutaneously implanted into patient 4. For example, as Figure 2 As shown, the housing 14 may include anti-migration protrusions 34 located near the integrated antenna 26. The anti-migration protrusions 34 may include a plurality of ridges or protrusions extending away from the first main surface 18 and may include features to prevent longitudinal movement of the IMD 10 after implantation in the patient 4. In other embodiments, the anti-migration protrusions 34 may be located on the main surface opposite the proximal electrode 16A and / or the integrated antenna 26. Additionally, in Figure 2In the example shown, 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 example shown, the suture hole 36 is located near the proximal electrode 16A. In some instances, the head assembly 32 may comprise 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.

[0047] The IMD 10 can determine the PTT value of patient 4 based on signals received from one or more of electrodes 16A to 16D, light emitter 38, and photodetectors 40A, 40B. Electrodes 16A and 16B can be used to sense cardiac ECG signals for determining the PTT value, as described herein. In some instances, additional electrodes 16C and 16D can be used, in addition to or instead of electrodes 16A and 16B, to sense subcutaneous tissue impedance (e.g., for measuring PTT).

[0048] In some instances, the processing circuitry of IMD 10 can determine the PTT value of patient 4 before and after a sitting-to-standing transition based on ECG signals sensed from electrodes 16A and 16B, and determine the current subcutaneous tissue impedance based on signals received from electrodes 16C and 16D. For example, the processing circuitry of IMD 10 can receive ECG signals from electrodes 16A and 16B and identify one or more features of the cardiac cycle within the ECG signal. For example, the processing circuitry can identify an R wave within the cardiac cycle and correlate a first time (T1) with the occurrence of the R wave. Next, the processing circuitry can identify fluctuations in the subcutaneous tissue impedance signal that occur after T1 and correlate a second time (T2) with these fluctuations, which may represent a portion of the blood ejected during the observed cardiac cycle through the vascular system near electrodes 16C and 16D. By subtracting T2 from T1, the processing circuitry of IMD 10 can then determine the PTT value of patient 4 (e.g., in milliseconds). In order for IMD 10 to accurately identify fluctuations in patient 4's PTT value, for clinicians, implantation is essentially as follows: Figure 1 The IMD 10 shown may be useful, wherein at least a portion of the IMD 10 is located at or below the heart 6 and is located subcutaneously or not adjacent to central arterial blood flow. In this way, the IMD 10 can be positioned at a sufficient circulatory distance from the heart 6 to detect even small fluctuations in the PTT, which can help the IMD 10 accurately assess the fall risk of patient 4.

[0049] exist Figure 2In the example shown, IMD 10 includes a light emitter 38, a proximal light detector 40A, and a distal light detector 40B positioned on the housing 14 of IMD 10. Light detector 40A may be located at a distance S from light emitter 38, while distal light detector 40B may be located at a distance S+N from 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, 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 PTT value of patient 4. Although the light emitter 38 and photodetectors 40A, 40B are described herein as being positioned on the housing 14 of the IMD 10, in other instances, 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 at one or more wavelengths within the (VIS) and / or (NIR) spectrum. For example, the light emitter 38 may emit light at one or more of about 660 nm, 720 nm, 760 nm, 800 nm, or at any other suitable wavelength.

[0050] like Figure 2 As shown, light emitter 38 can be positioned on head assembly 32, although in other embodiments, one or both of photodetectors 40A, 40B can be additionally or alternatively positioned on head assembly 32. In some embodiments, light emitter 38 can be positioned on the middle portion of IMD 10, such as a portion of the path between proximal end 22 and distal end 24. Although light emitter 38 and photodetectors 40A, 40B are shown located on first main surface 18, light emitter 38 and photodetectors 40A, 40B can alternatively be located on second main surface 20. In some embodiments, IMD can be implanted such that when 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 patient 4. Photodetectors 40A, 40B can include glass or sapphire windows, such as those referenced below. Figure 4B As described, it may be positioned beneath a portion of the casing 14 of the IMD 10, which is made of glass or sapphire, or it may be transparent or translucent.

[0051] As described above, one or both of the light emitter 38 and photodetectors 40A, 40B can be used in techniques for determining the PTT value of patient 4. Similar to techniques for determining PTT in which the processing circuitry of IMD 10 receives subcutaneous tissue impedance signals from multiple electrodes 16A to 16D, techniques for determining PTT using optical sensors include identifying one or more features within the cardiac cycle of patient 4 and correlating a first time T1 with an occurrence within the cardiac cycle. However, instead of determining a second time T2 based on the impedance signal, IMD 10 can determine T2 by identifying fluctuations in the intensity and / or wavelength of light detected by one or both of photodetectors 40A, 40B that occur after T1, and correlating the second time (T2) with the fluctuation, which may represent a portion of the vascular system ejected during the cardiac cycle through the vicinity of photodetectors 40A, 40B. By subtracting T2 from T1, the processing circuitry of IMD 10 can then determine the PTT value of patient 4 (e.g., in milliseconds).

[0052] In some instances, IMD 10 may include one or more additional sensors, such as one or more accelerometers 64. Such accelerometers 64 may be 3D accelerometers configured to generate signals indicative of one or more types of movement in the patient, such as the patient's whole body movement (e.g., activity), patient posture, movement associated with heartbeat, or coughing, rales, or other respiratory abnormalities. In some instances, one or more such accelerometers may be used in conjunction with light emitter 38 and light detectors 40A, 40B to determine a cardiac impact graph (i.e., a measurement of the motion corresponding to the blood ejection during systole), which the processing circuitry of IMD 10 can use to determine a PTT (post-coital transition) in addition to or in addition to the ECG signal from a pair of electrodes 16A to 16D. IMD 10 may also monitor accelerometer signals to determine if the patient 4 has undergone a sitting-to-standing transition. IMD 10 may also monitor accelerometer signals to determine if the patient 4 is active. IMD 10 can determine the patient's PTT before and after sitting-to-standing transition, can determine the difference between the patient's PPT before and after sitting-to-standing transition, and can determine whether the value of the difference exceeds a threshold that can indicate an increased likelihood of the patient's fall.

[0053] Although the processing circuitry of IMD 10 is described above as being configured to receive signals from one or more accelerometers, electrodes 16A to 16D, light emitter 38, and / or photodetectors 40A, 40B of IMD 10, and to determine the values ​​of one or more parameters of patient 4 based on such signals, any steps performed by the processing circuitry of IMD 10 as described herein can be performed by the processing circuitry of one or more devices. For example, the processing circuitry of external device 12 or any other suitable implantable or external device or server can be configured, for example, to receive signals from one or more accelerometers, electrodes 16A to 16D, light emitter 38, and / or photodetectors 40A, 40B of IMD 10 via, for example, the communication circuitry of IMD 10.

[0054] Figure 3 It is shown Figure 1 and Figure 2 A functional block diagram of an example configuration of the IMD 10 is provided. In the illustrated example, in addition to the aforementioned electrodes 16A to 16D (one or more of which may be disposed within the housing 14 of the IMD 10) and the light emitter 38, the IMD 10 includes processing circuitry 50, sensing circuitry 52, communication circuitry 54, memory 56, switching circuitry 58, sensor 62, and accelerometer 64. In some examples, the memory 56 includes computer-readable instructions that, when executed by the processing circuitry 50, cause the IMD 10 and the processing circuitry 50 to perform the various functions attributed herein to the IMD 10 and the processing circuitry 50. The memory 56 may include any volatile, non-volatile, magnetic, optical, or electrical medium, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other digital medium.

[0055] Processing circuitry 50 may include fixed-function circuitry and / or programmable processing circuitry. Processing circuitry 50 may include any one or more of a microprocessor, controller, digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or equivalent discrete or analog logic circuitry. In some instances, processing circuitry 50 may include multiple components, such as one or more microprocessors, one or more controllers, one or more DSPs, one or more ASICs, or one or more FPGAs, and any combination of other discrete or integrated logic circuitry. The functionality attributed herein to processing circuitry 50 may be implemented as software, firmware, hardware, or any combination thereof.

[0056] like Figure 3As shown, memory 56 may also include one or more tables 70 for storing baseline and threshold level values. As described above, in some instances, processing circuitry 50 of IMD 10 may be configured to determine a baseline value for the PTT difference during the learning phase of IMD 10, and then store it in table 70. Additionally, table 70 may include pre-programmed baseline values ​​that a clinician may select for patient 4 during the setup of IMD 10, or baseline values ​​that a clinician may manually enter based on their assessment of patient 4. Processing circuitry 50 may also be configured to determine a threshold for the deviation of the PTT difference from the baseline value and store the threshold in table 70. In some instances, processing circuitry 50 may determine such a threshold at least in part based on the baseline value selected for patient 4. In addition to the baseline value, table 70 may include thresholds that a clinician selects for patient 4 during the setup of IMD 10, or thresholds that a clinician may manually enter based on their assessment of patient 4.

[0057] Sensing circuit 52 and communication circuit 54 can be selectively coupled to electrodes 16A to 16D via a switching circuit 58 controlled by processing circuit 50. Sensing circuit 52 can monitor signals from electrodes 16A to 16D to monitor cardiac electrical activity (e.g., generating an ECG for PTT determination) and / or subcutaneous tissue impedance Z (e.g., for PTT determination). Sensing circuit 52 can also monitor signals from sensor 62, which may include photodetectors 40A, 40B and any additional photodetectors that may be located on IMD 10. In some instances, sensing circuit 52 may include one or more filters and amplifiers for filtering and amplifying signals received from electrodes 16A-16D and / or photodetectors 40A, 40B.

[0058] In some instances, processing circuitry 50 may also include rectifiers, filters and / or amplifiers, sensing amplifiers, comparators and / or analog-to-digital converters. Upon receiving signals from electrodes 16A to 16D and photodetectors 40A, 40B via sensing circuitry 52, processing circuitry 50 can determine the patient's PTT (post-transfer time). The processing circuitry can then compare the PTT to baseline levels stored in table 70 and determine whether the difference between the current value and the corresponding baseline level satisfies the corresponding threshold stored in table 70.

[0059] The processing circuit 50 can store the determined value, along with an indication of the date and time of the measurement, in the difference / slope 68 of the memory 56. Simultaneously or subsequently, the processing circuit 50 can transmit an indication that the patient 4 is more likely to fall to the external device 12 via the communication circuit 54.

[0060] The communication circuit 54 may include any suitable hardware, firmware, software, or any combination thereof for communicating with another device, such as external device 12 or another IMD or sensor, such as a pressure sensing device. Under the control of the processing circuit 50, the communication circuit 54 may receive downlink telemetry from external device 12 or another device and send uplink telemetry to external device 12 or another device by means of an internal or external antenna (e.g., antenna 26). In some instances, the communication circuit 54 may communicate with external device 12. Furthermore, the processing circuit 50 may communicate with networked computing devices via external devices (e.g., external device 12) and computer networks, such as the Medtronic Care Link developed by Medtronic, plc, Dublin, Ireland. network.

[0061] 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 communication circuitry 54. Clinicians can also use external device 12 or another local or networked computing device to program parameters of the IMD 10. In some instances, clinicians can select baseline values ​​and thresholds.

[0062] The various components of the IMD 10 can be connected to a power source, which may include a rechargeable or non-rechargeable battery located within the housing 14 of the IMD 10. The non-rechargeable battery may be selected to last for several years, while the rechargeable battery may be inductively charged from an external device, for example, daily or weekly.

[0063] Figure 4A and 4B It shows that it can be basically similar to Figure 1 to 3 An IMD 10 may include two additional instances of an IMD that can have one or more additional features. Figure 4A and 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 IMD 10A instance configuration. Figure 4B This is a block diagram of a side view of instance IMD 10B, which may include the insulating layer described below.

[0064] Figure 4A It shows that it can be used with Figure 1 A conceptual diagram of another instance, IMD 10A, which is basically similar to IMD 10. Besides... Figure 1 Apart from the components shown in -3, Figure 4AThe 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 attach the head 32 to the body portion 72 of the IMD 10A. The body portion 72 of the IMD 10A may be configured to accommodate... Figure 3 The IMD 10 shown may include one or more internal components, such as processing circuitry 50, sensing circuitry 52, communication circuitry 54, memory 56, switching circuitry 58, internal components of sensor 62, and timing control circuitry 64. In some embodiments, the body portion 72 may be formed of one or more of titanium, ceramic, or any other suitable biocompatible material.

[0065] Figure 4B It is shown that it can include basic similarities Figure 1 A conceptual diagram of another component of IMD 10, IMD 10B. Besides... Figure 1 to 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 helps to insulate electrical signals transmitted between electrodes 16A to 16D and / or photodetectors 40A, 40B on the housing 14B and the processing circuitry 50. In some embodiments, the insulating cover 76 may be positioned on the open housing 14 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 timing / control circuitry 64) may be formed on the underside of the insulating cover 76, for example, by using flip-chip technology. The insulating cover 76 may be flipped onto the housing 14B. When flipped and placed on the housing 14B, the components of the IMD 10B formed on the underside of the insulating cover 76 may be positioned within the gap 78 defined by the housing 14B.

[0066] The insulating cover 76 can be configured not to interfere with the operation of the IMD 10B. For example, one or more of the electrodes 16A-16D can be formed or placed on top of the insulating cover 76 and electrically connected to the switching circuit 58 through one or more through-holes (not shown) formed through the insulating cover 76. Additionally, in order for the IMD 10B to determine the PTT value, at least a portion of the insulating cover 76 can be transparent to NIR or visible wavelengths emitted by the light emitter 38 and detected by the photodetectors 40A, 40B, which in some instances can be positioned on the underside of the insulating cover 76 as described above.

[0067] In some instances, the light emitter 38 may include a filter between the light emitter 38 and the insulating cover 76, which may restrict the spectrum of the emitted light to a narrow band. Similarly, photodetectors 40A, 40B may include a filter between the photodetectors 40A, 40B and the insulating cover 76, such that the photodetectors 40A, 40B detect light from a narrow spectrum, typically at wavelengths longer than the emitted spectrum. Other optical elements that may be included in the IMD 10B may include a refractive index matching layer, an anti-reflective coating, or a light barrier, which may be configured to block light emitted laterally from the light emitter 38 from reaching the photodetector 40.

[0068] The insulating cap 76 can be formed of sapphire (i.e., corundum), glass, parylene, and / or any other suitable insulating material. Sapphire can have a transmittance greater than 80% for wavelengths in the range of about 300 nm to about 4000 nm and can have a relatively flat profile. In cases of variation, different transmittance at different wavelengths can be compensated for, for example, by using a ratiometric method. In some instances, the insulating cap 76 can have a thickness of about 300 micrometers to about 600 micrometers. The outer shell 14B can be formed of titanium or any other suitable material (e.g., a biocompatible material) and can have a thickness of about 200 micrometers to about 500 micrometers. These materials and dimensions are merely examples, and other materials and other thicknesses are possible for the device of the present invention.

[0069] Figure 5 This is a functional block diagram illustrating an example configuration of an external device 12 configured to communicate with one or more IMDs 10. Figure 5 In this example, external device 12 includes processing circuitry 200, memory 202, user interface (UI) 204, and communication circuitry 206. External device 12 may correspond to reference [reference missing]. Figure 1 and 6 Any external device 12 described. External device 12 may be a dedicated hardware device with dedicated software for programming and / or querying IMD 10. Alternatively, external device 12 may be an off-the-shelf computing device, such as a smartphone running a mobile application, enabling external device 12 to program and / or query IMD 10. In some instances where external device 12 is a smartphone, external device 12 may include a mobile application that facilitates interaction with IMD 10.

[0070] In some instances, the user of external device 12 may be a clinician, physician, healthcare provider, patient, patient's family member, or patient's friend. In some instances, the user uses external device 12 to select or program any value for the operating parameters of IMD 10, such as for measuring or determining patient body stability based on PTT (Patient Tolerance). In some instances, the user uses external device 12 to receive data collected by IMD 10, such as the difference measure / slope 180 of IMD 10 or other operational and performance data. The user may also receive alerts provided by IMD 10 predicting acute cardiac events (e.g., ventricular tachyarrhythmias). The user may also receive alerts indicating that the patient may be more likely to fall or that the patient requires attention due to deteriorating body stability. The user may interact with external device 12 via UI 204, which may include a display presenting a graphical user interface to the user, and a keypad or other mechanism (e.g., a touch-sensitive screen) for receiving input from the user. External device 12 can wirelessly communicate with IMD 10 using communication circuit 206, which can be configured to perform RF communication with communication circuit 168 of IMD 10.

[0071] Processing circuitry 200 may include any combination of integrated circuits, discrete logic circuits, analog circuits (such as one or more microprocessors), digital signal processors (DSPs), application-specific integrated circuits (ASICs), or field-programmable gate arrays (FPGAs). In some instances, processing circuitry 200 may include multiple components, such as one or more microprocessors, one or more DSPs, one or more ASICs, or one or more FPGAs, as well as any combination of other discrete or integrated logic circuits and / or analog circuits.

[0072] Memory 202 may store program instructions, which may include one or more program modules executable by processing circuitry 200. When executed by processing circuitry 200, such program instructions may enable processing circuitry 200 and external device 12 to provide the functions assigned to them herein. The program instructions may be contained in software, firmware, and / or RAMware. Memory 202 may include any volatile, non-volatile, magnetic, optical, or electrical medium, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically erasable programmable ROM (EEPROM), flash memory, or any other digital medium.

[0073] In some instances, the processing circuitry 200 of the external device 12 may be configured to provide some or all of the functions assigned herein to the processing circuitry 160 of the IMD 10. For example, the processing circuitry 200 may receive physiological signals and differential measures / slopes 180 generated by one or more IMDs 10 and / or may receive differential measures / slopes 180 from one or more IMDs 10. The processing circuitry 200 may determine a baseline stored in the baseline & threshold table 178 and / or differential measures / slopes 180 relative to the processing circuitry 50 of the IMD 10 in a manner described herein for determining patient body stability based on data generated by the accelerometer.

[0074] Figure 6 This is a functional block diagram illustrating an example system including an access point 90, a network 92, an external computing device (e.g., a server 94), and one or more other computing devices 80A to 80N, which can be connected to IMD 10 and external device 12 via network 92. In this example, IMD 10 can communicate with external device 12 via a first wireless connection using communication module 54, and with access point 90 via a second wireless connection. Figure 6 In this example, access point 90, external device 12, server 94, and computing devices 80A to 80N are interconnected and can communicate with each other via network 92.

[0075] 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 instances, access point 90 may be connected to network 92 via different forms of connection, including wired or wireless connections. In some instances, access point 90 may be a user device, a local user device, such as a tablet or smartphone. As described above, IMD 10 may be configured to transmit data, such as current values ​​and risk of decline, to external device 12. Additionally, access point 90 may query IMD 10, for example periodically or in response to commands from a patient or network 92, to retrieve values ​​determined by processing circuitry 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.

[0076] In some cases, server 94 can be configured to provide a secure storage site for data collected from IMD 10 and / or external devices 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 80A to 80N. Figure 6 shown One or more aspects can be implemented using general networking technologies and functions, which can be similar to the Medtronic Care Link developed by Medtronic PLC in Dublin, Ireland. The technologies and functions provided by the network.

[0077] In some instances, one or more of the computing devices 80A to 80N (e.g., device 80A) may be a tablet or other smart device located at the clinician's location, through which the clinician can program the IMD 10 to receive alerts and / or query it. For example, the clinician can access patient 4's PTT measurement, difference measure, or slope via device 80A, for example, between clinician visits, to check patient 4's fall risk as needed. In some instances, the clinician can input instructions for medical interventions for patient 4 into an application in device 80A, for example, based on patient 4's fall risk determined by the IMD 10, or based on other patient data known to the clinician. Device 80A can then transmit the instructions for medical interventions to another computing device 80A to 80N (e.g., device 80B) located at patient 4 or patient 4's caregiver. For example, such instructions for medical interventions may include instructions to change medication dosage, timing, or selection to schedule a clinician visit or seek medical attention. In a further example, device 80B can generate an alert for patient 4 based on the fall risk determined by IMD 10, which allows patient 4 to proactively seek medical attention before receiving instructions for medical intervention. In this way, patient 4 can be authorized to take action as needed to address his or her fall risk, which may include improving patient 4's clinical outcomes.

[0078] Figure 7 This is a flowchart illustrating an example technique used to determine the likelihood of a patient falling. As described herein, Figure 7 The technique shown can use one or more components of System 2, which have been referenced above. Figure 1 to 5 A description has been provided. Although the description was performed by IMD 10, Figure 7 The technology can be performed wholly or partially by the processing circuitry and memory of other devices in the medical device system, as described herein. For example, for clarity, although the processing circuitry 50 of the IMD is described as performing... Figure 7 The techniques shown are mostly those of the example technology, but in other examples, one or more devices (e.g., external device 12 or other external devices or servers) or clinicians may perform one or more steps of the processing circuitry 50 attributed to IMD 10.

[0079] Figure 7Examples could be techniques used by the processing circuitry 50 of IMD 10 to determine the physical stability or fall risk of patient 4 based on a comparison of a difference measure or slope based on the patient 4's PTT value with a corresponding baseline value stored in table 70 of memory 56. As discussed above, IMD 10 can determine the patient 4's baseline PTT value. In some instances, IMD 10 can determine the baseline value during the learning phase of IMD 10 after implantation in patient 4, as referenced... Figure 1 The learning phase discussed here can occur after IMD 10 implantation and when patient 4's condition is stable.

[0080] For example, by using the sensors described above, processing circuitry 50 can determine a first plurality of PTTs (102) of patient 4 prior to a sitting-to-standing transition. For example, the first plurality of PTTs may be PTTs measured prior to the sitting-to-standing transition and may be stored in a roll buffer 62. Processing circuitry 50 can determine whether a sitting-to-standing transition has occurred based on at least one accelerometer signal (104). For example, processing circuitry 50 can monitor the accelerometer 64 signal to determine whether a sitting-to-standing transition has occurred. Details regarding how to determine a sitting-to-standing transition based on accelerometer signals can be found in co-assigned U.S. Patent Application No. 15 / 607,945, filed May 25, 2017, entitled “ACCELEROMETER SIGNAL CHANGE AS A MEASURE OF PATIENTFUNCTIONAL STATUS,” now published as U.S. Patent Application Publication No. US2018 / 0035924A1 and claiming the benefit of Provisional Application No. 62 / 370,138, filed August 2, 2016.

[0081] If no change of sitting or standing occurred ( Figure 7If the "No" path is selected, the processing circuit can continue to determine the first plurality of PPTs (102). In some instances, if a sitting-to-standing transition occurs, the processing circuit 50 can determine whether the patient 4 has been inactive for a predetermined time period. The processing circuit 50 can make this determination based on signals from an activity sensor. In some instances, the activity sensor is an accelerometer within the IMD 10 or any medical device performing the techniques of this disclosure. In some instances, the processing circuit 50 determines a plurality of activity counts based on one or more accelerometer signals exceeding one or more thresholds, and uses the number of activity counts to determine whether the patient has been inactive for a predetermined time period. The activity count used to determine whether the patient has been inactive for a predetermined time period can be the total number, average, or median of counts within that time period. In some instances, the IMD 10 can determine whether the patient 14 has not taken a step by monitoring an accelerometer signal indicating that a step has been taken, thereby determining whether the patient 14 is inactive. Details of how to determine when to take a step based on accelerometer signals can be found in co-assigned U.S. Patent Application No. 15 / 603,776, filed May 24, 2017, entitled “Step Count Detection Using Accelerometer Axis,” now published as U.S. Patent Application Publication No. US 2018 / 0035920 A1, and claims the benefit of Provisional Application No. 62 / 370,102, filed August 2, 2016.

[0082] In some instances, if IMD 10 does not determine that patient 4 was inactive for at least a predetermined period of time before the sitting-to-standing transition (e.g., patient 4 was active), then in some instances, IMD 10 may ignore the sitting-to-standing transition and continue determining the first plurality of PPTs (102). For example, the predetermined period of time may be programmed by external device 12 or may be fixed. In some instances, the predetermined period of time may be several minutes, such as six minutes. IMD 10 may ignore the sitting-to-standing transition shortly after the period of patient 4's active state because recent activity may reduce the likelihood that patient 4's physical stability is worse than normal, or because measurements after recent activity may be inconsistent with measurements during periods of less activity, so the PTT measurements before and after the sitting-to-standing transition may not be comparable to other measurements. By ignoring the sitting-to-standing transition shortly after the period of patient 4's active state, IMD 10 can conserve battery power and save the PTT dataset before and after the sitting-to-standing transition, which is more indicative of measurements of physical stability problems. Alternatively, ICM 10B may not be able to determine whether patient 14 was inactive during the predetermined time period before the sitting-to-standing transition.

[0083] In some instances, if a sitting / standing change occurs ( Figure 7 If the path is "yes" in the text, then the processing circuit 50 can determine a second plurality of PTTs (106) after the sitting-to-standing transition occurs. For example, the processing circuit 50 can measure the second plurality of PTTs after the sitting-to-standing transition and can store the second plurality of PTTs in the scroll buffer 62.

[0084] Processing circuit 50 can determine the likelihood of a patient falling based on a first plurality of PTTs and a second plurality of PTTs (108). For example, processing circuit 50 can determine the likelihood of patient 4 falling at least in part by calculating a first metric based on the first plurality of PTTs (PPTs before sitting-to-standing transitions). For example, the first metric may include the median, mean, or mode of the PTTs before sitting-to-standing transitions. Processing circuit 50 of IMD 10 can also further determine the likelihood of patient 4 falling at least in part by calculating a second metric based on a second plurality of PTTs (e.g., PPTs after each sitting-to-standing transition). For example, the second metric may include at least one of the slope of the second plurality of PTTs, the minimum pulse delivery time of the second plurality of PTTs, the maximum PTT of the second plurality of PTTs, or the median, mean, or mode of the second plurality of PTTs. Thus, the second metric may include the slope of the second plurality of PTTs, the minimum PTT after sitting-to-standing transition (e.g., the fastest PTT), the maximum PTT after sitting-to-standing transition (e.g., the slowest PTT), and / or the median, mean, or mode of the PTTs after sitting-to-standing transitions.

[0085] In some instances, processing circuitry 50 may determine the likelihood of a patient falling, at least in part, by calculating a difference metric. The difference metric may include at least one of the differences between a first metric and one or more second metric. For example, the difference metric may include at least one of the differences between the first metric and the minimum PTT of a plurality of PTTs, the maximum PTT of a first metric and a plurality of PTTs, or the difference between the median, mean, or mode of a first metric and a plurality of PTTs. For example, processing circuitry 50 of IMD 10 may calculate the minimum difference, maximum difference, and median, mean, or mode difference by subtracting each second metric (e.g., from the second plurality of PTTs after the sitting-up transition) from the first metric (the mean, median, or mode of the first PTT before the sitting-up transition). For example, processing circuitry 50 of IMD 10 may calculate MinDiff by subtracting the fastest sitting-up transition PTT from the median of the pre-sitting-up transition PTTs. Processing circuitry 50 may calculate MaxDiff by subtracting the slowest post-sitting-up transition PTT from the median of the pre-sitting-up transition PTTs. The processing circuit can also calculate MedDiff by subtracting the median of the post-sitting transition PTT from the median of the pre-sitting transition PTT.

[0086] In addition to or instead of the difference measure, the processing circuitry 50 of IMD 10 can calculate the slope (110) of the PTT value after the sitting-to-standing transition. For example, IMD 10 can track the slope and difference measure of the PTT value after the sitting-to-standing transition over time by storing the calculated difference measure and slope value in the difference measure / slope 68 in memory 56. In some instances, IMD 10 can use the difference measure and / or slope of the PTT value after the sitting-to-standing transition to update the baseline in Table 70. Acute or chronic changes in these measures may be an indication that patient 4 is more likely to fall while standing. The processing circuitry 50 can store the difference measure and slope of the PTT value after the sitting-to-standing transition in memory 56.

[0087] In some instances, the processing circuitry 50 of IMD 10 can determine the likelihood of patient 4 falling by calculating a trend measure 71 based at least one of a plurality of difference measures of the slope of the PTT (e.g., the PTT after sitting-to-standing transition) over time. For example, the processing circuitry 50 can calculate the trend measure 71 based on a difference measure or at least one of a plurality of slopes of the PTT over time. For example, the processing circuitry 50 can calculate the trend measure 71 periodically (e.g., daily). For example, the processing circuitry 50 of IMD 10 can calculate the central tendency (e.g., median, mean, or mode) of one or more difference measures and / or slopes of the PTT after sitting-to-standing transition, as well as the variability (e.g., standard deviation or interquartile range) of one or more difference measures and / or slopes of the PTT after sitting-to-standing transition. In other instances, the processing circuitry 50 of IMD 10 can calculate the trend measure 71 based on a user request on external device 12. For example, the processing circuitry 50 of IMD 10 can calculate the central tendency (e.g., median, mean, or mode) of one or more difference measures and / or slopes of the PTT after the sitting-to-standing transition, and the variability (e.g., standard deviation or interquartile range) of one or more difference measures and / or slopes of the PTT after the sitting-to-standing transition, based on a user request on external device 12. The processing circuitry 50 can store the trend measure 71 (e.g., central tendency and / or variability) in memory 56. In some instances, the trend measure can be stored as a baseline value. In some instances, the processing circuitry 50 can determine the likelihood of a patient falling, at least in part, by comparing at least one of the difference measures or slopes of a second plurality of PPTs with a past trend measure.

[0088] IMD 10 can periodically measure PTT, for example, every minute. IMD 10 can then store the obtained measurements in a scroll buffer 64. The scroll buffer 64 can be configured to store a predetermined number of PTT values. For example, the scroll buffer can be configured to store 12 PTT values.

[0089] In an instance where the scroll buffer is configured to store 12 PTT values, the processing circuitry 50 of the IMD 10 can calculate the median, mean, or mode of the first five PTT measurements in the scroll buffer (those associated with patient 4 sitting). In some instances, the processing circuitry 50 can ignore PTT measurements that may occur during the sitting-to-standing transition, such as the middle two values ​​in the scroll buffer. For example, the processing circuitry 50 can continue to calculate five PTTs after standing, and calculate the PTT value after standing, the minimum PTT value after standing, the maximum PTT value after standing, and the slope of the median, mean, or mode PTT values ​​after standing. The processing circuitry 50 of the IMD 10 can then calculate a difference measure, such as the difference between the post-standing measure and the median, mean, or mode of the PTT before standing (e.g., while sitting). The processing circuitry 50 of the IMD 10 can compare the difference measure and / or the slope of the post-standing PTT values ​​with the baseline measure stored in Table 70 to determine whether patient 4's body may be less stable than when the baseline was determined. For example, if IMD 10 determines that the slope of the differential measurement and / or the post-standing PTT value has an acute or chronic change, it can send an alarm to external device 12. For example, communication circuitry 54 can be configured to transmit an alarm to an external device (e.g., external device 12) when it is determined that the likelihood of the patient falling has increased.

[0090] For example, if the slope of the differential measurement or post-standing PTT value changes by 50% over a relatively short time span (e.g., two days), this may indicate an acute change in the patient's physical stability and a higher likelihood of falling. If the slope of the differential measurement or post-standing PTT value changes over a relatively long period (e.g., two weeks), this may indicate a chronic change in the patient's physical stability and a higher likelihood of falling.

[0091] In some instances, processing circuitry 50 may also determine whether the difference between one or more difference measures and / or slopes of the PTT after a sitting-to-standing transition and the corresponding baseline value meets a threshold. In some instances, the threshold change value for a given parameter may be the absolute value of a percentage of the baseline value. For example, if the baseline value of the difference measure is =X, then the threshold for the difference measure may be X ± 0.5X. IMD 10 may repeat steps 100 to 114 during each sitting-to-standing transition.

[0092] Figure 8 This is a flowchart illustrating an example of a technique in which an external device 12 determines instructions or treatments for medical intervention based on the fall risk of the patient 4 received from the IMD 10, and transmits the instructions or treatments to a user interface. Figure 8 The method shown can be used in conjunction with any method described herein for determining fall risk using IMD 10, such as Figure 7The method is shown. In the example shown, the external device 12 is configured to receive the fall risk of the patient 4 from the IMD 10, which can be transmitted to the processing circuit (150) of the external device 12 via the communication circuit 54 and antenna 26 of the IMD 10.

[0093] In some instances, upon receiving a fall risk assessment of patient 4 from IMD 10 and before determining instructions or treatment for medical intervention for patient 4, external device 12 may transmit one or more queries to the user device. For example, external device 12 may request patient 4 or a caregiver to answer questions about patient 4's recent or current activities or symptoms, such as whether patient 4 has recently exercised, taken medication, or experienced symptoms. Additionally, if IMD 10 has not yet transmitted a difference measure to external device 12, external device 12 may query IMD 10 for the difference measure and / or the slope of the PTT after patient 4's sitting-to-standing transition. Based on patient 4's fall risk, and optionally based on the answers to the queries and / or patient 4's current values, external device 12 can then determine instructions or treatment for medical intervention for patient 4 (152).

[0094] In some instances, external device 12 can determine instructions or treatments for medical interventions independently of clinician input, for example, by selecting from treatment options stored in the memory of external device 12 or in a centralized database associated with the patient 4's fall risk. In other instances, the clinician can determine instructions or treatments for medical interventions on substantially the same basis and input the instructions into external device 12. External device 12 can then transmit the instructions or treatments to the user device's interface with the patient 4 (154). In some instances, external device 12 can control IMD 10, for example, to deliver treatments such as stimulation to the heart or nerves. In other instances, external device 12 can control a drug pump to deliver medication to the patient 4. In some instances, external device 12 can send follow-up queries to the patient 4 or caregiver via the user device after sending the instructions. Such queries may include questions about the patient 4's understanding of the transmitted instructions, whether the patient 4 has complied with the instructed medical intervention, whether the patient 4 feels that their condition has improved, and / or whether the patient 4 is experiencing symptoms. External device 12 can store the patient 4's responses in the memory of external device 12 or in a central database. After any changes are made to Patient 4's treatment, the clinician can review the response and conduct remote follow-ups with Patient 4 as needed. In this way, the technology and system described herein can advantageously enable Patient 4 to receive personalized, frequently updated treatment at a lower cost than a comparable number of clinician visits. Additionally, this technology and system can help reduce the number of falls experienced by Patient 4.

[0095] During medical intervention, the processing circuitry 50 of the IMD 10 can continue to measure body stability or fall risk, as referenced. Figure 7 As discussed, this allows for feedback to be provided to clinicians or patients regarding the effectiveness of medical interventions.

[0096] Although the processing circuitry 50 of IMD 10 and the processing circuitry of external device 12 are described above as being configured to perform Figure 7 to 8 The techniques described herein may include one or more steps, but any step of the techniques described herein may be performed by other processing circuitry in the IMD 10 or external device 12, or by one or more other devices. For example, the processing circuitry of external device 12 or any other suitable implantable or external device or server may be configured to perform one or more steps described as being performed by the processing circuitry 50 of the IMD 10. In other instances, the processing circuitry 50 of the IMD 10 or any other suitable implantable or external device or server may be configured to perform one or more steps described as being performed by the processing circuitry of external device 12. Such other implantable or external devices may include, for example, implantable pacemakers or ICDs, external monitoring devices, or any other suitable devices. Additionally, while optical sensors and electrodes are described herein as being positioned on the housing of the IMD 10, in other instances, such optical sensors and / or electrodes may be positioned on the housing of another device implanted inside or outside the patient (e.g., a transvenous, subcutaneous, or extravascular pacemaker or ICD), or connected to such a device via one or more leads.

[0097] Figure 9 This is a conceptual diagram 1100 showing the sagittal axis 1102, vertical axis 1104, and horizontal axis 1106 in a three-dimensional coordinate system. As shown in the diagram, the sagittal axis 1102 extends in the front-back direction, the vertical axis 1104 extends in the vertical direction, and the horizontal axis extends in the left-right direction.

[0098] Figure 10 This is illustrated by the accelerometer (see example) during a series of sitting and sitting-standing movements, respectively labeled A1 to A2, B1 to B2, and C1 to C2. Figure 7The curve 1200 shows the sagittal axis signal 1202, vertical axis signal 1204, and horizontal axis signal 1206 generated by element 166. The sagittal axis signal 1202 corresponds to a trajectory or trend showing maximum amplitude variation primarily on the (+) side of the y-axis (arbitrary units) in each of A1-A2, B1-B2, and C1-C2. The vertical axis signal 1204 corresponds to a trajectory or trend showing moderate amplitude variation on both the (+) and (-) sides of the y-axis in each of A1-A2, B1-B2, and C1-C2. The horizontal axis signal 1206 corresponds to a trajectory or trend showing amplitude variation primarily on the (-) side of the y-axis in each of A1-A2, B1-B2, and C1-C2, exhibiting a number of zero-crossing points less than that of the vertical axis signal 1204.

[0099] The voltage variation range provided within the sagittal axis signal 1202, vertical axis signal 1204, and horizontal axis signal 1206 is not limited to any particular voltage variation range, and in some instances, the voltage variations of the sagittal axis signal 1202, vertical axis signal 1204, and horizontal axis signal 1206 are provided by an accelerometer configured to generate and provide a processed single-axis accelerometer output signal to detect steps. In various instances, instead of the sagittal axis signal 1202, vertical axis signal 1204, and horizontal axis signal 1206, the voltage variation relative to the vertical axis is displayed. These variations are scaled to represent the change in gravity measured in units of gravity, for example, gravity = 9.80991 m / s², and the variations in the sagittal axis signal 1202, vertical axis signal 1204, and horizontal axis signal 1206 represent the change in gravity applied to the respective axis measured in units.

[0100] Various aspects of the technology can be implemented within one or more processors, including one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic circuits, and any combination of such components, specifically embodied in a programmer (e.g., a physician or patient programmer), an electrical stimulator, or other device. The terms "processor" or "processing circuitry" can generally refer to any of the aforementioned logic circuits, alone or in combination with other logic circuits or any other equivalent circuitry.

[0101] In one or more instances, the functions described herein may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. The computer-readable medium may include a computer-readable storage medium forming a tangible, non-transient medium. The instructions may be executed by one or more processors, such as one or more DSPs, ASICs, FPGAs, general-purpose microprocessors, or other equivalent integrated or discrete logic circuits. Therefore, the terms "processor" or "processing circuit" as used herein may refer to one or more of any of the foregoing structures or any other structure suitable for implementing the techniques described herein.

[0102] Additionally, in some aspects, the functionality described herein may be provided 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 these modules or units must be implemented by separate hardware or software components. Rather, the functionality associated with one or more modules or units may be performed by separate hardware or software components, or integrated within common or separate hardware or software components. Furthermore, the technology may be fully implemented in one or more circuit or logic elements. The technology disclosed herein can be implemented in a wide 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 circuitry residing in IMDs and / or external programmers.

[0103] This disclosure includes the following examples.

[0104] Example 1: A system comprising: an accelerometer circuit configured to generate at least one signal; a memory; and processing circuitry coupled to the accelerometer circuitry and the memory, the processing circuitry being configured to: determine a first plurality of pulse transit times of the patient prior to a sitting-standing transition; determine, based on the at least one accelerometer signal, whether the sitting-standing transition of the patient has occurred; determine, based on the occurrence of the sitting-standing transition, a second plurality of pulse transit times after the sitting-standing transition of the patient; and determine, based on the first plurality of pulse transit times and the second plurality of pulse transit times, the probability that the patient will fall.

[0105] Example 2. The system according to Example 1, wherein the processing circuitry is configured to determine the probability that the patient will fall by at least partly calculating a first metric based on the first plurality of pulse delivery times.

[0106] Example 3. The system according to Example 2, wherein the processing circuitry is further configured to determine the probability that the patient will fall by at least partly calculating a second metric based on the second plurality of pulse delivery times and comparing the second metric with the first metric.

[0107] Example 4. The system according to Example 3, wherein the first metric includes the median, average, or mode of the first plurality of pulse transit times, and the second metric includes at least one of the slope of the second plurality of pulse transit times, the minimum pulse transit time of the second plurality of pulse transit times, the maximum pulse transit time of the second plurality of pulse transit times, or the median, average, or mode of the second plurality of pulse transit times.

[0108] Example 5. The system according to Example 4, wherein the processing circuitry is configured to determine the probability that the patient will fall at least in part by calculating a difference metric, wherein the difference metric includes at least one of the difference between the first metric and the minimum pulse delivery time of the second plurality of pulse delivery times, the difference between the first metric and the maximum pulse delivery time of the second plurality of pulse delivery times, or the difference between the first metric and the median, mean, or mode of the second plurality of pulse delivery times.

[0109] Example 6. The system according to Example 5, wherein the processing circuitry is configured to determine, at least in part, the probability that the patient will fall by calculating a trend metric based on at least one of the difference metric or the slope of the second plurality of pulse delivery times over time.

[0110] Example 7. The system according to Example 6, wherein the processing circuitry is configured to determine the probability that the patient will fall by at least partly by comparing at least one of the difference metric or the slope of the second plurality of pulse delivery times with a past trend metric.

[0111] Example 8. The system according to any combination of Examples 3 to 7, wherein the processing circuitry is further configured to determine whether the patient has been inactive for a predetermined time period before calculating the first metric and the second metric.

[0112] Example 9. The system according to any combination of Examples 1 to 8, further comprising communication circuitry operable to transmit an alarm to an external device when it is determined that the probability of the patient falling has increased.

[0113] Example 10. A method comprising: determining, by processing circuitry, a first plurality of pulse transit times prior to a patient’s sitting-standing transition; determining, by processing circuitry and based on at least one accelerometer signal, whether the patient’s sitting-standing transition has occurred; determining, by processing circuitry and based on the occurrence of the sitting-standing transition, a second plurality of pulse transit times after the patient’s sitting-standing transition; and determining, based on the first plurality of pulse transit times and the second plurality of pulse transit times, the likelihood that the patient will fall.

[0114] Example 11. The method according to Example 10, wherein determining the probability that the patient will fall includes calculating a first metric based on the first plurality of pulse delivery times.

[0115] Example 12. According to the method of Example 11, wherein determining the probability that the patient will fall further comprises calculating a second metric based on the second plurality of pulse delivery times and comparing the second metric with the first metric.

[0116] Example 13. According to the method of Example 12, wherein the first metric includes the median, average, or mode of the first plurality of pulse transit times, and the second metric includes at least one of the slope of the second plurality of pulse transit times, the minimum pulse transit time of the second plurality of pulse transit times, the maximum pulse transit time of the second plurality of pulse transit times, or the median, average, or mode of the second plurality of pulse transit times.

[0117] Example 14. According to the method of Example 13, determining the probability that the patient will fall further includes calculating a difference metric, wherein the difference metric includes at least one of the difference between the first metric and the minimum pulse transit time of the second plurality of pulse transit times, the difference between the first metric and the maximum pulse transit time of the second plurality of pulse transit times, or the difference between the first metric and the median, mean, or mode of the second plurality of pulse transit times.

[0118] Example 15. According to the method of Example 14, determining the probability that the patient will fall further includes calculating a trend measure based on at least one of the difference measure or the slope of the second plurality of pulse delivery times over time.

[0119] Example 16. According to the method of Example 15, determining the probability that the patient will fall further comprises comparing at least one of the difference measure or the slope of the second plurality of pulse transit times with a past trend measure.

[0120] Example 17. The method according to any combination of Examples 12 to 16, further comprising determining that the patient has been inactive for a predetermined time period before calculating the first metric and the second metric.

[0121] Example 18. The method according to any combination of Examples 10 to 17, further comprising transmitting an alarm to an external device by a communication circuit and based on determining the probability that the patient will fall.

[0122] Example 19. A non-transitory computer-readable storage medium containing instructions that, when executed by a processing circuit of a device, cause the device to: determine a first plurality of pulse transit times prior to a patient’s sitting-standing transition; and determine, based on at least one accelerometer signal, whether the patient’s sitting-standing transition has occurred;

[0123] Determine a second plurality of pulse transit times following the patient's sitting-standing transition; and determine the likelihood that the patient will fall based on the first plurality of pulse transit times and the second plurality of pulse transit times.

[0124] Example 20. A non-transitory computer-readable storage medium according to Example 19, wherein the instructions cause the device to determine the probability that the patient will fall by calculating a first metric based on the first plurality of pulse delivery times.

[0125] Various aspects of this disclosure have been described. These and other aspects are within the scope of the appended claims.

Claims

1. A system comprising: an accelerometer circuit configured to generate at least one signal; a memory; and a processing circuit coupled to the accelerometer circuit and the memory, the processing circuit configured to: determine a first plurality of pulse transit times of a patient prior to a sit-to- stand transition of the patient; determine, based on the at least one accelerometer signal, whether the sit-to-stand transition of the patient occurred; determine, based on the sit-to-stand transition occurring, a second plurality of pulse transit times of the patient after the sit-to-stand transition of the patient; and determine, based on the first plurality of pulse transit times and the second plurality of pulse transit times, a likelihood that the patient will fall, wherein the processing circuit is configured to determine the likelihood that the patient will fall at least in part by calculating a first metric based on the first plurality of pulse transit times, wherein the processing circuit is further configured to determine the likelihood that the patient will fall at least in part by calculating a second metric based on the second plurality of pulse transit times.

2. The system of claim 1, wherein the first metric comprises a median, mean, or mode of the first plurality of pulse transit times, and the second metric comprises at least one of a slope of the second plurality of pulse transit times, a minimum pulse transit time of the second plurality of pulse transit times, a maximum pulse transit time of the second plurality of pulse transit times, or a median, mean, or mode of the second plurality of pulse transit times.

3. The system of claim 2, wherein the processing circuit is configured to determine the likelihood that the patient will fall at least in part by calculating a difference metric, wherein the difference metric comprises at least one of a difference between the first metric and one or more of the second metric.

4. The system of claim 3, wherein the processing circuit is configured to determine the likelihood that the patient will fall at least in part by calculating a trend metric based on at least one of the difference metric or the slope of the second plurality of pulse transit times changing over time.

5. The system of claim 4, wherein the processing circuit is configured to determine the likelihood that the patient will fall at least in part by comparing at least one of the difference metric or the slope of the second plurality of pulse transit times to a past trend metric.

6. The system of claim 1, wherein the processing circuit is further configured to determine whether the patient has been in an inactive state for a predetermined period of time prior to calculating the first metric and the second metric.

7. The system of any of the above claims, further comprising a communication circuit operable to transmit an alert to an external device when the likelihood that the patient will fall has increased.

8. A non-transitory computer-readable storage medium comprising instructions that, when executed by a processing circuit of a device, cause the device to: determine a first plurality of pulse transit times prior to a sit-to-stand transition of a patient; determine, based on at least one accelerometer signal, whether the sit-to-stand transition of the patient occurred; ​ determining a second plurality of pulse transit times after the sit-to-stand transfer of the patient; and determining a likelihood that the patient will fall based on the first plurality of pulse transit times and the second plurality of pulse transit times, wherein the instructions cause the apparatus to determine the likelihood that the patient will fall at least in part by calculating a first metric based on the first plurality of pulse transit times, wherein the instructions cause the apparatus to determine the likelihood that the patient will fall at least in part by calculating a second metric based on the second plurality of pulse transit times.

Images