Patent Description:
Implantable medical devices (IMDs) including implantable pacemakers and implantable cardioverter-defibrillators (ICDs) and insertable cardiac monitors without therapies (e.g. Medtronic LINQ™), and external, e.g., wearable medical devices, record cardiac electrogram (EGM) signals for sensing cardiac events, e.g., P-waves and R-waves. Such devices detect episodes of bradycardia, tachycardia and/or fibrillation from the sensed cardiac events, and some devices respond to the episodes as needed with pacing therapy or high-voltage anti-tachyarrhythmia shocks, e.g., cardioversion or defibrillation shocks. These and other medical devices may include, or be part of a system that includes, sensors that generate other physiological-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. Document <CIT> relates to methods and systems for measuring physiological parameters, such as heart rate.

PTT may be used to determine a measurement of pulse wave velocity (PWV). PTT indicates the time taken by a pulse wave (e.g., of an ECG signal) to travel over an estimable distance within the patient. In such examples, the estimable distance traveled by the pulse wave may be divided by a determined PTT value to arrive at a PWV value.

In general, this disclosure is directed to techniques for determining an increase in the likelihood a patient may fall based on a measured PTT. More particularly, this disclosure contemplates a medical system and method that monitors the patient for a Sit-to-Stand transition (e.g., the patient transitioning from a sitting position to a standing position) and measures the patient's PTT prior to and after the Sit-to-Stand transition. People may experience a change in blood pressure when transitioning from a sitting position to a standing position. This change in blood pressure may cause a person to feel light-headed or presyncope. PTT may be a surrogate for blood pressure. By measuring PTT prior to and after a Sit-to-Stand transition, the system and techniques of this disclosure may determine the likelihood that a patient may fall and facilitate changes in treatment of the patient. The PWV value may be used instead of or in addition to the PTT value in assessing the body stability of a patient including the likelihood of the patient falling.

A comparison of current values based on PTT to corresponding baseline values may be used to determine a status of the patient's likelihood of falling. In techniques described herein, one or more IMDs or external devices may determine a PTT and transmit an indication of the patient's body stability or the likelihood of the patient falling to a remote computer or other device external to the patient. The remote computer or other device then may transmit instructions for a medical intervention (e.g., instructions for changes to a drug regimen or physical therapy), to a user device used by the patient or a caregiver. In addition to or as an alternative to transmitting instructions, the remote computer may control the one or more IMDs to deliver a treatment, such as stimulating the heart or a nerve or delivering a drug through a drug pump. In this manner, a patient's treatment may be modified as needed to mitigate the risk of the patient falling.

According to the invention, a system according to claim <NUM> is disclosed. Further embodiments are disclosed by claims <NUM>-<NUM>.

Further according to the invention, a non-transitory computer-readable storage medium according to claim <NUM> is disclosed.

This summary is intended to provide an overview of the subject matter described in this disclosure. This summary is not intended to provide an exclusive or exhaustive explanation of the apparatus and methods described in detail within the accompanying drawings and description below.

<FIG> is a conceptual drawing illustrating an example of a medical device system including a leadless implantable medical device and an external device in conjunction with a patient.

A medical device system according to certain features or aspects of this disclosure includes accelerometer circuitry configured to generate a number of signals including a sagittal (frontal) axis signal, as well as processing circuitry configured to detect a Sit-to-Stand transition and calculate a plurality of PTTs prior to and after the Sit-to-Stand transition. The system may determine differences between the plurality of PTTs taken after the Sit-to-Stand transition and those taken prior to the Sit-to-Stand transition and monitor those differences over time to determine the likelihood of the patient falling. Such an implementation may, among other things, provide an objective measure of change (or not) in well-being to help guide therapies, because a PTT metric surrounding a Sit-To-Stand transition may help determine whether health is improving, declining, or stable. Although not so limited, an appreciation of the various aspects of the present disclosure may be gained from the following discussion in connection with the drawings. While this disclosure may provide examples, including identifying medical devices that may be configured to implement the techniques described herein, these identifications are not meant to be limiting. Any devices having an accelerometer and configured to measure a PTT may be used to implement the techniques of this disclosure.

<FIG> illustrates the environment of an example medical device system <NUM> in conjunction with a patient <NUM> and a heart <NUM>, in accordance with an apparatus and method of certain examples described herein. The example techniques may be used with a leadless subcutaneously-implantable medical device (IMD) <NUM>, which may be in wireless communication with external device <NUM>. In some examples, IMD <NUM> may be implanted outside of a thoracic cavity of patient <NUM> (e.g., subcutaneously in the pectoral location illustrated in <FIG>). IMD <NUM> may be positioned near the sternum near or just below the level of heart <NUM>, e.g., at least partially within the cardiac silhouette. In some examples, IMD <NUM> may take the form of a Reveal LINQ™ Insertable Cardiac Monitor (ICM), available from Medtronic plc, of Dublin, Ireland. External device <NUM> may be a computing device configured for use in settings such as a home, clinic, or hospital, and may further be configured to communicate with IMD <NUM> via wireless telemetry. For example, external device <NUM> may be coupled to a remote patient monitoring system, such as Carelink®, available from Medtronic plc, of Dublin, Ireland. External device <NUM> may, in some examples, comprise a programmer, an external monitor, or a consumer device such as a smart phone or tablet.

IMD <NUM> may include a plurality of electrodes and one or more optical sensors, which collectively detect signals that enable processing circuitry, e.g., of the IMD <NUM>, to determine values of PTT prior to and after a Sit-to-Stand transition for patient <NUM>, and determine the likelihood that patient <NUM> may fall based on such values. In some examples, processing circuitry of IMD <NUM> may determine a Sit-to-Stand transition has occurred based on an accelerometer signal. In some examples, processing circuitry of IMD <NUM> also may use an ECG signal detected by the plurality of electrodes to determine PTT values of patient <NUM> prior to and after the Sit-to-Stand transition. In other examples, processing circuitry of IMD <NUM> may use signals detected by one or more optical sensors positioned on a surface of IMD <NUM> to determine PTT values in conjunction with the ECG signal prior to and after the Sit-to-Stand transition.

Although not necessarily illustrated in the example of <FIG>, 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 instead of IMD <NUM>. For example, a medical device system may include a pressure sensing IMD, vascular ICD, extravascular ICD, cardiac pacemaker or other external device. One or more of 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 body stability based on accelerometer-generated data. The implanted devices may communicate with each other and/or an external device <NUM>, and one of the implanted or external devices may ultimately calculate PTTs just prior to and after a Sit-To-Stand transition from at least one of a sagittal axis signal, a vertical axis signal and a transverse axis signal.

Accelerometer signals coincident with a sagittal axis of a patient may be leveraged for determining a Sit-To-Stand transition, for example. This is because 3D accelerometers in the IMD <NUM>, which is implanted in the chest, for example, are relatively stationary over the lifetime of the implant. The stationary chest location presents an opportunity to monitor changes in the upper body that occur during various activities. As a patient gets in and out of a chair for example the upper body has a reproducible motion (similar to a "bowing" motion) that may be identified with signals produced by the accelerometers.

After determining PTT values of patient <NUM> prior to and after the Sit-to-Stand transition, processing circuitry, e.g., of IMD <NUM>, calculates a first metric based on the first plurality of PTTs (e.g., the PTTs prior to the Sit-to-Stand transition), for example, a mean, median or mode of the first plurality of PTTs. Processing circuitry also calculates second metrics based on the second plurality of PTTs (e.g., the PTTs after the Sit-to-Stand transition).

In some examples, processing circuitry may also calculate difference metrics between the first metric and the second metrics based on PTT values prior to and after the Sit-to-Stand transition and compare the difference metrics to corresponding baseline values of difference metrics, e.g., stored in a memory of IMD <NUM>, to determine differences therebetween. If the differences between one or more of the PTT difference metrics and corresponding baseline values of difference metrics satisfies a threshold, then the processing circuitry may determine that patient <NUM> is more likely to fall relative to a time when the baseline values were established.

In addition to or alternatively, processing circuitry, e.g. of IMD <NUM>, may calculate a slope of PTTs after the Sit-to-Stand transition. Processing circuitry, e.g. of IMD <NUM> may compare the slope of the PTTs after the Sit-to-Stand transition to corresponding baseline value(s) of slope of the PTTs after the Sit-to-Stand transition, e.g., stored in a memory of IMD <NUM>, to determine differences therebetween. If the differences between the slope of the PTTs after the Sit-to-Stand transition and a corresponding baseline satisfies a threshold, then the processing circuitry may determine that patient <NUM> is more likely to fall relative to a time when the baseline value was established.

Regardless of whether any such differences satisfy a threshold, IMD <NUM> then may wirelessly transmit data associated with the difference metrics, the slope of the PTTs after the Sit-to-Stand transition and/or PTT values to external device <NUM>. IMD <NUM> may transmit the data associated with the PTT values to external device <NUM> at predetermined intervals, such as daily, weekly, or at any other desired period or may transmit the data associated with the difference metrics, the slope of the PTTs after the Sit-to-Stand transition and/or PTT values upon a user request on external device <NUM>.

In some examples, IMD <NUM> may be configured to undertake a learning phase after implantation into patient <NUM>, in which IMD <NUM> determines the baseline values of the difference metrics and/or the slope of the PTTs after a Sit-to-Stand transition for patient <NUM> based on values collected by IMD <NUM> over a period of time, and stores the baseline values in a memory of IMD <NUM>. For example, IMD <NUM> may measure PTT prior to and after each Sit-to-Stand for a period of time (e.g., a week or more) to determine baseline values during a period when the physiological condition of patient <NUM> is stable.

In other examples, instead of determining baseline values, a clinician may select baseline values for patient <NUM>. Lists or tables of such baseline values may be presented by an app on the clinician tablet or other smart device, or may be available from a centralized database. Once the clinician has selected appropriate baseline values for patient <NUM>, the clinician may use the app to store the values in IMD <NUM>.

Values for baselines and thresholds associated with patient <NUM> may be updated periodically. For example, IMD <NUM> may undertake a new learning phase daily, weekly, monthly, quarterly, yearly, or at an expiration of any other appropriate period. The new learning phase may produce new values associated with one or more of the baseline values and thresholds of patient <NUM>. In other examples, a clinician may program IMD <NUM> to update such values as needed, such as following a falling event experienced by patient <NUM>.

In some examples, IMD <NUM> may determine baseline values based on median, mean or mode PTT values collected during the training period. In other examples, IMD <NUM> may reject outlier values collected during the training period prior to determining the baseline values. In some examples, a baseline value may be indicative of a value of a difference between a PTT prior to a Sit-to-Stand transition and a PTT after the Sit-to-Stand transition. In addition to determining baseline values for patient <NUM>, IMD <NUM> or a clinician also may determine threshold values for patient <NUM> and store the threshold values in a memory of IMD <NUM>.

IMD <NUM> may determine threshold values for each of a number of different baseline values, such as during the training period of IMD <NUM>. In some examples, IMD <NUM> may automatically associate a particular threshold value with a particular baseline value for patient <NUM>. In some examples, the thresholds may be determined using Statistical Process Control (SPC) of the baseline for comparison to the current value (e.g., a corresponding current difference metric). In such examples, the thresholds may be useful in detecting an acute change in the falling risk of patient <NUM>. In other examples, Change Point Analysis (CPA) may be applied to determine if there has been a significant change in the slope of PTT after a Sit-to-Stand transition with a time series of slope values (e.g., different slopes measured over time) from the baseline slope value. A significant change in the slope of PTT after a Sit-to-Stand transition with a time series of slope values from the baseline slope value may be indicative of a chronic change in the falling risk of patient <NUM>. The SPC and/or the CPA may be performed by IMD <NUM>, external device <NUM> or external device <NUM> (of <FIG>), for example. In other examples, a clinician may choose to program IMD <NUM> to apply relatively higher or lower thresholds than those selected by processing circuitry of IMD <NUM> based on other considerations known to the clinician.

Regardless of whether the threshold values are determined by processing circuitry of IMD <NUM> during a training period or by a clinician, such threshold values may be updated at one or more times after implantation of IMD <NUM>. For example, threshold values may be updated after patient <NUM> experiences a falling event. Or, the threshold values may be updated at the expiration of a time period (e.g., weekly, monthly, or yearly following implantation of IMD <NUM>). Such updates to the threshold values may be performed automatically by processing circuitry of IMD <NUM>, or manually by a clinician. In any such examples, the updated threshold values may be determined based on trends in PTT during the preceding time period. In this manner, the threshold values used in the techniques described herein may be modified as needed to account for changes in patient <NUM>'s health.

External device <NUM> may be used to program commands or operating parameters into IMD <NUM> for controlling its functioning (e.g., when configured as a programmer for IMD <NUM>). In some examples, external device <NUM> may be used to interrogate IMD <NUM> to retrieve data, including device operational data as well as physiological data accumulated in IMD memory. Such interrogation may occur automatically according to a schedule, or may occur in response to a remote or local user command. Programmers, external monitors, and consumer devices are examples of external devices <NUM> that may be used to interrogate IMD <NUM>. Examples of communication techniques used by IMD <NUM> and external device <NUM> include radiofrequency (RF) telemetry, which may be an RF link established via Bluetooth®, a wireless local area network, or medical implant communication service (MICS). In some examples, external device <NUM> may include a user interface configured to allow a clinician to remotely interact with IMD <NUM>.

Medical system <NUM> is an example of a medical device system configured to determine a patient's risk of falling by monitoring PTT prior to and after a Sit-to-Stand transition. The techniques described herein may be performed by processing circuitry of a device of medical system <NUM>, such as processing circuitry of IMD <NUM>. Additionally, or alternatively, the techniques described herein may be performed, in whole or in part, by processing circuitry of external device <NUM>, and/or by processing circuitry of one or more other implanted or external devices or servers not shown. Examples of the one or more other implanted or external devices may include a transvenous, subcutaneous, or extravascular pacemaker or implantable cardioverter-defibrillator (ICD), a blood analyzer, an external monitor, or a drug pump. The communication circuitry of each of the devices of system <NUM> allows the devices to communicate with one another. In addition, although the optical sensors and electrodes are described herein as being positioned on a housing of IMD <NUM>, in other examples, such optical sensors and/or electrodes may be positioned on a housing of another device implanted in or external to patient <NUM>, such as a transvenous, subcutaneous, or extravascular pacemaker or ICD, or coupled to such a device by one or more leads. For example, electrodes or one or more optical sensors for detecting signals associated with PTT may be positioned on one or more external monitoring devices (e.g., a wearable monitor). In such examples, one or more of the pacemaker/ICD and the 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 the signals from the electrodes or optical sensors to another device (e.g., external device <NUM>) or server.

<FIG> illustrate various aspects and example arrangements of IMD <NUM> of <FIG>. For example, <FIG> conceptually illustrates an example physical configuration of IMD <NUM>. <FIG> is a block diagram illustrating an example functional configuration of IMD <NUM>. <FIG> and <FIG> illustrate additional views of an example physical and functional configuration of IMD <NUM>. It should be understood that any of the examples of IMD <NUM> described below with respect to <FIG> may be used to implement the techniques described herein for determining a falling risk of patient <NUM>.

<FIG> is a conceptual drawing illustrating an example configuration of IMD <NUM> of <FIG>. In the example shown in <FIG>, IMD <NUM> may comprise a leadless, subcutaneously-implantable monitoring device having housing <NUM>, proximal electrode 16A, and distal electrode 16B. Housing <NUM> may further comprise first major surface <NUM>, second major surface <NUM>, proximal end <NUM>, and distal end <NUM>. In some examples, IMD <NUM> may include one or more additional electrodes 16C, 16D positioned on one or both of major surfaces <NUM>, <NUM> of IMD <NUM>. Housing <NUM> encloses electronic circuitry located inside the IMD <NUM>, and protects the circuitry contained therein from fluids such as body fluids. In some examples, electrical feedthroughs provide electrical connection of electrodes 16A-16D, and antenna <NUM>, to circuitry within housing <NUM>. In some examples, electrode 16B may be formed from an uninsulated portion of conductive housing <NUM>.

In the example shown in <FIG>, IMD <NUM> is defined by a length L, a width W, and thickness or depth D. In this example, IMD <NUM> is in the form of an elongated rectangular prism in which length L is significantly greater than width W, and in which width W is greater than depth D. However, other configurations of IMD <NUM> are contemplated, such as those in which the relative proportions of length L, width W, and depth D vary from those described and shown in <FIG>. In some examples, the geometry of the IMD <NUM>, such as the width W being greater than the depth D, may be selected to allow IMD <NUM> to be inserted under the skin of the patient using a minimally invasive procedure and to remain in the desired orientation during insertion. In addition, IMD <NUM> may include radial asymmetries (e.g., the rectangular shape) along a longitudinal axis of IMD <NUM>, which may help maintain the device in a desired orientation following implantation.

In some examples, a spacing between proximal electrode 16A and distal electrode 16B may range from about <NUM>-<NUM>, about <NUM>-<NUM>, or about <NUM>-<NUM>, or more generally from about <NUM>-<NUM>. Overall, IMD <NUM> may have a length L of about <NUM>-<NUM>, about <NUM>-<NUM>, or about <NUM>-<NUM>. In some examples, the width W of major surface <NUM> may range from about <NUM>-<NUM>, and may be any single width or range of widths between about <NUM>-<NUM>. In some examples, a depth D of IMD <NUM> may range from about <NUM>-<NUM>. In other examples, the depth D of IMD <NUM> may range from about <NUM>-<NUM>, and may be any single or range of depths from about <NUM>-<NUM>. In any such examples, IMD <NUM> is sufficiently compact to be implanted within the subcutaneous space of patient <NUM> in the region of a pectoral muscle.

IMD <NUM>, according to an example of the present disclosure, may have a geometry and size designed for ease of implant and patient comfort. Examples of IMD <NUM> described in this disclosure may have a volume of <NUM> cubic centimeters (cm3) or less, <NUM> cm3 or less, or any volume therebetween. In addition, in the example shown in <FIG>, proximal end <NUM> and distal end <NUM> are rounded to reduce discomfort and irritation to surrounding tissue once implanted under the skin of patient <NUM>.

In the example shown in <FIG>, first major surface <NUM> of IMD <NUM> faces outward towards the skin, when IMD <NUM> is inserted within patient <NUM>, whereas second major surface <NUM> is faces inward toward musculature of patient <NUM>. Thus, first and second major surfaces <NUM>, <NUM> may face in directions along a sagittal axis of patient <NUM> (see <FIG>), and this orientation may be maintained upon implantation due to the dimensions of IMD <NUM>.

Proximal electrode 16A and distal electrode 16B may be used to sense cardiac EGM signals (e.g., ECG signals) when IMD <NUM> is implanted subcutaneously in patient <NUM>. In the techniques described herein, processing circuitry of IMD <NUM> may determine a PTT value based in part on cardiac ECG signals, as further described below. The cardiac ECG signals may be stored in a memory of the IMD <NUM>, and data derived from the cardiac ECG signals may be transmitted via integrated antenna <NUM> to another medical device, such as external device <NUM>.

In the example shown in <FIG>, proximal electrode 16A is in close proximity to proximal end <NUM>, and distal electrode 16B is in close proximity to distal end <NUM> of IMD <NUM>. In this example, distal electrode 16B is not limited to a flattened, outward facing surface, but may extend from first major surface <NUM>, around rounded edges <NUM> or end surface <NUM>, and onto the second major surface <NUM> in a three-dimensional curved configuration. As illustrated, proximal electrode 16A is located on first major surface <NUM> and is substantially flat and outward facing. However, in other examples not shown here, proximal electrode 16A and distal electrode 16B both may be configured like proximal electrode 16A shown in <FIG>, or both may be configured like distal electrode 16B shown in <FIG>. In some examples, additional electrodes 16C and 16D may be positioned on one or both of first major surface <NUM> and second major surface <NUM>, such that a total of four electrodes are included on IMD <NUM>. Any of electrodes 16A-16D may be formed of a biocompatible conductive material. For example, any of electrodes 16A-16D may be formed from any of stainless steel, titanium, platinum, iridium, or alloys thereof. In addition, electrodes of IMD <NUM> may be coated with a material such as titanium nitride or fractal titanium nitride, although other suitable materials and coatings for such electrodes may be used.

In the example shown in <FIG>, proximal end <NUM> of IMD <NUM> includes header assembly <NUM> having one or more of proximal electrode 16A, integrated antenna <NUM>, anti-migration projections <NUM>, and suture hole <NUM>. Integrated antenna <NUM> is located on the same major surface (e.g., first major surface <NUM>) as proximal electrode 16A, and may be an integral part of header assembly <NUM>. In other examples, integrated antenna <NUM> may be formed on the major surface opposite from proximal electrode 16A, or, in still other examples, may be incorporated within housing <NUM> of IMD <NUM>. Antenna <NUM> may be configured to transmit or receive electromagnetic signals for communication. For example, antenna <NUM> may be configured to transmit to or receive signals from a programmer via inductive coupling, electromagnetic coupling, tissue conductance, Near Field Communication (NFC), Radio Frequency Identification (RFID), Bluetooth®, wireless local area network, or other proprietary or non-proprietary wireless telemetry communication schemes. Antenna <NUM> may be coupled to communication circuitry of IMD <NUM>, which may drive antenna <NUM> to transmit signals to external device <NUM>, and may transmit signals received from external device <NUM> to processing circuitry of IMD <NUM> via communication circuitry.

IMD <NUM> may include several features for retaining IMD <NUM> in position once subcutaneously implanted in patient <NUM>. For example, as shown in <FIG>, housing <NUM> may include anti-migration projections <NUM> positioned adjacent integrated antenna <NUM>. Anti-migration projections <NUM> may comprise a plurality of bumps or protrusions extending away from first major surface <NUM>, and may help prevent longitudinal movement of IMD <NUM> after implantation in patient <NUM>. In other examples, anti-migration projections <NUM> may be located on the opposite major surface as proximal electrode 16A and/or integrated antenna <NUM>. In addition, in the example shown in <FIG> header assembly <NUM> includes suture hole <NUM>, which provides another means of securing IMD <NUM> to the patient to prevent movement following insertion. In the example shown, suture hole <NUM> is located adjacent to proximal electrode 16A. In some examples, header assembly <NUM> may comprise a molded header assembly made from a polymeric or plastic material, which may be integrated or separable from the main portion of IMD <NUM>.

IMD <NUM> determines values of PTTs of patient <NUM> based on signals received from one or more of electrodes 16A-16D, light emitter <NUM>, and light detectors 40A, 40B. Electrodes 16A and 16B may be used to sense cardiac ECG signals for PTT value determination, as described herein. Additional electrodes 16C and 16D may be used to sense subcutaneous tissue impedance (e.g., for measuring PTT), in addition to or instead of electrodes 16A, 16B, in some examples.

In some examples, processing circuitry of IMD <NUM> may determine a PTT value of patient <NUM> prior to and after a Sit-to-Stand transition based on the sensed ECG signal from electrodes 16A, 16B and a current subcutaneous tissue impedance based on signals received from electrodes 16C and 16D. For example, the processing circuitry of IMD <NUM> may receive the ECG signal from electrodes 16A, 16B, and identify one or more features of a cardiac cycle within the ECG signal. For example, the processing circuitry may identify an R wave within a cardiac cycle, and associate a first time (T <NUM>) with the occurrence of the R wave. Next, the processing circuitry may identify a fluctuation in the subcutaneous tissue impedance signal occurring after T1, and associate a second time (T2) with the fluctuation, which may represent the passing of blood ejected during the observed cardiac cycle through the portion of the vasculature near electrodes 16C, 16D. By subtracting T2 from T1, processing circuitry of IMD <NUM> then may determine a PTT value (e.g., in milliseconds) of patient <NUM>. To enable IMD <NUM> to accurately identify fluctuations in PTT values of patient <NUM>, it may be beneficial for a clinician to implant IMD <NUM> substantially as shown in <FIG>, with at least a portion of IMD <NUM> positioned at or inferior to heart <NUM> and subcutaneously or otherwise not adjacent to a central arterial blood flow. In this way, IMD <NUM> may be positioned at a sufficient circulatory distance from heart <NUM> to detect even small fluctuations in PTT, which may help IMD <NUM> to accurately assess the falling risk of patient <NUM>.

In the example shown in <FIG>, IMD <NUM> includes a light emitter <NUM>, a proximal light detector 40A, and a distal light detector 40B positioned on housing <NUM> of IMD <NUM>. Light detector 40A may be positioned at a distance S from light emitter <NUM>, and a distal light detector 40B positioned at a distance S+N from light emitter <NUM>. In other examples, IMD <NUM> may include only one of light detectors 40A, 40B, or may include additional light emitters and/or additional light detectors. Collectively, light emitter <NUM> and light detectors 40A, 40B may comprise an optical sensor, which may be used in the techniques described herein to determine PTT values of patient <NUM>. Although light emitter <NUM> and light detectors 40A, 40B are described herein as being positioned on housing <NUM> of IMD <NUM>, in other examples, one or more of light emitter <NUM> and light detectors 40A, 40B may be positioned, on a housing of another type of IMD within patient <NUM>, such as a transvenous, subcutaneous, or extravascular pacemaker or ICD, or connected to such a device via a lead. Light emitter <NUM> includes a light source, such as an LED, that may emit light at one or more wavelengths within the (VIS) and/or (NIR) spectra. For example, light emitter <NUM> may emit light at one or more of about <NUM> (nm), <NUM>, <NUM>, <NUM>, or at any other suitable wavelengths.

As shown in <FIG>, light emitter <NUM> may be positioned on header assembly <NUM>, although, in other examples, one or both of light detectors 40A, 40B may additionally or alternatively be positioned on header assembly <NUM>. In some examples, light emitter <NUM> may be positioned on a medial section of IMD <NUM>, such as part way between proximal end <NUM> and distal end <NUM>. Although light emitter <NUM> and light detectors 40A, 40B are illustrated as being positioned on first major surface <NUM>, light emitter <NUM> and light detectors 40A, 40B alternatively may be positioned on second major surface <NUM>. In some examples, IMD may be implanted such that light emitter <NUM> and light detectors 40A, 40B face inward when IMD <NUM> is implanted, toward the muscle of patient <NUM>, which may help minimize interference from background light coming from outside the body of patient <NUM>. Light detectors 40A, 40B may include a glass or sapphire window, such as described below with respect to <FIG>, or may be positioned beneath a portion of housing <NUM> of IMD <NUM> that is made of glass or sapphire, or otherwise transparent or translucent.

As noted above, light emitter <NUM> and one or both of light detectors 40A, 40B may be used in a technique for determining a PTT value of patient <NUM>. As with techniques for determining PTT in which processing circuitry of IMD <NUM> receives a subcutaneous tissue impedance signal from a plurality of electrodes 16A-16D, techniques for determining PTT that include using an optical sensor include identifying one or more features within a cardiac cycle of patient <NUM>, and associating a first time T1 with an occurrence in the cardiac cycle. Instead of determining a second time T2 based on an impedance signal, however, IMD <NUM> may determine T2 by identifying a fluctuation in the intensity and/or wavelength of light detected by one or both of light detectors 40A, 40B occurring after T1, and associate the second time (T2) with the fluctuation, which may represent the passing of blood ejected during the cardiac cycle through the portion of the vasculature near the light detectors 40A, 40B. By subtracting T2 from T1, processing circuitry of IMD <NUM> then may determine a PTT value (e.g., in milliseconds) of patient <NUM>.

In some examples, IMD <NUM> may include one or more additional sensors, such as one or more accelerometers <NUM>. Such accelerometers <NUM> may be 3D accelerometers configured to generate signals indicative of one or more types of movement of the patient, such as gross body movement (e.g., activity) of the patient, patient posture, movements associated with the beating of the heart, or coughing, rales, or other respiration abnormalities. In some examples, one or more of such accelerometers may be used, in conjunction with light emitter <NUM> and optical detectors 40A, 40B, to determine a ballistocardiogram (i.e., a measure of motion corresponding to blood ejection at systole) that processing circuitry of IMD <NUM> may use to determine PTT instead of or in addition to an ECG signal from a pair of electrodes 16A-16D. IMD <NUM> may also monitor accelerometer signal(s) to determine that patient <NUM> has made a Sit-to-Stand transition. IMD <NUM> may also monitor accelerometer signal(s) to determine whether patient <NUM> is active. IMD <NUM> may determine the PTT of patient <NUM> prior to and after the Sit-to-Stand transition, may determine difference metrics between the PPT of patient <NUM> prior to and the PPT after the Sit-to-Stand transition, and may determine if a value of the difference metrics exceeds a threshold which may be indicative of an increase in the likelihood that patient <NUM> may fall.

Although processing circuitry of IMD <NUM> is described above as being configured to receive signals from one or more accelerometers, electrodes 16A-16D, light emitter <NUM>, and/or light detectors 40A, 40B of IMD <NUM> and determine a value of one or more parameters of patient <NUM> based on such signals, any steps described herein as being carried out by processing circuitry of IMD <NUM> may carried out by processing circuitry of one or more devices. For example, processing circuitry of external device <NUM>, or any other suitable implantable or external device or server, may be configured to receive signals from the one or more accelerometers, electrodes 16A-16D, light emitter <NUM>, and/or light detectors 40A, 40B of IMD <NUM>, such as via communication circuitry of IMD <NUM>.

<FIG> is a functional block diagram illustrating an example configuration of IMD <NUM> of <FIG> and <FIG>. In the illustrated example, IMD <NUM> includes processing circuitry <NUM> sensing circuitry <NUM>, communication circuitry <NUM>, memory <NUM>, switching circuitry <NUM>, sensors <NUM>, accelerometer <NUM>, in addition to previously-described electrodes 16A-16D, one or more of which may be disposed within housing <NUM> of IMD <NUM>, and light emitter <NUM>. In some examples, memory <NUM> includes computer-readable instructions that, when executed by processing circuitry <NUM>, cause IMD <NUM> and processing circuitry <NUM> to perform various functions attributed to IMD <NUM> and processing circuitry <NUM> herein. Memory <NUM> may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital media.

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

As illustrated in <FIG>, memory <NUM> also may include one or more tables <NUM> for storing baseline and threshold level values. As described above, in some examples, processing circuitry <NUM> of IMD <NUM> may be configured to determine baseline values of PTT differences during a learning phase of IMD <NUM>, which then may be stored in tables <NUM>. In addition, tables <NUM> may include pre-programmed baseline values that a clinician may select for patient <NUM> during setup of IMD <NUM>, or baseline values that a clinician may manually enter based on the clinician's assessments of patient <NUM>. Processing circuitry <NUM> also may be configured to determine threshold values for deviations of difference values of PTT from the baseline values, and store the threshold values in tables <NUM>. In some examples, processing circuitry <NUM> may determine such threshold values based, at least in part, on baseline values selected for patient <NUM>. In addition to the baseline values, tables <NUM> may include threshold values that a clinician may select for patient <NUM> during setup of IMD <NUM>, or threshold values that a clinician may manually enter based on the clinician's assessments of patient <NUM>.

Sensing circuitry <NUM> and communication circuitry <NUM> may be selectively coupled to electrodes 16A-16D via switching circuitry <NUM>, as controlled by processing circuitry <NUM>. Sensing circuitry <NUM> may monitor signals from electrodes 16A-16D in order to monitor electrical activity of heart (e.g., to produce an ECG for PTT determination), and/or subcutaneous tissue impedance Z (e.g., for PTT determination). Sensing circuitry <NUM> also may monitor signals from sensors <NUM>, which may include light detectors 40A, 40B, and any additional light detectors that may be positioned on IMD <NUM>. In some examples, sensing circuitry <NUM> may include one or more filters and amplifiers for filtering and amplifying signals received from one or more of electrodes 16A-16D and/or light detectors 40A, 40B.

In some examples, processing circuitry <NUM> also may include a rectifier, filter and/or amplifier, a sense amplifier, comparator, and/or analog-to-digital converter. Upon receiving signals from electrodes 16A-16D and light detectors 40A, 40B via sensing circuitry <NUM>, processing circuitry <NUM> may determine PTT for patient <NUM>. Processing circuitry then may compare the PTT to the baseline levels stored in tables <NUM>, and determine whether differences between the current values and the corresponding baseline levels satisfy corresponding thresholds stored in tables <NUM>.

Processing circuitry <NUM> may store the determined values in difference metrics/slopes <NUM> of memory <NUM>, along with an indication of a date and time of the measurements. Simultaneously or thereafter, processing circuitry <NUM> may transmit, via communication circuitry <NUM> an indication that patient <NUM> is more likely to fall to external device <NUM>.

Communication circuitry <NUM> may include any suitable hardware, firmware, software or any combination thereof for communicating with another device, such as external device <NUM> or another IMD or sensor, such as a pressure sensing device. Under the control of processing circuitry <NUM>, communication circuitry <NUM> may receive downlink telemetry from, as well as send uplink telemetry to, external device <NUM> or another device with the aid of an internal or external antenna, e.g., antenna <NUM>. In some examples, communication circuitry <NUM> may communicate with external device <NUM>. In addition, processing circuitry <NUM> may communicate with a networked computing device via external device (e.g., external device <NUM>) and a computer network, such as the Medtronic CareLink® Network developed by Medtronic, plc, of Dublin, Ireland.

A clinician or other user may retrieve data from IMD <NUM> using external device <NUM>, or by using another local or networked computing device configured to communicate with processing circuitry <NUM> via communication circuitry <NUM>. The clinician may also program parameters of IMD <NUM> using external device <NUM> or another local or networked computing device. In some examples, the clinician may select baseline values and threshold values.

The various components of IMD <NUM> may be coupled a power source, which may include a rechargeable or non-rechargeable battery positioned within housing <NUM> of IMD <NUM>. A non-rechargeable battery may be selected to last for several years, while a rechargeable battery may be inductively charged from an external device, e.g., on a daily or weekly basis.

<FIG> and <FIG> illustrate two additional example IMDs that may be substantially similar to IMD <NUM> of <FIG>, but which may include one or more additional features. The components of <FIG> and <FIG> may not necessarily be drawn to scale, but instead may be enlarged to show detail. <FIG> is a block diagram of a top view of an example configuration of an IMD 10A. <FIG> is a block diagram of a side view of example IMD 10B, which may include an insulative layer as described below.

<FIG> is a conceptual drawing illustrating another example IMD 10A that may be substantially similar to IMD <NUM> of <FIG>. In addition to the components illustrated in <FIG>, the example of IMD <NUM> illustrated in <FIG> also may include a body portion <NUM> and an attachment plate <NUM>. Attachment plate <NUM> may be configured to mechanically couple header <NUM> to body portion <NUM> of IMD 10A. Body portion <NUM> of IMD 10A may be configured to house one or more of the internal components of IMD <NUM> illustrated in <FIG>, such as one or more of processing circuitry <NUM>, sensing circuitry <NUM>, communication circuitry <NUM>, memory <NUM>, switching circuitry <NUM>, internal components of sensors <NUM>, and timing control circuitry <NUM>. In some examples, body portion <NUM> may be formed of one or more of titanium, ceramic, or any other suitable biocompatible materials.

<FIG> is a conceptual drawing illustrating another example IMD 10B that may include components substantially similar to IMD <NUM> of <FIG>. In addition to the components illustrated in <FIG>, the example of IMD 10B illustrated in <FIG> also may include a wafer-scale insulative cover <NUM>, which may help insulate electrical signals passing between electrodes 16A-16D and/or optical detectors 40A, 40B on housing 14B and processing circuitry <NUM>. In some examples, insulative cover <NUM> may be positioned over an open housing <NUM> to form the housing for the components of IMD 10B. One or more components of IMD 10B (e.g., antenna <NUM>, light emitter <NUM>, light detectors 40A, 40B, processing circuitry <NUM>, sensing circuitry <NUM>, communication circuitry <NUM>, switching circuitry <NUM>, and/or timing/control circuitry <NUM>) may be formed on a bottom side of insulative cover <NUM>, such as by using flip-chip technology. Insulative cover <NUM> may be flipped onto a housing 14B. When flipped and placed onto housing 14B, the components of IMD 10B formed on the bottom side of insulative cover <NUM> may be positioned in a gap <NUM> defined by housing 14B.

Insulative cover <NUM> may be configured so as not to interfere with the operation of IMD 10B. For example, one or more of electrodes 16A-16D may be formed or placed above or on top of insulative cover <NUM>, and electrically connected to switching circuitry <NUM> through one or more vias (not shown) formed through insulative cover <NUM>. In addition, to enable IMD 10B to determine values of PTT, at least a portion of insulative cover <NUM> may be transparent to the NIR or visible wavelengths emitted by light emitter <NUM> and detected by light detectors 40A, 40B, which in some examples may be positioned on a bottom side of insulative cover <NUM> as described above.

In some examples, light emitter <NUM> may include an optical filter between light emitter <NUM> and insulative cover <NUM>, which may limit the spectrum of emitted light to be within a narrow band. Similarly, light detectors 40A, 40B may include optical filters between light detectors 40A, 40B and insulative cover <NUM>, so that light detectors 40A, 40B detects light from a narrow spectrum, generally at longer wavelengths than the emitted spectrum. Other optical elements that may be included in the IMD 10B may include index matching layers, antireflective coatings, or optical barriers, which may be configured to block light emitted sideways by the light emitter <NUM> from reaching light detector <NUM>.

Insulative cover <NUM> may be formed of sapphire (i.e., corundum), glass, parylene, and/or any other suitable insulating material. Sapphire may be greater than <NUM>% transmissive for wavelengths in the range of about <NUM> to about <NUM>, and may have a relatively flat profile. In the case of variation, different transmissions at different wavelengths may be compensated for, such as by using a ratiometric approach. In some examples, insulative cover <NUM> may have a thickness of about <NUM> micrometers to about <NUM> micrometers. Housing 14B may be formed from titanium or any other suitable material.

(e.g., a biocompatible material), and may have a thickness of about <NUM> micrometers to about <NUM> micrometers. These materials and dimensions are examples only, and other materials and other thicknesses are possible for devices of this disclosure.

<FIG> is a functional block diagram illustrating an example configuration of an external device <NUM> configured to communicate with one or more IMDs <NUM>. In the example of <FIG>, external device <NUM> includes processing circuitry <NUM>, memory <NUM>, user interface (UI) <NUM>, and communication circuitry <NUM>. External device <NUM> may correspond to any of external devices <NUM> described with respect to <FIG> and <FIG>. External device <NUM> may be a dedicated hardware device with dedicated software for the programming and/or interrogation of an IMD <NUM>. Alternatively, external device <NUM> may be an off-the-shelf computing device, e.g., a smartphone running a mobile application that enables external device <NUM> to program and/or interrogate IMD <NUM>. In some examples where external device <NUM> is a smart phone, external device <NUM> may include a mobile application to facilitate interaction with IMD <NUM>.

In some examples, a user of external device <NUM> may be clinician, physician, heath care giver, patient, family member of the patient or friend of the patient. In some examples, a user uses external device <NUM> to select or program any of the values for operational parameters of IMD <NUM>, e.g., for measuring or determining patient body stability based on PTT. In some examples, a user uses external device <NUM> to receive data collected by IMD <NUM>, such as difference metrics/slopes <NUM> or other operational and performance data of IMD <NUM>. The user may also receive alerts provided by IMD <NUM> that indicate that an acute cardiac event, e.g., ventricular tachyarrhythmia, is predicted. The user may also receive alerts that the patient may be more likely to fall or that the patient needs attention due to deterioration of the patient's body stability. The user may interact with external device <NUM> via UI <NUM>, which may include a display to present a graphical user interface to a user, and a keypad or another mechanism (such as a touch sensitive screen) for receiving input from a user. External device <NUM> may communicate wirelessly with IMD <NUM> using communication circuitry <NUM>, which may be configured for RF communication with communication circuitry <NUM> of IMD <NUM>.

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

Memory <NUM> may store program instructions, which may include one or more program modules, which are executable by processing circuitry <NUM>. When executed by processing circuitry <NUM>, such program instructions may cause processing circuitry <NUM> and external device <NUM> to provide the functionality ascribed to them herein. The program instructions may be embodied in software, firmware and/or RAMware. Memory <NUM> may include any volatile, non-volatile, magnetic, optical, or electrical media, such as a random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), electrically-erasable programmable ROM (EEPROM), flash memory, or any other digital media.

In some examples, processing circuitry <NUM> of external device <NUM> may be configured to provide some or all of the functionality ascribed to processing circuitry <NUM> of IMD <NUM> herein. For example, processing circuitry <NUM> may receive physiological signals generated by one or more IMDs <NUM> and difference metrics/slopes <NUM> and/or may receive difference metrics/slopes <NUM> from one or more IMDs <NUM>. Processing circuitry <NUM> may determine baselines stored in baseline & threshold tables <NUM> and/or difference metrics/slopes <NUM> in the manner described herein with respect to processing circuitry <NUM> of IMD <NUM> for determining patient body stability based on accelerometer-generated data.

<FIG> is a functional block diagram illustrating an example system that includes an access point <NUM>, a network <NUM>, external computing devices, such as a server <NUM>, and one or more other computing devices 80A-80N, which may be coupled to IMD <NUM>, and external device <NUM> via network <NUM>. In this example, IMD <NUM> may use communication module <NUM> to communicate with external device <NUM> via a first wireless connection, and to communicate with an access point <NUM> via a second wireless connection. In the example of <FIG>, access point <NUM>, external device <NUM>, server <NUM>, and computing devices 80A-80N are interconnected and may communicate with each other through network <NUM>.

Access point <NUM> may comprise a device that connects to network <NUM> via any of a variety of connections, such as telephone dial-up, digital subscriber line (DSL), or cable modem connections. In other examples, access point <NUM> may be coupled to network <NUM> through different forms of connections, including wired or wireless connections. In some examples, access point <NUM> may be a user device, such as a tablet or smartphone, that may be co-located with the patient. As discussed above, IMD <NUM> may be configured to transmit data, such as current values and falling risk, to external device <NUM>. In addition, access point <NUM> may interrogate IMD <NUM>, such as periodically or in response to a command from the patient or network <NUM>, in order to retrieve values determined by processing circuitry <NUM> of IMD <NUM>, or other operational or patient data from IMD <NUM>. Access point <NUM> may then communicate the retrieved data to server <NUM> via network <NUM>.

In some cases, server <NUM> may be configured to provide a secure storage site for data that has been collected from IMD <NUM>, and/or external device <NUM>. In some cases, server <NUM> may assemble data in web pages or other documents for viewing by trained professionals, such as clinicians, via computing devices 80A-80N. One or more aspects of the illustrated system of <FIG> may be implemented with general network technology and functionality, which may be similar to that provided by the Medtronic CareLink® Network developed by Medtronic plc, of Dublin, Ireland.

In some examples, one or more of computing devices 80A-80N (e.g., device 80A) may be a tablet or other smart device located with a clinician, by which the clinician may program, receive alerts from, and/or interrogate IMD <NUM>. For example, the clinician may access patient <NUM>'s PTT measurements or difference metrics or slopes through device 80A, such as when patient <NUM> is in in between clinician visits, to check on a falling risk of patient <NUM> as desired. In some examples, the clinician may enter instructions for a medical intervention for patient <NUM> into an app in device 80A, such as based on falling risk of patient <NUM> determined by IMD <NUM>, or based on other patient data known to the clinician. Device 80A then may transmit the instructions for medical intervention to another of computing devices 80A-80N (e.g., device 80B) located with patient <NUM> or a caregiver of patient <NUM>. For example, such instructions for medical intervention may include an instruction to change a drug dosage, timing, or selection, to schedule a visit with the clinician, or to seek medical attention. In further examples, device 80B may generate an alert to patient <NUM> based on a falling risk of patient <NUM> determined by IMD <NUM>, which may enable patient <NUM> proactively to seek medical attention prior to receiving instructions for a medical intervention. In this manner, patient <NUM> may be empowered to take action, as needed, to address his or her falling risk, which may help improve clinical outcomes for patient <NUM>.

<FIG> is a flow diagram illustrating an example technique for determining a likelihood of a patient to fall. As described herein, the techniques illustrated in <FIG> may be employed using one or more components of system <NUM>, which have been described above with respect to <FIG>. Although described as being performed by IMD <NUM>, the techniques of <FIG> may be performed, in whole or in part, by processing circuitry and memory of other devices of a medical device system, as described herein. For example, although processing circuitry <NUM> of IMD is described as carrying out most of the example techniques illustrated in <FIG> for the sake of clarity, in other examples, one or more devices (e.g., external device <NUM> or other external device or server) or a clinician may carry out one or more steps attributed below to processing circuitry <NUM> of IMD <NUM>.

The example of <FIG> may be an example technique for determining, by processing circuitry <NUM> of IMD <NUM>, body stability or a falling risk of patient <NUM> based on a comparison of difference metrics or slopes based on PTT values of patient <NUM> to corresponding baseline values stored in tables <NUM> of memory <NUM>. As discussed above, IMD <NUM> may determine baseline PTT values for patient <NUM>. In some examples, IMD <NUM> may determine the baseline values during a learning phase of IMD <NUM> following implantation of IMD <NUM> into patient <NUM>, as discussed above with respect to <FIG>. Such a learning phase may take place after implantation of IMD <NUM> at a time when the condition of patient <NUM> is stable.

Processing circuitry <NUM> may determine a first plurality of PTTs of patient <NUM> prior to a Sit-to-Stand transition of patient <NUM> (<NUM>), for example, through the use of sensors as described above. The first plurality of PTTs may be PTTs measured prior to a Sit-to-Stand transition and may be stored in rolling buffer <NUM>, for example. Processing circuitry <NUM> may determine, based on at least one accelerometer signal, whether a Sit-to-Stand transition occurs (<NUM>). For example, processing circuitry <NUM> may monitor accelerometer <NUM> signals to determine whether a Sit-to-Stand transition has occurred. Details on how to determine a Sit-to-Stand transition is occurring based on an accelerometer signal can be found in commonly-assigned <CIT>, now <CIT> and claiming the benefit of Provisional Application No. <CIT>.

If a Sit-to-Stand transition has not occurred (the "NO" path in <FIG>), processing circuitry may continue to determine the first plurality of PPTs (<NUM>). In some examples, if a Sit-to-Stand transition has occurred, processing circuitry <NUM> may determine whether patient <NUM> has been inactive for a predetermined period of time. Processing circuitry <NUM> may make this determination based on a signal from an activity sensor. In some examples, the activity sensor is an accelerometer within IMD <NUM> or whichever medical device is performing the techniques of this disclosure. In some examples, processing circuitry <NUM> determines a number of activity counts based on one or more accelerometer signals exceeding one or more thresholds and uses the number of activity counts to determine if the patient has been inactive for the predetermined period of time. The activity counts used to determine if the patient has been inactive for the predetermined period of time may be a total, mean, or median number of counts during the period. In some examples, IMD <NUM> may determine if patient <NUM> has been inactive by determining patient <NUM> has not taken a step by monitoring the accelerometer signal for an indication that a step has been taken. Details on how to determine when a step is taken based on an accelerometer signal can be found in commonly-assigned <CIT>, now published as US Patent Application Publication No. <CIT> and claiming the benefit of Provisional Application No. <CIT>.

In some examples, if IMD <NUM> does not determine that patient <NUM> has been inactive for at least a predetermined period of time prior to the Sit-to-Stand transition (e.g., patient <NUM> has been active), in some examples, IMD <NUM> may ignore the Sit-to-Stand transition and continue to determine the first plurality of PPTs (<NUM>). The predetermined period of time may be programmable by external device <NUM> for example, or may be fixed. In some examples, the predetermined period of time may be several minutes, such as six minutes. IMD <NUM> may ignore the Sit-to-Stand transition shortly after a period in which patient <NUM> is active because recent activity may decrease the likelihood of patient <NUM>'s body stability being worse than normal or the measurement of the PTTs prior to and after the Sit-to-Stand transition may not be as comparable with other measurements due to the measurements after recent activity may not be consistent with measurements during less active times. By ignoring the Sit-to-Stand transition shortly after a period in which patient <NUM> is active, IMD <NUM> may save battery power and may preserve a data set of PTTs prior to and after Sit-to-Stand transitions that is more indicative of a measure of body stability issues. Alternatively, ICM 10B may not determine if patient <NUM> has been inactive for a predetermined period of time prior to the Sit-to-Stand transition.

In some examples, if a Sit-to-Stand transition has occurred (the "YES" path in <FIG>), processing circuitry <NUM> may determine, based on the Sit-to-Stand transition occurring, a second plurality of PTTs after the Sit-to-Stand transition of patient <NUM> (<NUM>). For example, the processing circuitry <NUM> may measure the second plurality of PTTs after the Sit-to-Stand transition and may store the second plurality of PTTs in rolling buffer <NUM>.

Processing circuitry <NUM> may determine a likelihood the patient will fall based on the first plurality of PTTs and the second plurality of PTTs (<NUM>). For example, processing circuitry <NUM> may determine the likelihood patient <NUM> will fall at least in part by calculating a first metric based on the first plurality of PPTs (PPTs prior to a Sit-to-Stand transition). The first metric may include, for example, a median, mean or mode of PTTs prior to a Sit-to-Stand transition. Processing circuitry <NUM> of IMD <NUM> may further determine the likelihood patient <NUM> will fall at least in part by calculating second metrics based on the second plurality of PTTs (e.g., PPTs after each Sit-to-Stand transition). For example, the second metrics may include at least one of a slope of the second plurality of PTTs, a minimum pulse transit time of the second plurality of PTTs, a maximum PTT of the second plurality of PTTs, or a median, mean, or mode of the second plurality of PTTs. The second metrics may therefore include a slope of the second plurality of PTTs, a minimum PTT after the Sit-to-Stand transition (e.g., the fastest PTT), a maximum PTT after the Sit-to-Stand transition (e.g., the slowest PTT), and/or a median, mean or mode of PTTs after the Sit-to-Stand transition.

In some examples, processing circuitry <NUM> may determine the likelihood the patient will fall at least in part by calculating difference metrics. The difference metrics may include at least one of a difference between the first metric and one or more of the second metrics. For example, the difference metrics may include at least one of a difference between the first metric and a minimum PTT of the second plurality of PTTs, a difference between the first metric and a maximum PTT of the second plurality of PTTs, or a difference between the first metric and a median, mean or mode of the second plurality of PTTs. For example, processing circuitry <NUM> of IMD <NUM> may calculate a minimum difference, a maximum difference and a median, mean or mode difference by subtracting each of the second metrics (e.g., from the second plurality of PTTs after the Sit-to-Stand transition) from the first metric (e.g., the mean, median or mode of the first PTTs prior to the Sit-to-Stand transition). For example, processing circuitry <NUM> of IMD <NUM> may calculate MinDiff by subtracting the fastest Sit-to-Stand transition PTT from the median of the pre-Sit-to Stand transition PTTs. Processing circuitry <NUM> may calculate MaxDiff by subtracting the slowest post Sit-to-Stand transition PTT from the median of the pre-Sit-to Stand transition PTTs. Processing circuitry may also calculate MedDiff by subtracting the median post Sit-to-Stand transition PTT from the median of the pre-Sit-to Stand transition PTTs.

Processing circuitry <NUM> of IMD <NUM> may, in addition to or in place of the difference metrics, calculate the slope of the PTT values after the Sit-to-Stand transition (<NUM>). IMD <NUM> may track the slope of the PTT values after the Sit-to-Stand transition and the difference metrics over time, for example, by storing calculated difference metrics and slope values in difference metrics/slopes <NUM> in memory <NUM>. In some examples, IMD <NUM> may use the difference metrics and/or the slope of the PTT values after the Sit-to-Stand transition to update the baseline(s) in table <NUM>. An acute or chronic change in these metrics may be an indication that patient <NUM> is becoming more likely to fall upon standing. Processing circuitry <NUM> may store the difference metrics and the slope of the PTT values after the Sit-to-Stand transition in memory <NUM>.

In some examples, processing circuitry <NUM> of IMD <NUM> may determine the likelihood patient <NUM> will fall at least in part by calculating tendency metrics <NUM> based on at least one of the difference metrics of the slope of the second plurality of PTTs (e.g., the PPTs after the Sit-to-Stand transition) over time. For example, processing circuitry <NUM> may calculate tendency metrics <NUM> based upon at least one of the difference metrics or the slope of the second plurality of PTTs over time. For example, processing circuitry <NUM> may periodically (e.g., daily) calculate tendency metrics <NUM>. For example, processing circuitry <NUM> of IMD <NUM> may calculate a central tendency (such as a median, mean or mode) of one or more of the difference metrics and/or slope of the PTTs after the Sit-to-Stand transitions and a variability (such as a standard deviation or interquartile range) of one or more of the difference metrics and/or slope of the PTTs after the Sit-to-Stand transitions. In other examples, processing circuitry <NUM> of IMD <NUM> may calculate tendency metrics <NUM> based upon a user request on external device <NUM>. For example, processing circuitry <NUM> of IMD <NUM> may calculate a central tendency (such as a median, mean or mode) of one or more of the difference metrics and/or the slope of the PTTs after the Sit-to-Stand transitions and a variability (such as a standard deviation or interquartile range) of one or more of the difference metrics and/or the slope of the PTTs after the Sit-to-Stand transitions based upon a user request on external device <NUM>. Processing circuitry <NUM> may store the tendency metrics <NUM> (e.g., central tendencies and/or the variabilities) in memory <NUM>. In some examples, the tendency metrics may be stored as baseline values. In some examples, processing circuitry <NUM> may determine the likelihood the patient will fall at least in part by comparing at least one of the difference metrics or the slope of the second plurality of PPTs to past tendency metrics.

IMD <NUM> may measure PTT on a periodic basis such as every minute. IMD <NUM> may then store the resulting measurements in rolling buffer <NUM>. Rolling buffer <NUM> may be configured to store a predetermined number of PTT values. For example, the rolling buffer may be configured to store <NUM> PTT values.

In the example where the rolling buffer is configured to store <NUM> PTT values, processing circuitry <NUM> of IMD <NUM> may calculate a median, mean or mode of first five PTT measurements in the rolling buffer (those associated with patient <NUM> sitting). In some examples, processing circuitry <NUM> may ignore PTT measurements that may occur during the Sit-to-Stand transition, for example the middle two values in the rolling buffer. Processing circuitry <NUM> may continue to calculate PTT an additional five times after standing for example and calculate slope of the PPT values post-standing, the minimum PTT value post-standing, the maximum PTT value post-standing, and the median, mean or mode PTT value post-standing. Processing circuitry <NUM> of IMD <NUM> may then calculate the difference metrics, such as the difference between the post-standing metrics and the mean, median or mode of the pre-standing (e.g., sitting) PPT. Processing circuitry <NUM> of IMD <NUM> may compare the difference metrics and/or the slope of the PTT values post-standing with baseline metrics saved in tables <NUM> to determine if the body of patient <NUM> may be less stable than at a time when the baseline was determined. IMD <NUM> may send an alert to external device <NUM>, for example, if it determines that there is an acute or chronic change in the difference metrics and/or the slope of the PTT values post-standing. For example, communication circuitry <NUM> may be configured to transmit an alert to an external device, such as external device <NUM>, upon determining the likelihood the patient will fall has increased.

For example, if the difference metrics or the slope of the PTT values post-standing change by <NUM>% within a relatively shorter time span, two days for instance, that may be indicative of an acute change in patient <NUM> body stability and a higher likelihood for a fall. If the difference metrics or the slope of the PTT values post-standing change over a relatively longer period of time, two weeks for instance, that may be indicative of a chronic change in patient <NUM> body stability and a higher likelihood of a fall.

In some examples, processing circuitry <NUM> also may determine whether a difference between one or more of the difference metrics and/or slope of the PTTs after the Sit-to-Stand transition and the corresponding baseline values satisfies a threshold value. In some examples, a threshold change value for a given parameter may be an absolute value of a percentage of the baseline value. For example, if a baseline value of a difference metric is = X, then a threshold value of the difference metric may be X ± <NUM>. IMD <NUM> may repeat steps <NUM>-<NUM> during each Sit-to-Stand transition.

<FIG> is a flow diagram illustrating an example technique for external device <NUM> to determine instructions or treatment for a medical intervention based on a falling risk of patient <NUM> received from IMD <NUM>, and transmit the instructions or treatment to a user interface. The method illustrated in <FIG> may be used with any of the methods for determining a falling risk by IMD <NUM> described herein, such as the method illustrated in <FIG>. In the illustrated example, external device <NUM> is configured to receive a falling risk of patient <NUM> from IMD <NUM>, which may be transmitted to a processing circuitry of external device <NUM> via communication circuitry <NUM> and antenna <NUM> of IMD <NUM> (<NUM>).

In some examples, upon receiving the falling risk of patient <NUM> from IMD <NUM> and prior to determining instructions or treatment for a medical intervention for patient <NUM>, external device <NUM> may transmit one or more queries to a user device. For example, external device <NUM> may ask patient <NUM> or a caregiver to answer questions about recent or current activities or symptoms of patient <NUM>, such as whether patient <NUM> recently has exercised, taken medications, or experienced symptoms. In addition, external device <NUM> may interrogate IMD <NUM> for difference metrics and/or the slope of PTTs after Sit-to-Stand transition(s) of patient <NUM>, if IMD <NUM> did not already transmit the difference metrics to external device <NUM>. Based on the falling risk of patient <NUM>, and optionally based on answers to queries and/or the current values of patient <NUM>, external device <NUM> then may determine instructions or treatment for a medical intervention for patient <NUM> (<NUM>).

In some examples, external device <NUM> may determine the instructions or treatment for medical intervention independent of clinician input, such as by selecting among treatment options stored in a memory of external device <NUM> or a centralized database that are associated with the falling risk of patient <NUM>. In other examples, a clinician may determine the instructions or treatment for medical intervention on substantially the same basis, and input the instructions to external device <NUM>. External device <NUM> then may transmit the instructions or treatment to an interface of the user device with patient <NUM> (<NUM>). In some examples, external device <NUM> may control IMD <NUM>, for example, to deliver a treatment such as a stimulation to the heart or a nerve. In other examples, external device may control a drug pump to deliver a drug to patient <NUM>. In some examples, external device <NUM> may transmit follow-up queries to patient <NUM> or a caregiver via the user device after transmitting the instructions. Such queries may include questions pertaining to patient <NUM>'s understanding of the transmitted instructions, whether patient <NUM> has complied with the instructed medical intervention, whether patient <NUM> feels their condition has improved and/or whether patient <NUM> is experiencing symptoms. External device <NUM> may store patient <NUM>'s responses in a memory of external device <NUM>, or in a centralized database. A clinician may review the responses, and remotely follow-up with patient <NUM> as needed following any changes to patient <NUM>'s treatment. In this manner, the techniques and systems described herein advantageously may enable patient <NUM> to receive individualized, frequently updated treatment at less expense than a comparable number of clinician visits would incur. In addition, the techniques and systems may help reduce falling events for patient <NUM>.

During the medical intervention, processing circuitry <NUM> of IMD <NUM> may continue to measure body stability or falling risk as discussed with respect to <FIG>. In this manner, feedback may be provided to a clinician or patient <NUM> as to the efficacy of the medical intervention.

Although processing circuitry <NUM> of IMD <NUM> and processing circuitry of external device <NUM> is described above as being configured to perform one or more of the steps of the techniques illustrated in <FIG>, any steps of the techniques described herein may be performed by processing circuitry of the other of IMD <NUM> or external device <NUM>, or by one or more other devices. For example, processing circuitry of external device <NUM>, or of any other suitable implantable or external device or server, may be configured to perform one or more of the steps described as being performed by processing circuitry <NUM> of IMD <NUM>. In other examples, processing circuitry <NUM> of IMD <NUM>, or of any other suitable implantable or external device or server, may be configured to perform one or more of the steps described as being performed by processing circuitry of external device <NUM>. Such other implantable or external devices may include, for example, an implantable pacemaker or ICD, an external monitoring device, or any other suitable device. In addition, although the optical sensors and electrodes are described herein as being positioned on a housing of IMD <NUM>, in other examples, such optical sensors and/or electrodes may be positioned on a housing of another device implanted in or external to patient <NUM>, such as a transvenous, subcutaneous, or extravascular pacemaker or ICD, or coupled to such a device by one or more leads.

<FIG> is a conceptual diagram <NUM> illustrating a sagittal axis <NUM>, a vertical axis <NUM> and transverse axis <NUM> in a three-dimensional coordinate system. As can be seen, sagittal axis <NUM> runs in the anterior-posterior direction, vertical axis <NUM> runs vertically and transverse axis runs left-right.

<FIG> is a plot <NUM> illustrating a sagittal axis signal <NUM>, a vertical axis signal <NUM>, and a transverse axis signal <NUM> produced by an accelerometer (see e.g., <FIG>, element <NUM>) during a series of sit-stand and stand-sit movements labeled A1-A2, B1-B2 and C1-C2, respectively. The sagittal axis signal <NUM> corresponds to the trace or trend that exhibits the largest amplitude variations primarily on the (+) side of the y-axis (arbitrary units) across each one of A1-A2, B1-B2 and C1-C2. The vertical axis signal <NUM> corresponds to the trace or trend that exhibits moderate amplitude variations on the (+) side and the (-) side of the y-axis across each one of A1-A2, B1-B2 and C1-C2. The transverse axis signal <NUM> corresponds to the trace or trend that exhibits amplitude variations primarily on the (-) side of the y-axis across each one of A1-A2, B1-B2 and C1-C2, exhibits a number of zero-crossings that is less than a number of zero-crossings of the vertical axis signal <NUM>.

The range of voltage variation provided within sagittal axis signal <NUM>, vertical axis signal <NUM>, and transverse axis signal <NUM> is not limited to any particular range of voltage variation, and in some examples is the voltage variation of sagittal axis signal <NUM>, vertical axis signal <NUM>, and transverse axis signal <NUM> as provided by the accelerometer configured to generated and provide the single axis accelerometer output signal processed to detect steps. In various examples, instead of sagittal axis signal <NUM>, a vertical axis signal <NUM>, and transverse axis signal <NUM> showing variations in voltage relative to the vertical axis, the variations are scaled to represent variations in gravitational force, measured in units of gravity - e.g., gravity = <NUM>/s2, and the variations in sagittal axis signal <NUM>, vertical axis signal <NUM>, and transverse axis signal <NUM> represent variations, measured in units, in the gravitational forces exerted in the respective axis.

Various aspects of the techniques may be implemented within one or more processors, including one or more microprocessors, DSPs, ASICs, FPGAs, or any other equivalent integrated or discrete logic circuitry, as well as any combinations of such components, embodied in programmers, such as physician or patient programmers, electrical stimulators, or other devices. The term "processor" or "processing circuitry" may generally refer to any of the foregoing logic circuitry, alone or in combination with other logic circuitry or any other equivalent circuitry.

In one or more examples, the functions described in this disclosure may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored on, as one or more instructions or code, a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include computer-readable storage media forming a tangible, non-transitory medium. 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 circuitry. Accordingly, the terms "processor" or "processing circuitry" as used herein may refer to one or more of any of the foregoing structures or any other structure suitable for implementation of the techniques described herein.

Claim 1:
A system (<NUM>) comprising:
a plurality of electrodes and/or one or more optical sensors for detecting at least one signal associated with pulse transit times;
accelerometer circuitry 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, by using the at least one signal associated with pulse transit times, a first plurality of pulse transit times of a patient (<NUM>) prior to a Sit-to-Stand transition of the patient (<NUM>);
determine, based on the at least one accelerometer signal, whether the Sit-to-Stand transition of the patient (<NUM>) occurs;
determine, based on the Sit-to-Stand transition occurring and by using the at least one signal associated with pulse transit times, a second plurality of pulse transit times after the Sit-to-Stand transition of the patient (<NUM>); and
determine a likelihood the patient (<NUM>) will fall based on the first plurality of pulse transit times and the second plurality of pulse transit times;
wherein the processing circuitry is configured to determine the likelihood the patient (<NUM>) will fall at least in part by calculating a first metric based upon the first plurality of pulse transit times; and
wherein the processing circuitry is further configured to determine the likelihood the patient (<NUM>) will fall at least in part by calculating second metrics based on the second plurality of pulse transit times.