Abstract: In some examples, determining a heart failure status using a medical device comprising one or more sensors includes determining a first value of a heart beat variability metric of a patient while an activity state of a patient satisfies an inactivity criterion based on a signal received from the one or more sensors, and determining, within a predetermined period of time after further determining that the activity state of the patient no longer satisfies the inactivity criterion, a second value of the heart beat variability metric while the activity state of the patient no longer satisfies the inactivity criterion based on the signal. A difference between the first value of the heart beat variability metric and the second value of the heart beat variability metric may be determined and the heart failure status of the patient may be determined based on the difference.

Abstract: A vestibular implant system including a memory; an implantable sensor; a vestibular implant; and processing circuitry coupled to the memory. The processing circuitry is configured to: detect, via the implantable sensor, an electrical signal of a patient that is generated in response to an electrical stimulation signal transmitted by the vestibular implant to one or more regions of vestibular organ of a patient; determine, based on the detected electrical signal, that the vestibular implant is transmitting the electrical stimulation signal at a first rate of change of an amplitude of the electrical stimulation signal; and based on a determination that the first rate of change satisfies a threshold condition, adjust one or more parameters of the electrical stimulation signal to cause the vestibular implant to transmit the electrical stimulation signal to the nerve at a second rate of change of the amplitude of the electrical stimulation signal.

Abstract: A system comprising a memory, a gyroscope configured to detect movements of a head of a patient, an implantable sensor configured to detect electrical signals in tissue of the patient, a vestibular implant, and processing circuitry coupled to the memory. The processing circuitry configured: to determine, based on the detected movements of the head, an angular shift frequency of the head and an evoked electrical signal, and select, based on determined characteristics comprising the angular shift frequency and the evoked electrical signal, an operating mode for the vestibular implant from a plurality of operating modes stored in the memory, wherein the vestibular implant is configured to transmit an electrical stimulation signal to one or more regions of the vestibular organ based on the selected operating mode.

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

Abstract: In some examples, determining a heart failure status using a medical device comprising one or more sensors includes determining a first value of a heart beat variability metric of a patient while an activity state of a patient satisfies an inactivity criterion based on a signal received from the one or more sensors, and determining, within a predetermined period of time after further determining that the activity state of the patient no longer satisfies the inactivity criterion, a second value of the heart beat variability metric while the activity state of the patient no longer satisfies the inactivity criterion based on the signal. A difference between the first value of the heart beat variability metric and the second value of the heart beat variability metric may be determined and the heart failure status of the patient may be determined based on the difference.

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

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

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

Abstract: A method for artifact suppression in a sensed signal includes receiving the sensed signal sensed in a brain of a patient, wherein the sensed signal includes a neural signal and artifacts from a cardiac signal, decomposing the sensed signal into a plurality of components of the sensed signal, determining a first group of components, from the plurality of components, that are correlated with one another, determining an estimate of the cardiac signal based on the first group of components, wherein the estimate of the cardiac signal includes the cardiac signal and components of the neural signal, and generating a denoised neural signal based on the estimate of the cardiac signal and a second group of components of the plurality of components of the sensed signal, wherein the cardiac signal is suppressed in the denoised neural signal, and wherein the second group of components excludes the first group of components.

Abstract: Systems and methods are directed to influencing organ function by stimulating the dorsal root ganglion. Systems include at least one electrode to deliver electrical stimulation to the dorsal root ganglion to activate afferent nerves innervating at least one organ, and computing apparatus comprising one or more processors operably coupled to the at least one electrode to control the electrical stimulation.

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

Abstract: An example medical device system and method includes 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 is configured to determine a first plurality of pulse transit times (PTTs), determine, based on the at least one accelerometer signal, a Sit-to-Stand transition, determine, based on the Sit-to-Stand transition occurring, a second plurality of PTTs after the Sit-to-Stand transition, and determine a likelihood that a person, such as a patient, may fall based on the first plurality of PTTs and the second plurality of PTTs.

Abstract: In some examples, determining a heart failure status using a medical device comprising one or more sensors includes determining a first value of a heart beat variability metric of a patient while an activity state of a patient satisfies an inactivity criterion based on a signal received from the one or more sensors, and determining, within a predetermined period of time after further determining that the activity state of the patient no longer satisfies the inactivity criterion, a second value of the heart beat variability metric while the activity state of the patient no longer satisfies the inactivity criterion based on the signal. A difference between the first value of the heart beat variability metric and the second value of the heart beat variability metric may be determined and the heart failure status of the patient may be determined based on the difference.

Abstract: The invention relates to a device for detecting atrial fibrillation of a subject, comprising a member comprising —mechanical connection means to wearably connect the member to a subject; —the member further comprising an optical sensor arranged to measure a signal representing a blood perfusion parameter of the subject; the member comprising the optical sensor and mechanical connection means at such relative positions that when the mechanical connection means connect the member to the subject, the optica! sensor operatively faces the subject. The device is arranged to detect atrial fibrillation of the subject based on the signal measured by the optical sensor.

Abstract: Cardiac dyssynchrony of a patient may be evaluated based on electrical activity of a heart of the patient and corresponding chest wall motion of the patient sensed via an external accelerometer. In one example, an acceleration signal indicative of the chest wall motion is generated by an external accelerometer positioned on the chest wall of the patient. A processor of a diagnostic device integrates the acceleration signal to generate a velocity signal and temporally correlates the velocity signal and an electrical cardiac signal. The processor determines a time delay between a deflection of the electrical cardiac signal indicating ventricular electrical activation and a subsequent greatest peak of the velocity signal. The time delay may indicate a degree of electromechanical delay of the left ventricle. In some examples, the processor generates an output indicative of a cardiac dyssynchrony status based on the time delay.

Abstract: Cardiac dyssynchrony of a patient may be evaluated based on electrical activity of a heart of the patient and corresponding chest wall motion of the patient sensed via an external accelerometer. In one example, an acceleration signal indicative of the chest wall motion is generated by an external accelerometer positioned on the chest wall of the patient. A processor of a diagnostic device integrates the acceleration signal to generate a velocity signal and temporally correlates the velocity signal and an electrical cardiac signal. The processor determines a time delay between a deflection of the electrical cardiac signal indicating ventricular electrical activation and a subsequent greatest peak of the velocity signal. The time delay may indicate a degree of electromechanical delay of the left ventricle. In some examples, the processor generates an output indicative of a cardiac dyssynchrony status based on the time delay.