Medtronic, Inc.

Mirko De Melis

John E Burnes

Stacy D Beske Radford

Berthold Stegemann

Richard N Cornelussen

Richard Sutton

Eduardo N Warman

Bruce D Gunderson

Giorgio Corbucci

Alfonso Aranda Hernandez

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.

Determining a risk or occurrence of health event responsive to determination of patient parameters

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.

Sensing for heart failure management

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.

Body stability measurement using pulse transit time

Determining a fall risk responsive to detecting body position movements

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.

Evaluation of cardiac dyssynchrony based on chest wall motion and electrical cardiac activity

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Maastricht, Netherlands

Mirko De Melis