Medtronic, Inc.

Covidien LP

University of Minnesota

Medtronics, Inc.

Eduardo N Warman

H Toby Markowitz

Phillip C Falkner

Shantanu Sarkar

Douglas A Hettrick

Steven Lawrence Waldhauser

Rahul Mehra

Vinod Sharma

Yong Kyun Cho

Karen J Kleckner

Lilian Kornet

Todd M Zielinski

Robert W Stadler

Mark T Stewart

Matthew David Bonner

David E Francischelli

Rodolphe Katra

Xiaohong Zhou

John E Burnes

Jeffrey M Gillberg

Mark L Brown

Harry A Puryear

Michael F Hess

Niranjan Chakravarthy

James R Skarda

Luc Roland Mongeon

Mark T Marshall

Bruce D Gunderson

Eric J Panken

Randal C Schulhauser

Tommy Dean Bennett

John Wainwright

Johnson E Goode

Teresa A Whitman

Brian Bruce Lee

Todd Jon Sheldon

David K L Peterson

Xusheng Zhang

Avram Scheiner

Paul T Rothstein

John Louis Sommer

James Kevin Carney

Can Cinbis

Scott Eric Jahns

Jonathan L Kuhn

Robert A Betzold

David A Anderson

Michael R S Hill

Paul J Degroot

Steven C Christian

Roderick E Briscoe

Koen J Michels

Ioana Fleming

Pooja Mehta

Chad Giese

Paul D Ziegler

Rogier Receveur

Mustafa Karamanoglu

Vincent Larik

David Scanlon

Olaf Eick

Michael Kryger

Sean Campbell-Massa

Shangqian Peter Zhang

James Steeves

Jeff Jannicke

Don Hefner

Tarek D Haddad

Andrew Radtke

Donald R Musgrove

Richard N M Cornelussen

Lindsay A Pedalty

David S Schwartzman

Lucy M Nichols

Karl Evan Nowak

Patrick W Kinzie

Jadin C Jackson

Kevin T Ousdigian

Tom Dean Bennett

Siddharth Dani

Zhendong Song

Paul A Sobotka

Steven N Lu

David E Ritscher

Brian Houston Craig

Nirav Vijay Sheth

Richard J O'Brien

Michael R Ujhelyi

David K Peterson

Christopher P Olig

Jeffrey David Wilkinson

David J Miller

David E Euler

Michael R Hill

James H Ericksen

Scott J Brabec

Joel R Lauer

David J S Kim

Gautham Rajagopal

Techniques are disclosed for using both feature delineation and machine learning to detect cardiac arrhythmia. A computing device receives cardiac electrogram data of a patient sensed by a medical device. The computing device obtains, via feature-based delineation of the cardiac electrogram data, a first classification of arrhythmia in the patient. The computing device applies a machine learning model to the received cardiac electrogram data to obtain a second classification of arrhythmia in the patient. As one example, the computing device uses the first and second classifications to determine whether an episode of arrhythmia has occurred in the patient. As another example, the computing device uses the second classification to verify the first classification of arrhythmia in the patient. The computing device outputs a report indicating that the episode of arrhythmia has occurred and one or more cardiac features that coincide with the episode of arrhythmia.

Arrhythmia detection with feature delineation and machine learning

Provided is a method, system and/or apparatus for determining prospective heart failure event risk. Acquired from a device memory are a heart failure patient's current and preceding risk assessment periods. Counting detected data observations in the current risk assessment period for a current risk assessment total amount and counting detected data observations in the preceding risk assessment period for a preceding risk assessment period total amount. Associating the current risk assessment and preceding risk assessment total amounts with a lookup table to acquire prospective risk of heart failure (HF) event for the preceding risk assessment period and the current risk assessment period. Employing weighted sums of the prospective risk of the HF event for the preceding risk assessment period and the current risk assessment period to calculate a weighted prospective risk of the HF event for a patient. Displaying on a graphical user interface the weighted prospective risk of the HF event for the patient.

Using biomarker information for heart failure risk computation

A method of operation of a medical device system for determining prospective heart failure hospitalization risk. The method includes measuring one or more data observations via one or more electrodes of an implanted medical device disposed in a patient's body. The data observations are stored into memory of the implantable medical device of a patient. The data observations are transmitted to an external device. The processor of the external device parses the data observations into one or more evaluation periods. Using the number of observations in one or more evaluation periods, a look up table, stored into memory of the external device, is accessed. The look up table associates prospective heart failure hospitalization risk with the data observations noted in the evaluation period. One or more embodiments involve a weighted prospective heart failure hospitalization risk for the set of evaluation periods. The prospective heart failure hospitalization is then displayed on the graphical user interface.

Determining prospective risk of heart failure hospitalization

A system for detecting strokes includes a sensor device configured to obtain physiological data from a patient, for example brain activity data. The sensor device can include electrodes configured to be disposed at the back of the patient's neck or base of the skull. The electrodes can detect electrical signals corresponding to brain activity in the P3, Pz, and/or P4 brain regions or other brain regions. A computing device communicatively coupled to the sensor device is configured to receive the physiological data and analyze it to indicate whether the patient has suffered a stroke.

Systems and methods for detecting strokes

Techniques that include applying machine learning models to episode data, including a cardiac electrogram, stored by a medical device are disclosed. In some examples, based on the application of one or more machine learning models to the episode data, processing circuitry derives, for each of a plurality of arrhythmia type classifications, class activation data indicating varying likelihoods of the classification over a period of time associated with the episode. The processing circuitry may display a graph of the varying likelihoods of the arrhythmia type classifications over the period of time. In some examples, processing circuitry may use arrhythmia type likelihoods and depolarization likelihoods to identify depolarizations, e.g., QRS complexes, during the episode.

Machine learning based depolarization identification and arrhythmia localization visualization

Methods and systems for seamless adjustment of treatment are disclosed. A determination is made as to whether to intervene with a patient's treatment. Implanted device memory data is acquired over a pre-specified time period. Risk status is determined from the device memory data. Another external device memory data is acquired over a pre-specified time period. A determination is made as to whether to adjust treatment of the patient in response to the risk status, the data acquired from the implanted device memory and the external device memory data.

Medical system for seamless therapy adjustment

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

This disclosure is directed to devices, systems, and techniques for determining an efficacy of a treatment program. For example, a medical device system includes a medical device including one or more sensors configured to generate a signal that indicates a parameter of a patient. Additionally, the medical device system includes processing circuitry configured to receive data indicative of a user selection of a reference time; determine a plurality of parameter values of the parameter based on a portion of the signal corresponding to a period of time including the reference time. Additionally, the processing circuitry is configured to identify, based on a first set of parameter values, a reference parameter value, calculate a parameter change value, and determine, based on the parameter change value, whether an improvement or a worsening of the patient has occurred responsive to a treatment administered beginning at the reference time.

Determining an efficacy of a treatment program

The disclosure describes techniques for delivering electrical stimulation to decrease the ventricular rate response during an atrial tachyarrhythmia, such as atrial fibrillation. AV nodal stimulation is employed during an atrial tachyarrhythmia episode with rapid ventricular conduction to distinguish ventricular tachyarrhythmia from supraventricular tachycardia and thereby prevent delivering inappropriate therapy to a patient.

AV nodal stimulation during atrial tachyarrhythmia to prevent inappropriate therapy delivery

Techniques for transmitting diagnostic information stored in an implantable medical device (IMD) based on patient hospitalization are described. For example, the IMD may transmit higher resolution diagnostic information to a clinician and/or an external device during a hospitalization period to aid the clinician in evaluating heart failure treatment and when discharge is proper. This higher resolution diagnostic information may include one or more patient metrics automatically generated and transmitted by the IMD at least once every two hours. During a post-hospitalization period, the IMD may transmit lower resolution diagnostic information to a clinician that indicates a risk level of re-hospitalization. The lower resolution diagnostic information may include the risk level and/or patient metrics once a day, for example. In this manner, the IMD transmitted diagnostic information may be tailored to the specific heart failure monitoring needed by the patient.

Heart failure monitoring

A method of detecting sleep apnea includes generating a cardiac signal indicating activity of a heart of a patient. The method further includes determining a short-term average heart rate and a long-term average heart rate. The method further includes determining a start and end of a heart rate cycle based on the short-term average heart rate and the long-term average heart rate. The method further includes determining physiological parameter values occurring during the heart rate cycle. The method further includes determining whether patient has or has not experienced a sleep apnea event based on whether one or more conditions are satisfied by one or more parameter values for one or more heart rate cycles and responsively generating an indication that patient has or has not experienced a sleep apnea event.

Detection and monitoring of sleep apnea conditions

Some aspects relate to systems, devices, and methods of assessing heart rate recovery. A heart rate of a patient may be measured during a plurality of heart rate recovery events. Each of the plurality of heart rate recovery events comprises a duration of time after an activity resulting in an elevated heart rate. Heart rate recovery information may be determined based on the measured heart rate during each of the plurality of heart rate recovery events and a cardiac status of the patient may be generated from the determined heart rate recovery information over the plurality of heart rate recovery events.

Heart rate recovery assessment

Growing community of inventors

Maple Grove, MN, United States of America

Eduardo N Warman