Medtronic, Inc.

Umc Utrecht Holding B.v.

Universiteit Maastricht

Academisch Ziekenhuis Maastricht

Alfonso Aranda Hernandez

Berthold Stegemann

Marc A Vos

David J Sprenkeler

Richard N Cornelussen

Frits W Prinzen

Elien B Engels

Subham Ghosh

Xusheng Zhang

Jeffrey M Gillberg

Saul E Greenhut

Troy Edward Jackson

Lilian Kornet

Richard J O'Brien

Michael W Heinks

Timothy A Ebeling

Yuanzhen Liu

Raphael Schneider

Mirko De Melis

Jean E Hudson

Irving J Sanchez

Joshua J Blauer

James Vander Heyden

Richard Sutton

Kevin Vernooy

A medical device is configured to receive cardiac electrical signals and sense ventricular event signals from the cardiac electrical signals. The medical device may start a validation window in response to sensing a ventricular event signal and determine if the ventricular event signal is a valid event signal or an invalid event signal based on processing of a different cardiac electrical signal than the cardiac electrical signal from which the ventricular event signal was sensed.

Medical device and method for cardiac pacing and sensing

Methods and related systems and devices for cardiac therapy use pseudo-electric vectors (PEVs) for characterizing and representing the electrical forces generated by a patient's heart in a three-dimensional (3D) manner. PEVs may be used to predict whether a patient will respond to pacing therapy prior to implant, during implant, or in the follow-up after implant. Various cardiac therapy systems and devices, such as an electrocardiogram (ECG) belt or vest, which may include a plurality of external electrodes, may be used to obtain electrical activity information to generate the PEVs. One or more spatio-temporal PEVs may be determined using one or more sensors at one or more points in time. Spatial representation data may be determined based on the PEVs.

Propagation patterns method and related systems and devices

A method includes detecting whether one or more myocardial infarctions (MI) has occurred using vectorcardiographic (VCG) signals with gradient boosting, the VCG signals including VCG loops, and determining an MI location using the VCG signals and gradient boosting.

Detection and localization of myocardial infarction using vectorcardiography

An example device for detecting one or more parameters of a cardiac signal is disclosed herein. The device includes one or more electrodes and sensing circuitry configured to sense a cardiac signal via the one or more electrodes. The device further includes processing circuitry configured to determine a representative signal based on the cardiac signal, the representative signal having a single polarity, and determine an end of a T-wave of the cardiac signal based on an area under the representative signal.

Cardiac signal T-wave detection

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

A medical system including processing circuitry configured to receive a cardiac signal indicative of a cardiac characteristic of a patient from sensing circuitry and configured to receive a glucose signal indicative of a glucose level of the patient. The processing circuitry is configured to formulate a training data set including one or more training input vectors using the cardiac signal and one or more training output vectors using the glucose signal. The processing circuitry is configured to train a machine learning algorithm using the formulated training data set. The processing circuitry is configured to receive a current cardiac signal from the patient and determine a representative glucose level using the current cardiac signal and the trained machine learning algorithm.

Self-calibrating glucose monitor

Cardiac signal t-wave detection

In some examples, controlling delivery of CRT includes delivering ventricular pacing according to a sequence of different values of at least one of A-V delay or V-V delay, and acquiring one or more electrograms from respective vectors. For each of the different values of the at least one of A-V delay or V-V delay, at least one of a QRS amplitude or a QRS area may be determined based on the one or more electrograms, and a target change in QRS amplitude or QRS area between adjacent ones of the values of the at least one of A-V delay or V-V delay of the sequence may be identified. In response to the identification of the target change, the implantable medical device may deliver the ventricular pacing at a value of the at least one of A-V delay or V-V delay determined based on the identification to provide CRT.

Electrogram-based control of cardiac resynchronization therapy

A cardiac resynchronization pacemaker and a method of adjusting the pacemaker. The method includes deriving a vectorcardiogram from implanted electrodes (D-VCG), analyzing the D-VCG, deriving optimal CRT pacing parameters from the analysis of the D-VCG, and adjusting the CRT pacemaker according to the derived parameters. The pacemaker may include a processor configured to perform the method.

Method and apparatus for optimization of cardiac resynchronization therapy using vectorcardiograms derived from implanted electrodes

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Maastricht, Netherlands

Alfonso Aranda Hernandez