Physio-control, Inc.

West Affum Holdings Corp.

West Affum Holdings Dac

Medtronic Physio-control Manufacturing Corporation

Other

Physio-control Manufacturing Corporation

Medtronic Emergency Response Systems, Inc.

Medtronic, Inc.

Medtronic Physio-control Corp.

West Affum Holdings Designated Activity Company

Joseph Leo Sullivan

Richard C Nova

David Peter Finch

Laura Marie Gustavson

Gregory T Kavounas

Isabelle L Banville

Phillip Dewey Foshee, Jr

Jaeho Kim

Kenneth Frederick Cowan

Fred William Chapman

Robert G Walker

Krystyna Szul

Daniel Ralph Piha

Lawrence A Borschowa

Robert Reuben Buchanan

Ronald Eugene Stickney

Daniel James Finney

Daniel W Piraino

Zoie R Engman

Blaine Krusor

Pamela F Breske

Carmen Ann Chacon

Steven E Sjoquist

Gary DeBardi

Douglas Keith Medema

Erick Michael Roane

Patrick F Kelly

Garrett McCann Kotlarchik

Robert P Marx

Kevin Kelly Covey

Nikolai Korsun

William John Havel

Stephen W Radons

Larry R Nygaard

Daniel Peter Finch

Vladimir Pavlovich Nikolski

David R Hampton

Cynthia P Jayne

D Craig Edwards

Jonathan Paul Niegowski

Steven B Duke

Thomas J McGrath

David Thomas Brown

Brian J Bennett

Steven Postlewait

Matthew David Bonner

Kevin Patrick Kuehn

James W Taylor

Paul S Tamura

Scott Owen Schweizer

Stephen B Johnson

Brian D Webster

Benjamin A Flugstad

Glenn Morita

D Curtis Deno

James M Owen

Eric R Williams

Tarek Z Elabbady

Robert P Marx, Jr

David W Van Ess

Thomas Martin Daniel

Mark P Moore

Barry F Waltman

Kathleen E Briscoe

Sawyer Fuller

Melanie Anderson

Kevin R Brink

Zoie R Brent

Jennifer Talley

James M Pihl

Bret R Warrick

Hema A Ingle

Leo J Gilbert

The system and method provide for electrocardiogram analysis and optimization of patient-customized cardiopulmonary resuscitation and therapy delivery. An external medical device includes a housing and a processor within the housing. The processor can be configured to receive an input signal for a patient receiving chest compressions and to select at least one filter mechanism and to apply the filter mechanism to the signal to at least substantially remove chest compression artifacts from the signal. A real time dynamic analysis of a cardiac rhythm is applied to adjust and integrate CPR prompting of a medical device. Real-time cardiac rhythm quality is facilitated using a rhythm assessment meter.

System and method for electrocardiogram analysis and optimization of cardiopulmonary resuscitation and therapy delivery

Disclosed is a wearable medical device, such as a Wearable Cardioverter Defibrillator, which includes one or more sensors and a processor coupled to the one or more sensors. The processor is configured to record patient-specific information derived from signals output by the one or more sensors while the wearable medical device is being worn and to execute an algorithm to analyze the recorded information, the algorithm being based on data collected from multiple different persons. The processor is further configured to perform an artificial intelligence analysis of the recorded information, to update the algorithm with update information derived from the artificial intelligence analysis of the derived information, and to use the updated algorithm to analyze subsequent signals output by the one or more sensors while the wearable medical is being worn. The disclosed techniques result in a more patient-specific approach, which results in fewer false alarms.

Wearable Cardioverter Defibrillator (WCD) with artificial intelligence features

In embodiments, a wearable cardioverter defibrillator (WCD) system includes electrodes that render an ECG signal of the patient, and a processor that receives ECG data are derived from the rendered ECG signal. The processor may filter the received ECG data with a matched difference filter to detect QRS complexes, and compute a heart rate from the detected QRS complexes. The matched difference filter itself can have coefficient values associated with a baseline QRS complex, which improves detection.

Wearable cardioverter defibrillator (WCD) system detecting QRS complexes in ECG signal by matched difference filter

A medical wearable matching system is provided that pre-assesses a fit of a wearable article of medical device for a patient. The matching system may facilitate fitting and selection of a class of wearable articles for a patient. The matching system may also evaluate potential effects of the fit on operations of the medical device. Patient specific data, such as measurements, are fed into a fit prediction artificial intelligence ('AI') model. The fitting AI model is trained with fitting data of previous patients to predict a class of wearable medical article that is likely, above a threshold amount, to provide a target fit for the patient. The patient specific information may also be fed into an adverse potential AI model. The adverse potential AI model is trained with patient experience data describing adverse operations of a wearable article worn by prior patients.

Selection of a wearable article for a medical device

A wearable cardioverter defibrillator (WCD) including a support structure configured to be worn by an ambulatory patient, an energy storage module configured to store an electrical charge, a discharge circuit coupled to the energy storage module, electrodes configured to render an electrocardiogram (ECG) signal of the patient while the patient is wearing the support structure, a user interface configured to output an alarm in response to a noise alarm signal, and a processor. The processor is configured to receive the ECG signal, determine whether noise is present on the ECG signal, determine from the ECG signal whether a shock criterion is met, and cause the user interface to generate the noise alarm signal when the noise is present on the ECG signal and the shock criterion is met and not generate the noise alarm signal when noise is present on the ECG signal and the shock criterion is not met.

Wearable cardioverter defibrillator having reduced noise prompts

In one embodiment, a method to diagnose a heart condition by a wearable cardioverter defibrillator is described. The method includes obtaining a signal from the at least one sensor and analyzing the signal from the at least one sensor into usable data. The method including calculating a measured heart rate and a consistency metric from the usable data. The method further including determining when the measured heart rate satisfies a heart rate threshold and determining when the consistency metric satisfies a consistency threshold. The method including diagnosing a slow PVT episode based at least in part on the heart rate threshold and consistency threshold being satisfied.

Detecting shockable polymorphic ventricular tachycardia

A Wearable Medical System (WMS) includes one or more pacing capabilities. The WMS may detect when the patient's heart rhythm starts to deteriorate, but not necessarily in a way that requires defibrillation. In particular, the WMS may detect bradycardia of one or more types, and then confirm the detection before pacing to treat the detected bradycardia.

Rhythm sensing during external pacing

A wearable medical device comprising monitoring circuitry to monitor one or more patient parameters of a patient and defibrillation circuitry to provide one or more defibrillation shocks to the patient responsive to a control signal from the monitoring circuitry. The defibrillation circuitry comprises a defibrillation capacitor to provide energy for the one or more defibrillation shocks. The wearable medical device also comprises a power source to provide power to the monitoring circuitry and the defibrillation circuitry. The power source comprises a low current power source (LCPS) to provide power to the monitoring circuitry, and a high current power source (HCPS) to provide power to the defibrillation circuitry.

Power in a wearable cardioverter defibrillator (WCD)

Embodiments of a wearable monitoring device system can include one or more dry ECG electrodes and a processor that can be configured with one or more algorithms for detecting atrial fibrillation (AF) from sensed ECG signals sensed by the one or more dry ECG electrodes, and optionally other signals. In some embodiments the algorithms include one or more AF detection algorithms and optionally a noise detection algorithm. In some embodiments the wearable monitoring device or a remote system that receives data from the wearable medical device may calculate and/or characterize AF burden from ECG signals sensed by the one or more dry ECG electrodes.

Atrial fibrillation detection

In one embodiment, a WCD is described. The WCD includes a support structure configured to be worn by a patient and a processor coupled to the support structure. The WCD also includes an energy storage module configured to store an electrical charge and in communication with the processor. The WCD also includes a discharge circuit coupled to the energy storage module, the discharge circuit in communication with the processor and configured to discharge the stored electrical charge through a body of the patient. The processor is configured to detect an event at the WCD, classify the detected event, and determine an alarm onset time of the detected event based at least in part on the event classification. The processor is further configured to issue the alarm after the alarm onset time.

WCD system alert issuance and resolution

A wearable cardioverter defibrillator (WCD) system includes a support structure that the patient may wear, and one or more sensors that may acquire patient physiological signals, such as ECG and others. A processor of the WCD system may determine diagnostics from the patient physiological signals. These diagnostics include a six-second ECG portion, heart rates as histograms, heart rates against QRS width, heart rate trends, clinical event counters, diagnostics relating to heart rate variability and about the atrial arrhythmia burden of the patient. In some embodiments, the WCD system includes a user interface with a screen that displays these diagnostics. In some embodiments, the WCD system exports these diagnostics for viewing by a different screen. When viewed, these diagnostics permit more detailed analysis of the state of the patient.

Wearable cardioverter defibrillator (WCD) causing patient's QRS width to be plotted against the heart rate

In one aspect, an example wearable cardioverter defibrillator (WCD) system implementing the disclosed techniques includes a support structure, the support structure configured to be worn by a subject. The WCD system also includes a consciousness detection module configured to determine whether the subject wearing the support structure is unconscious. The WCD system further includes an interactive bystander module configured to, responsive to a determination that the subject wearing the support structure is unconscious, generate a first voice prompt inquiring as to a presence of a bystander.

Wearable cardioverter defibrillator (WCD) with bystander voice interaction

In one embodiment, a method to monitor a patient's heart health is described. The method may include processing at least one electrocardiogram (ECG) signal and diagnosing an episode of nonsustained ventricular tachycardia (NSVT) based at least in part on the processing of the at least one ECG signal. In some embodiments, the NSVT episode may satisfy an NSVT time duration and a QRS criterion. The NSVT time duration may be between 5 seconds and 15 seconds. In some instances, the QRS criterion may be a temporary QRS template of two sequential incoming QRS complexes. In some embodiments, the method may determine a similarity between the temporary QRS template and at least two subsequent QRS complexes by calculating a feature correlation coefficient between the QRS template and the subsequent QRS complexes.

Detecting nonsustained ventricular tachycardia in a wearable cardioverter defibrillator

In one example, an apparatus of a wearable cardioverter defibrillator (WCD) system comprises a support structure wearable by a patient, a plurality of electrocardiogram (ECG) electrodes to obtain an ECG signal, a processor to receive and analyze the ECG signal of the patient, wherein the processor is configured to monitor four or more channels of the ECG signal, a high voltage subsystem coupled with defibrillation electrodes configured to be coupled with patient, wherein processor is configured to cause the high voltage subsystem to apply a therapeutic shock to the patient through the defibrillation electrodes in response to a shockable event detected by the processor from the ECG signal. The processor measures a peak-to-peak amplitude of QRS complexes of the ECG signal, and detects asystole in the patient when the peak-to-peak amplitude of one or more QRS complexes is less than an asystole threshold. Other examples and related methods are disclosed herein.

Asystole and complete heart block detection

In one embodiment, a method to determine reliable electrocardiogram (ECG) signal is described. The method includes receiving at least one ECG signal for a period of time from a patient. The method also includes analyzing the at least one ECG signal to determine a first heart rate using a first method and analyzing the at least one ECG signal to determine a second heart rate using a second method different from the first method. The method includes comparing the first and second heart rates to each other and classifying the at least one ECG signal as reliable when a reliability threshold is satisfied.

Identifying reliable vectors

Growing community of inventors

Kirkland, WA, United States of America

Joseph Leo Sullivan