Cardionet, Inc.

Braemar Manufacturing LLC

Mad Apparel, Inc.

Infobionic, Inc.

Ventrilink Corporation

Other

Biomedical Systems Corporation

Applied Nonlinear Sciences, LLC

Card Guard Technologies, Inc.

Lev Korzinov

Eric Baumann

David Churchville

Justin James

Manuel Eduardo Jaime

Christopher John Wiebe

Scott Denis

Eric N Prystowsky

Anna McNamara

Michael Kremliovsky

James E Ott

Ankit Gordhandas

Adnan Aslam

David Bondietti

Daniel Blatnik

James U Lemke

Nikolai F Rulkov

Habib Homayoun

Lev S Tsimring

Paul Henry Bryant

Henry D I Abarbanel

Mikhail M Sushchik

Dang Le

Dave Churchville

Zach Cybulski

A computer-implemented method for processing ECG data may include: receiving, over an electronic network, ECG data, wherein the ECG data represents a plurality of heartbeats; analyzing the ECG data, by at least one processor, to determine whether each of the plurality of heartbeats is a normal heartbeat or an abnormal heartbeat; associating, by the at least one processor, each of the abnormal heartbeats with either only one of a plurality of existing templates or a new template; receiving, from a user, input related to each new template, wherein the input includes either: a) a confirmation that the new template represents an abnormal heartbeat, or b) a reclassification of the new template as representing a normal heartbeat or a different abnormal heartbeat; and in response to the user input, updating, by the at least one processor, a label of each of the heartbeats associated with each confirmed new template and each of the heartbeats associated with each reclassified new template. The ECG data may be received from a portable monitor configured to be carried on a patient's body.

Systems and methods for classifying ECG data

Systems and techniques for managing biological signals. In one implementation, a method includes receiving a cardiac biological signal that includes information describing events, determining a merit of each event based on one or more of a severity of a cardiac condition associated with the event and a quality of the event, and handling a subset of the events that meet a merit criterion. The subset can be handled for medical purposes.

Biological signal management

An exercise feedback system determines muscle fatigue measurements using physiological data generated by a sensor-equipped athletic garment. The muscle fatigue measurement is determined by analyzing the frequency spread of the physiological data. The exercise feedback system may customize exercise programs, determine risks of injury, or generate biofeedback for presentation on graphical user interfaces using the muscle fatigue measurements. The exercise feedback system accesses pre-determined muscle fatigue measurement models that define criteria for the aforementioned features. For instance, if an athlete is becoming fatigued and exercising with improper form based on a muscle fatigue measurement, the exercise feedback system modifies the athlete's exercise program to help target and improve the athlete's weaknesses as well as to prevent injury.

Fatigue measurement in a sensor equipped garment

An exercise feedback system determines sensor data quality of an athletic garment based on bioimpedance data. The athletic garment includes sensors that can generate physiological data and bioimpedance data. An athlete wears the athletic garment while exercising. If the sensors have a stable contact with the skin of the athlete, the sensors generate high quality physiological data. However, if the sensors have unstable or no contact with the skin of the athlete, the sensors generate low quality physiological data. The exercise feedback system uses the magnitude and/or variance of the bioimpedance data to determine whether the physiological data is high or low quality. If the physiological data is high quality, the exercise feedback system may generate and provide feedback based on the physiological data for display to the athlete. The exercise feedback system may also use the bioimpedance data to identify defects in the garment during quality assurance tests.

Determining sensor data quality based on bioimpedance information

An electrical patch and associated system for acquisition of physiological data is described. The patch has a design that enables a variety of configurations depending upon the requirements physiological measurements to be made. Patches with single input channels to patches with multiple input channels, processing capabilities and radio communication can all use the same physical configuration. The design includes a battery management system to enable long term data acquisition and an optimization process that includes mirroring of algorithms on the patch and devices local to the user with algorithms running on a centrally located server. The server can then optimize data acquisition and analysis algorithms. The components of the system and methods of use are included.

Electrical patch for physiological measurements

Devices and methods are described that provide improved diagnosis from the processing of physiological data. The methods include use of multiple algorithms and intelligently combing the results of multiple algorithms to provide a single optimized diagnostic result. The algorithms are adaptive and may be customized for particular data sets or for particular patients. Examples are shown with applications to electrocardiogram data, but the methods taught are applicable to many types of physiological data.

Electrocardiogram device and methods

A method for communicating beat parameters to a user includes: providing an electrode module comprising a first and a second set of electrodes, associated with a first and a second sensor channel, respectively; receiving a first and a second dataset based on a first and a second set of bioelectrical signals detected from the first and the second sensor channel, respectively; receiving a supplemental dataset based on supplemental bioelectrical signals detected from a supplemental sensor module; generating a noise-mitigated power spectrum upon: generating a combined dataset based upon combining the first and second datasets, calculating 1) a heart power spectrum based on the combined data set, and 2) a supplemental power spectrum based on the supplemental dataset, and generating a noise-mitigated power spectrum based on processing the heart power spectrum with the supplemental power spectrum; and rendering information derived from a beat parameter analysis to the user.

Method and system for measuring beat parameters

Devices and methods are described that provide improved diagnosis from the processing of physiological data. The methods include use of transforms prior to submitting the data to a multiple level neural network. In one embodiment for ECG analysis, a template is used to subtract data that is not pertinent to the diagnosis and then a Fourier transform is applied to the time series data. Examples are shown with applications to electrocardiogram data, but the methods taught are applicable to many types of physiological data.

A system and method for presenting information relating to heart data can involve operations including identifying arrhythmia events in physiological data obtained for a living being, receiving human assessments of at least a portion of the arrhythmia events, determining a measure of correlation between the human assessments and the identified events, and selectively presenting information regarding the identified events based on the measure of correlation. The operations can also include identifying atrial fibrillation events in physiological data obtained for a living being, obtaining heart rate data for the living being, and presenting information regarding the heart rate data and duration of the atrial fibrillation events together with a common time scale to pictographically represent heart rate trend with atrial fibrillation burden during a defined time period.

System and method for processing and presenting arrhythmia information to facilitate heart arrhythmia identification and treatment

A computer-implemented method for processing ECG data may include: receiving ECG data representing a plurality of heartbeats; analyzing the ECG data to determine whether each of the plurality of heartbeats is a normal heartbeat or an abnormal heartbeat; associating each of the abnormal heartbeats with an existing template or a new template; receiving input related to each new template, wherein the input includes either: a) a confirmation that the new template represents an abnormal heartbeat, or b) a reclassification of the new template as representing a normal heartbeat or a different abnormal heartbeat; and in response to the user input, updating a label of each of the heartbeats associated with each confirmed new template and each of the heartbeats associated with each reclassified new template.

Systems and methods for processing ECG data

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San Diego, CA, United States of America

Lev Korzinov