Alivecor, Inc.

Innovalarm Corporation

Ge Medical Systems Information Technologies Gmbh

Data Critical Corporation

Corazonix Corporation

Other

General Electric Company

University of Oklahoma

Innovalaem Corporation

David E Albert

Euan Thomson

Conner Daniel Cross Galloway

Bruce Richard Satchwell

Omar Dawood

Iman Abuzeid

Ravi Gopalakrishnan

Frank Losasso Petterson

Nupur Srivastava

Fei Wang

Alexander Vainius Valys

Lev Korzinov

James Joe Lewis

Landgrave T Smith

Jonathan Beck

Simon Prakash

Kim Norman Barnett

Kathryn Marie Albert

Arthur Okamoto

James Jenkins

Donald Brodnick

Melissa D McLean

William E Saltzstein

Brian Joseph Young

Vivek Paul Gundotra

James M Gray

G Ian Rowlandson

Paul S Schluter

David G Hernke

Robert T Wolfe

Carl J Rieger

Paul Lander

Joel Qiuzhen Xue

Colin J Cumming

Leslie D Hoy

Conner Daniel Galloway

Rik Slaven

Mac L Reiter

Nicholas Peter Hughes

Benjamin J Scherlag

Sunny Po

Patrick Dorsey

Joel Roark

Edward J Berbari

Aziz El Idrisi

Judy Wade

Vivek Gundotra

Stavros Stavrakis

Bruce Satchwell

Melissa Mclean

Paul Garabelli

Leslies D Hoy

A smart watch to detect a presence of an arrhythmia of a user is disclosed. The smart watch comprising: a processing device and a photoplethysmography ('PPG') sensor operatively coupled to the processing device, an ECG sensor comprising two or more ECG electrodes and operatively coupled to the processing device, a display operatively coupled to the processing device, and a memory, operatively coupled to the processing device. The memory may have instructions stored thereon that, when executed by the processing device, cause the processing device to: receive PPG data from the PPG sensor, receive ECG data from the ECG sensor, detect, based on the PPG data and the ECG data simultaneously, the presence of an arrhythmia, and provide one or more recommendations to the user based on the PPG data and the ECG data.

Methods and systems for arrhythmia tracking and scoring

Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator ('other-factors') to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.

Continuous monitoring of a user's health with a mobile device

Described herein are software platforms (or 'platforms'), systems and methods for providing effective healthcare to a patient while increasing the efficiency of a healthcare provider. A healthcare provider system may receive physiological data of a user and generate an interface to provide a visualization of the physiological data and identifying information of the user. The system may analyze the physiological data to generate a risk score for the user based at least in part on the physiological data and transmit to a device of the user, a notification comprising the risk score.

Devices, systems, and methods for physiology monitoring

A mobile electrocardiogram (ECG) device is described, comprising an electrode assembly comprising electrodes that sense heart-related signals when in contact with a body of a user, and produce electrical signals representing the sensed heart-related signals. The mobile ECG device further comprises a light-emitting device to facilitate an optimal placement of the electrode on the body of the user. The mobile ECG device further comprises a housing containing the electrode assembly, the converter assembly, the transmitter, and the light-emitting device. A first electrode of the electrode assembly forms a first side of the housing and comprises an opening through which the light-emitting device provides the UV light.

Ultraviolet cardiac monitoring and analysis

Apparatuses and methods for extracting, de-noising, and analyzing electrocardiogram signals. Any of the apparatuses described herein may be implemented as a (or as part of a) computerized system. For example, described herein are apparatuses and methods of using them or performing the methods, for extracting and/or de-noising ECG signals from a starting signal. Also described herein are apparatuses and methods for analyzing an ECG signal, for example, to generate one or more indicators or markers of cardiac fitness, including in particular indicators of atrial fibrillation. Described herein are apparatuses and method for determining if a patient is experiencing a cardiac event, such as an arrhythmia.

Electrocardiogram signal detection

A set of training electrocardiograms (ECGs) for each of a plurality of subjects is processed using a machine learning model to generate an output for each training ECG of each of the plurality of subjects. Training ECGs for each subject are labeled with an identity of the subject. A machine learning model is trained by comparing the output generated for each training ECG to a corresponding label of the training ECG to generate an identity model to identify ECGs of a first subject of the plurality of subjects. A first ECG is received from an ECG sensor and input to the identity model, which generates an output indicating whether the first ECG corresponds to the first subject. In response to the output indicating that the first ECG does not correspond to the first subject, a condition that the first subject has or may develop is determined based on the output.

Systems and methods of identity analysis of electrocardiograms

Described herein are devices, systems, and methods for sensing, generating, and time aligning one or more of the 12 leads of a standard ECG. A set of electrodes may be used to perform an electrocardiogram (ECG) of a user to generate a set of limb leads including a first lead I. A Wilson's Central Terminal (WCT) value may be determined using a subset of the set of limb leads. In response to an electrode of the set of electrodes contacting the user's chest, a precordial lead may be generated based on electric potentials sensed by the electrode while contacting the user's chest and the WCT value. As the precordial lead is generated, a corresponding second lead I is simultaneously generated. The corresponding second lead I is used to time align the set of limb leads and the precordial lead.

Mobile cardiac monitoring and analysis

Disclosed are systems for non-invasively determining a measurement of an analyte. The systems include an electrocardiogram sensor and a processing device operatively coupled to the electrocardiogram sensor. The processing device can execute instructions to receive electrocardiogram data from the electrocardiogram sensor and apply a machine learning model, wherein the machine learning model has been trained based on previous electrocardiogram data associated with a subject and source of an analyte measurement associated with the subject. The system may also determine an indication of a level of the analyte based on the electrocardiogram data.

Systems and methods of analyte measurement analysis

Disclosed systems and method receive electrocardiogram (EKG) data of a subject. The EKG data comprises a record of at least a full beat of the subject. The EKG data is input into a machine learning model to generate an output. The output can include a segmentation of the full beat or a measurement of a QT interval of the subject.

Systems and methods of QT interval analysis

Described herein are methods, systems, and software for monitoring blood pressure. In some embodiments, a using a mobile device is used. The blood pressure monitoring system utilizes heart rate measurements at two separate locations on the body to calculate a differential pulse arrival time which is used to estimate blood pressure. The heart rate measurements can be taken simultaneously, or they can be taken sequentially while simultaneously taking ECG measurement. If taken sequentially, the heart rate measurements are aligned with the ECG to determine the differential. Accurate, inexpensive, and discreet blood pressure monitoring is thus provided.

Blood pressure monitor

Multi-electrode device

Described herein are systems, devices, and methods for cardiac monitoring. In particular, the systems, devices, and methods described herein may be used to conveniently sense the presence of an intermittent arrhythmia in an individual. The systems, devices, and methods described herein may be further configured to sense an electrocardiogram.

Discordance monitoring

Disclosed herein are devices, systems, methods and platforms for continuously monitoring the health status of a user, for example the cardiac health status. The present disclosure describes systems, methods, devices, software, and platforms for continuously monitoring a user's low-fidelity health-indicator data (for example and without limitation PPG signals, heart rate or blood pressure) from a user-device in combination with corresponding (in time) data related to factors that may impact the health-indicator ('other-factors') to determine whether a user has normal health as judged by or compared to, for example and not by way of limitation, either (i) a group of individuals impacted by similar other-factors, or (ii) the user him/herself impacted by similar other-factors.

Machine learning health analysis with a mobile device

Described herein are software platforms (or 'platforms'), systems, devices, and methods for providing effective healthcare to a patient while increasing the efficiency of the healthcare provider. A platform provides effective healthcare to a patient through, but not limited to, monitoring of the physical parameters and therapeutic objectives of the patient. In addition, a platform is further configured to provide a communication link between a patient and a healthcare provider. The platform may be implemented by a system comprising one or more computing devices that interact with or run one or more applications of the platform.

Disclosed systems include an electrocardiogram sensor configured to sense electrocardiograms of a subject and a processing device operatively coupled to the electrocardiogram sensor. The processing device receives an electrocardiogram from the electrocardiogram sensor. The electrocardiogram is input into a machine learning model, the machine learning model to generate an output based on the received electrocardiogram. The processing device determines based on the electrocardiogram, that the output does not match an expected range of outputs for the target subject and generates an alert indicating a possible change in a status of the subject in response to the output not matching the expected range of outputs for the target subject.

Growing community of inventors

San Francisco, CA, United States of America

David E Albert