General Electric Company

Ge Precision Healthcare LLC

Gordon Ian Rowlandson

Joel Quizhen Xue

Robert Edward Henderson

Anthony Ricke

Long Yu

Donald Brodnick

Joel Qiuzhen Xue

Chepur Sudhakar Rao

David Thomas Peterson

Chepur Sudakar Rao

Kjell Kristoffersen

Brian Joseph Young

Derek Exner

Willi Kaiser

Irshath Ahamed

Michael A Juhl

Robert Farrell

Erin K Carroll

Tyler M Brown

Michael Slawnych

Alfred Lojewski

Tyler Mcnulty Brown

Methods and systems are provided for automatically diagnosing a patient based on a reduced lead electrocardiogram (ECG), using one or more deep neural networks. In one embodiment, a method for automatically diagnosing a patient using a reduced lead ECG comprises, acquiring reduced lead ECG data, wherein the reduced lead ECG data comprises less than twelve lead signals, determining a type of each of the less than twelve lead signals, selecting a deep neural network based on the type of each of the less than twelve lead signals, and mapping the less than twelve lead signals to a diagnosis using the deep neural network. In this way, reduced lead ECG data may be mapped to a diagnosis using an intelligently selected deep neural network, wherein the deep neural network was trained on reduced lead ECG data comprising a same set of ECG lead types as the acquired reduced lead ECG data.

Systems and methods for reduced lead electrocardiogram diagnosis using deep neural networks and rule-based systems

Display screen or portion thereof with graphical user interface

Methods and systems are provided for automatically diagnosing an electrocardiogram (ECG) using a hybrid system comprising a rule-based system and one or more deep neural networks. In one embodiment, by mapping ECG data to a plurality of features using a convolutional neural network, mapping the plurality of features to a preliminary diagnosis using a decision network, and determining a diagnosis based on the ECG data and the preliminary diagnosis using the rule-based system, a more accurate diagnosis may be determined. In another example, by incorporating both a rule-based system and one or more deep neural networks into the hybrid system, the hybrid system may be more easily adapted for use in various contexts/communities, as the one or more deep learning networks may be trained using context/community specific ECG data.

Systems and methods for electrocardiogram diagnosis using deep neural networks and rule-based systems

A system for processing ECG to detect atrial fibrillation includes three software modules. A beat module is executable on a processor to receive a time series of ECG data, identify heart beats, and determine a beat AFIB value based on a timing of each identified heart beat. The beat AFIB value represents a presence or absence of AFIB based on variability in the timing of each identified heart beat. A segment module is executable to receive the time series of ECG data, divide the time series of ECG data into two or more time segments, and determine a segment AFIB value for each time segment. The segment AFIB value indicates a presence or absence of AFIB in the time segment based on whether any of a set of rhythms are identified. The AFIB detection module is executable to determine an AFIB identification value for each time segment based on the beat AFIB value during that time segment and the segment AFIB value for that time segment.

System and method for detecting atrial fibrillation

A system for transferring medical data includes a computer network upon which an electronic medical record of a patient is stored. A mobile computer presents a bar code on a graphical display. A medical device is communicatively connected to a bar code scanner and receives patient information and mobile computer information from the bar code scanner. The medical device further performs a medical test to generate test result data. The test result data is transmitted to the mobile computer based upon the received mobile computer information. A method of medical data transfer includes receiving a medical data transfer request. A bar code is scanned to input the patient information and mobile computer information into the medical device. A medical test is performed with the medical device to produce test result data that is transmitted to the mobile computer based upon the mobile computer information.

System and method for medical data transfer

A system for generating a diagnosis is disclosed herein. The system includes a controller, an electrocardiograph connected to the controller, and an ultrasound device connected to the controller. The electrocardiograph is configured to generate a diagnostic electrocardiogram. The controller is configured to generate a diagnosis based on data from the electrocardiograph or the ultrasound device.

System and method for generating a patient diagnosis

A system and apparatus for obtaining physiological data from a patient. The system and apparatus comprising at least one electrode disposed to collect physiological data from the patient and an electrode connection device having a conductive array formed by a plurality of conductive regions and a plurality of nonconductive regions. The conductive regions are suitable to be connected to the at least one electrode. The electrode may be connected to a first conductive region of the electrode communication device and wherein upon movement by the patient or the conductive array, the electrode is connected to a second conductive region of the conductive array.

System and apparatus for collecting physiological signals from a plurality of electrodes

A method and apparatus for the collection of physiological data from a patient is disclosed herein. An electrode assembly comprises an external label identifying an anatomical location and an electrode identifying circuitry that produces a signal indicative of the anatomical location to which the electrode assembly is to be attached. The electrode assembly transmits both the collected physiological signal and the identification signal to a data monitor for collection and processing physiological data.

Method and apparatus for acquiring physiological data

A data acquisition module for use in monitoring a plurality of physiological signals is disclosed herein. The data acquisition module may include a first signal processing path for biopotential data, a second signal processing path for therapeutic event data, and a processing unit that receives and processes the data from the first and second signal processing paths. The data acquisition module may further compare identified likely therapeutic events in each of a plurality of psychological signals.

Module and device for discerning therapeutic signals from noise in physiological data

A method for generating a cardiac electrical instability assessment is disclosed herein. The method includes obtaining a short duration T-wave alternans (SDTWA) measurement, obtaining a long duration T-wave alternans (LDTWA) measurement, and obtaining a cardiac electrical instability assessment based on both the SDTWA measurement and the LDTWA measurement.

Method and system for detecting T-wave alternans

A method of diagnosing a malfunction of a pacing system includes the steps of receiving a biopotential signal, detecting a pacing system malfunction, detecting a cause of the malfunction, and displaying the detected malfunction and detected cause of the malfunction. A pacing system is also disclosed herein. The system includes an electrode array that receives a biopotential signal associated with the pacing system. A malfunction detector applies a malfunction logic to the biopotential signal to identify a pacing system malfunction and applies a morphology logic to the biopotential signal to identify a morphology of the biopotential signal. An output generator receives an indication of the identified pacing system malfunction and the identified cause of the malfunction and creates an output indicative of the identified pacing system malfunction and the identified cause.

System and method of detecting and diagnosing pacing system malfunctions

A data acquisition module for use in monitoring a plurality of physiological signals is disclosed herein. The data acquisition module may include a first signal processing path for biopotential data, a second signal processing path for therapeutic event data, and a processing unit that receives and processes the data from the first and second signal processing paths. The data acquisition module may further compare identified likely therapeutic events in each of a plurality of psychological signals. A method of identifying an electronic therapeutic event from a physiological signal is also disclosed herein the method may include steps of obtaining a plurality of psychological signals, removing any biopotential data from the physiological signals, using a first technique to identify likely therapeutic event data, comparing the identified likely therapeutic event data from each of the plurality of physiological signals and refining the identification of likely therapeutic events based on the comparison between each of the plurality of physiological signals.

Growing community of inventors

Milwaukee, WI, United States of America

Gordon Ian Rowlandson