Abstract: 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.

Abstract: A system for comparative analysis of cardiac information includes an electrocardiogram (ECG) device including a plurality of electrodes and a monitor, an ultrasound device including a transducer probe configured to emit and receive sound waves; a user interface configured to provide information to a user and obtain information from a user; a memory storing instructions; and a processor configured to execute the instructions to: obtain ECG data and ECHO data from a patient over a same time period, the ECG data being obtained from the ECG device and the ECHO data being obtained from the ultrasound device; obtain an ECG object based on the ECG data, the ECG object being three-dimensional; obtain an ECHO object based on the echocardiogram data, the ECHO object being three-dimensional; and control the user interface to simultaneously display a 3D rendering of the ECG object and a 3D rendering of the ECHO object.

Abstract: A system for comparative analysis of cardiac information including an electrocardiogram (ECG), a user interface, a memory storing instructions; and a processor configured to execute the instructions to: obtain a first set of ECG data from the ECG device, the first set of ECG data being measured from a patient during a first time period; obtain a second set of ECG data from the ECG device, the second set of ECG data being measured from the patient during a second time period distinct from the first time period; obtain a first three-dimensional (3D) object based on the first set of ECG data; obtain a second 3D object based on the second set of ECG data; and control the user interface to simultaneously display the first 3D object and the second 3D object on a same axis.

Abstract: A system for diagnosing a progressive structural heart disease including a 6-lead electrocardiogram (ECG), a 12-lead ECG, and an analysis device. The analysis device including a processor configured to obtain 12-lead ECG data corresponding to a patient, analyze the 12-lead ECG data for signs of the progressive structural heart disease; based on the 12-lead ECG data indicating the disease: obtain multiple sets of 6-lead ECG data corresponding to the patient, the multiple sets of 6-lead ECG data being obtained during spaced apart time intervals to show changes over time; perform serial analysis on the multiple sets of the 6-lead ECG data to validate the indicated progressive structural heart disease; and control an operation of the detection device based on a result of the analysis.

Abstract: Systems are provided for detecting potential pacemaker lack of capture in surface electrocardiogram (ECG) signals. In one example, a system includes a plurality of electrodes configured to measure electrical potential generated at a skin of a patient, an electrode monitor configured to generate an ECG signal from the electric potential, an interface, a memory storing instructions, and at least one processor configured to execute the instructions to: obtain a baseline ECG signal of the patient, obtain a current ECG signal of the patient from the electrode monitor, determine, based on the baseline ECG signal and/or the current ECG signal, that the patient has an electronic implant carrying out biventricular pacing, compare, based on the determination, the baseline ECG signal to the current ECG signal of the patient, and indicate, through the interface, a degradation condition of the electronic implant based on the comparing and the determination.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method of processing ECG data includes generating a first feature set with a trained neural network using ECG data and processing a patient's ECG data using a criteria-based algorithm to generate a second feature set. The patient's ECG data is then clustered into a number of clusters based on the first feature set and the second feature set to generate clustered ECG data. The clustered ECG data is presented to a user via a user interface, and user input is received from the user via the user interface regarding the clustered ECG data. A feature vector is defined based on the user input and the feature vector is applied to at least a portion of the patient's ECG data to generate revised clustered ECG data. The revised clustered ECG data is then presented to the user via the user interface.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Method and system for displaying measured health data are disclosed herein. An example method includes receiving configuration for a rules engine which comprises a plurality of configurable rules. Each rule defines a link to specific information in an electronic medical record (EMR) in response to the measured health data meeting one or more criteria. The method further includes receiving the measured health data for a subject; linking the measured health data to the specific information in the EMR in response to the measured health data meeting the one or more criteria based on the configured rules engine; and displaying the measured health data and the specific information in the EMR which is linked to the measured health data.

Abstract: 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.

Abstract: The system and method of the present application selects and presents ECGs that are most important to the user in conjunction with a measurement trend that relates to the diagnosis and management of the abnormality. In addition, the system and method of the present application will guide the user to verify whether the ECGs selected by the computer were valid and if not guide the user through measurement trends to find 12-ECGs of significance.

Abstract: The system of the present application includes computerized diagnostic ECG that is tailored for the EMR. The system and method of the present application provides several new approaches to the computerized ECG based on information available from the EMR through an EMR portal. Some of these information items include: Test indication and reason for performing the ECG, previous ECGs as a measure of the patient's “normal” baseline; electrolytes, and drugs known to cause cardiac toxicity/prolonged QT. Based on these inputs, the computerized ECG analysis will behave differently, including the formation of reports, the ancillary information supplied with the ECG and the interpretation itself.

Abstract: Techniques for rules engine referencing are described herein. The techniques may include receiving electrocardiograph (ECG) data for a subject and identifying an electronic medical record (EMR) associated with the subject. The techniques further including referencing a rules engine including multiple configurable conditions defining when specific ECG data is to be linked to the EMR, and linking the specific ECG data to the EMR if one or more of the plurality of configurable conditions are met.

Abstract: 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.

Abstract: 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.