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: 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.

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 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.