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 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: Various methods and systems are provided for analyzing an electrocardiogram (ECG) in real-time using machine learning to identify heartbeats, calculate a cycle length for each heartbeat, and display the cycle length for each heartbeat at a user interface. Waveform morphology of ECG data is continuously learned to identify recurrent signals and generate templates based on recurrent signals, to which ECG data is compared to identify and display heartbeats. Generated templates are continuously updated to reflect changing waveform morphologies.

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 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: A cluster database includes existing ECG datasets organized into clusters, wherein each existing ECG dataset includes an existing ECG waveform with at least one corresponding existing feature and existing interpretation. Each cluster is comprised of existing ECG datasets having a common existing feature. The cluster training module is executable by the processor to receive a new ECG waveform and a feature extracted from the new ECG waveform. The cluster training module then selects a cluster interpretation module based on the feature, wherein the cluster interpretation module is trained on one of the clusters from the cluster database. The cluster training module processes the new ECG waveform and/or the feature to provide a cluster interpretation output. The cluster interpretation output is then displayed on the user interface, and the cluster training module receives clinician input via the user interface accepting or rejecting the cluster interpretation output.

Abstract: A method of directing positioning of ECG electrodes on a patient includes receiving at a processor an image of the patient with one or more electrodes and determining with the processor an actual location of each of the electrodes on the patient based on the image. The method further includes determining with the processor whether the actual location of each of the electrodes is correct and providing information via a user interface regarding the actual location of the electrodes.

Abstract: A cluster database includes existing ECG datasets organized into clusters, wherein each existing ECG dataset includes an existing ECG waveform with at least one corresponding existing feature and existing interpretation. Each cluster is comprised of existing ECG datasets having a common existing feature. The cluster training module is executable by the processor to receive a new ECG waveform and a feature extracted from the new ECG waveform. The cluster training module then selects a cluster interpretation module based on the feature, wherein the cluster interpretation module is trained on one of the clusters from the cluster database. The cluster training module processes the new ECG waveform and/or the feature to provide a cluster interpretation output. The cluster interpretation output is then displayed on the user interface, and the cluster training module receives clinician input via the user interface accepting or rejecting the cluster interpretation output.

Abstract: A cluster database includes existing ECG datasets organized into clusters, wherein each existing ECG dataset includes an existing ECG waveform with at least one corresponding existing feature and existing interpretation. Each cluster is comprised of existing ECG datasets having a common existing feature. The cluster training module is executable by the processor to receive a new ECG waveform and a feature extracted from the new ECG waveform. The cluster training module then selects a cluster interpretation module based on the feature, wherein the cluster interpretation module is trained on one of the clusters from the cluster database. The cluster training module processes the new ECG waveform and/or the feature to provide a cluster interpretation output. The cluster interpretation output is then displayed on the user interface, and the cluster training module receives clinician input via the user interface accepting or rejecting the cluster interpretation output.

Abstract: A cluster database includes existing, ECG datasets organized into clusters, wherein each existing ECG dataset includes an existing ECG waveform with at least one corresponding existing feature and existing interpretation. Each cluster is comprised of existing ECG datasets having a common existing feature. The duster training module is executable by the processor to receive a new ECG waveform and a feature extracted from the new ECG waveform. The cluster training module then selects a cluster interpretation module based on the feature, wherein the cluster interpretation module is trained on one of the clusters from the duster database. The duster training module processes the new ECG waveform and/or the feature to provide a duster interpretation output. The cluster interpretation output is then displayed on the user interface, and the cluster training module receives clinician input via the user interface accepting or rejecting the cluster interpretation output.

Abstract: A method of directing positioning of ECG electrodes on a patient includes receiving at a processor an image of the patient with one or more electrodes and determining with the processor an actual location of each of the electrodes on the patient based on the image. The method further includes determining with the processor whether the actual location of each of the electrodes is correct and providing information via a user interface regarding the actual location of the electrodes.

Abstract: A method of analyzing electrocardiograph (ECG) data includes receiving a first representative ECG of a patient and isolating a first principal component, a second principal component, and a third principal component of the first representative ECG. The principal components are isolated by selecting a portion of the first representative ECG relating to depolarization, calculating a covariance matrix based on the portion of the first representative ECG, conducting a principal component analysis of the covariance matrix, and selecting a first component of the principal component analysis as the first principal component, the second component of the principal component analysis as the second principal component, and the third component of the principal component analysis as the third principal component. A depolarization subspace is then formed based on the first principal component, second principal component, and the third principal component of the first representative ECG.

Abstract: A method of analyzing electrocardiograph (ECG) data includes receiving a first representative ECG of a patient and isolating a first principal component, a second principal component, and a third principal component of the first representative ECG. The principal components are isolated by selecting a portion of the first representative ECG relating to depolarization, calculating a covariance matrix based on the portion of the first representative ECG, conducting a principal component analysis of the covariance matrix, and selecting a first component of the principal component analysis as the first principal component, the second component of the principal component analysis as the second principal component, and the third component of the principal component analysis as the third principal component. A depolarization subspace is then formed based on the first principal component, second principal component, and the third principal component of the first representative ECG.

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: Devices and methods comprise or provide an ECG recording device containing a mailable base and electrode assembly and/or other means engageable with the base to receive ECG signals from a subject during a self-administered ECG examination. The devices and methods may also include a single-use or limited-use ECG recording device, in which the base is disposable or reusable or recyclable. In addition, the device may be self-contained, battery-operated, portable, disposable, mailable to a location remote from an ECG examination, provide feedback, indicate its method of use, including graphically depicting same, contain a finger cuff and/or sensor pad for receiving ECG signals, and/or contain a memory. Preferably, the base conforms to various body shapes and/or sizes, is made of flexible and/or semi-flexible material, and/or contains a receptor, such as sealable blood well.

Abstract: 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.

Abstract: A method for evaluating an electrocardiogram is disclosed herein. The method includes measuring an electrical activity of a patient, processing the measured electrical activity to form a multi-lead signal, and extracting a segment of the multi-lead signal. The method for evaluating an electrocardiogram also includes transforming the segment of the multi-lead signal into a synthesized signal that is most representative of the patient's electrical activity, and evaluating the synthesized signal. A corresponding system for evaluating an electrocardiogram is also disclosed.