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 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:
A method for non-invasive treatment of cardiac arrhythmias is provided. The method includes acquiring body surface electrical signals at locations on a body surface of a living being from electrodes placed on locations of the body surface, reconstructing three-dimensional heart and torso anatomical models of the living being from an imaging scan, and calculating an electrical activity a throughout three-dimensional volume of the heart by electrocardiogram inverse problem solving based at least in part on the acquired body surface electrical signals and the reconstructed three-dimensional heart and torso anatomical models. The method also includes identifying at least one location of at least one site of origin of a cardiac arrhythmia according to the calculated electrical activity within the heart, and delivering focused energy to the identified at least one location of the at least one site of origin of the cardiac arrhythmia.
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 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.