Abstract: The exemplified technology facilitates de-noising of magnetic field-sensed signal data (e.g., of an electrophysiological event) using signal reconstruction processes that fuse the magnetic field-sensed signal data with another sensed signal data (e.g., voltage gradient signal data) captured simultaneously with the magnetic field-sensed signal data. To this end, the purely algorithmic processing technique beneficially facilitates removal and/or filtering of noise from a sensor lead of a noisy captured source and rebuilds the signal for that lead from information simultaneously obtained from other leads of a different source. In some embodiments, a data are fused via a sparse approximation operation that uses candidate terms based on Van der Pol differential equations.

Abstract: The present disclosure generally relates to systems and methods and systems of a noninvasive technique for characterizing cardiac chamber size and cardiac mechanical function. A mathematical analysis of three-dimensional (3D) high resolution data may be used to estimate chamber size and cardiac mechanical function. For example, high-resolution mammalian signals are analyzed across multiple leads, as 3D orthogonal (X,Y,Z), or 10-channel data, for 30 to 800 seconds, to derive estimates of cardiac chamber size and cardiac mechanical function. Multiple mathematical approaches may be used to analyze the dynamical and geometrical properties of the data.

Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.

Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.

Abstract: Methods and systems for evaluating the electrical activity of the heart to identify novel ECG patterns closely linked to the subsequent development of serious heart rhythm disturbances and fatal cardiac events. Two approaches are describe, for example a model-based analysis and space-time analysis, which are used to study the dynamical and geometrical properties of the ECG data. In the first a model is derived using a modified Matching Pursuit (MMP) algorithm. Various metrics and subspaces are extracted to characterize the risk for serious heart rhythm disturbances, sudden cardiac death, other modes of death, and all-cause mortality linked to different electrical abnormalities of the heart. In the second method, space-time domain is divided into a number of regions (e.g., 12 regions), the density of the ECG signal is computed in each region and input to a learning algorithm to associate them with these events.

Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.

Abstract: The exemplified technology facilitates de-noising of magnetic field-sensed signal data (e.g., of an electrophysiological event) using signal reconstruction processes that fuse the magnetic field-sensed signal data with another sensed signal data (e.g., voltage gradient signal data) captured simultaneously with the magnetic field-sensed signal data. To this end, the purely algorithmic processing technique beneficially facilitates removal and/or filtering of noise from a sensor lead of a noisy captured source and rebuilds the signal for that lead from information simultaneously obtained from other leads of a different source. In some embodiments, a data are fused via a sparse approximation operation that uses candidate terms based on Van der Pol differential equations.

Abstract: Methods and systems for evaluating the electrical activity of the heart to identify novel ECG patterns closely linked to the subsequent development of serious heart rhythm disturbances and fatal cardiac events. Two approaches are describe, for example a model-based analysis and space-time analysis, which are used to study the dynamical and geometrical properties of the ECG data. In the first a model is derived using a modified Matching Pursuit (MMP) algorithm. Various metrics and subspaces are extracted to characterize the risk for serious heart rhythm disturbances, sudden cardiac death, other modes of death, and all-cause mortality linked to different electrical abnormalities of the heart. In the second method, space-time domain is divided into a number of regions (e.g., 12 regions), the density of the ECG signal is computed in each region and input to a learning algorithm to associate them with these events.

Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.

Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.

Abstract: Methods and systems for evaluating the electrical activity of the heart to identify novel ECG patterns closely linked to the subsequent development of serious heart rhythm disturbances and fatal cardiac events. Two approaches are describe, for example a model-based analysis and space-time analysis, which are used to study the dynamical and geometrical properties of the ECG data. In the first a model is derived using a modified Matching Pursuit (MMP) algorithm. Various metrics and subspaces are extracted to characterize the risk for serious heart rhythm disturbances, sudden cardiac death, other modes of death, and all-cause mortality linked to different electrical abnormalities of the heart. In the second method, space-time domain is divided into a number of regions (e.g., 12 regions), the density of the ECG signal is computed in each region and input to a learning algorithm to associate them with these events.

Abstract: The present disclosure generally relates to systems and methods and systems of a noninvasive technique for characterizing cardiac chamber size and cardiac mechanical function. A mathematical analysis of three-dimensional (3D) high resolution data may be used to estimate chamber size and cardiac mechanical function. For example, high-resolution mammalian signals are analyzed across multiple leads, as 3D orthogonal (X,Y,Z), or 10-channel data, for 30 to 800 seconds, to derive estimates of cardiac chamber size and cardiac mechanical function. Multiple mathematical approaches may be used to analyze the dynamical and geometrical properties of the data.

Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.

Abstract: The present disclosure generally relates to systems and methods of a noninvasive technique for characterizing cardiac chamber size and cardiac mechanical function. A mathematical analysis of three-dimensional (3D) high resolution data may be used to estimate chamber size and cardiac mechanical function. For example, high-resolution mammalian signals are analyzed across multiple leads, as 3D orthogonal (X,Y,Z) or 10-channel data, for 30 to 800 seconds, to derive estimates of cardiac chamber size and cardiac mechanical function. Multiple mathematical approaches may be used to analyze the dynamical and geometrical properties of the data.

Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.

Abstract: Methods and systems for evaluating the electrical activity of the heart to identify novel ECG patterns closely linked to the subsequent development of serious heart rhythm disturbances and fatal cardiac events. Two approaches are describe, for example a model-based analysis and space-time analysis, which are used to study the dynamical and geometrical properties of the ECG data. In the first a model is derived using a modified Matching Pursuit (MMP) algorithm. Various metrics and subspaces are extracted to characterize the risk for serious heart rhythm disturbances, sudden cardiac death, other modes of death, and all-cause mortality linked to different electrical abnormalities of the heart. In the second method, space-time domain is divided into a number of regions (e.g., 12 regions), the density of the ECG signal is computed in each region and input to a learning algorithm to associate them with these events.

Abstract: Methods and systems for evaluating the electrical activity of the heart to identify novel ECG patterns closely linked to the subsequent development of serious heart rhythm disturbances and fatal cardiac events. Two approaches are describe, for example a model-based analysis and space-time analysis, which are used to study the dynamical and geometrical properties of the ECG data. In the first a model is derived using a modified Matching Pursuit (MMP) algorithm. Various metrics and subspaces are extracted to characterize the risk for serious heart rhythm disturbances, sudden cardiac death, other modes of death, and all-cause mortality linked to different electrical abnormalities of the heart. In the second method, space-time domain is divided into a number of regions (e.g., 12 regions), the density of the ECG signal is computed in each region and input to a learning algorithm to associate them with these events.

Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.

Abstract: The present disclosure uses physiological data, ECG signals as an example, to evaluate cardiac structure and function in mammals. Two approaches are presented, e.g., a model-based analysis and a space-time analysis. The first method uses a modified Matching Pursuit (MMP) algorithm to find a noiseless model of the ECG data that is sparse and does not assume periodicity of the signal. After the model is derived, various metrics and subspaces are extracted to image and characterize cardiovascular tissues using complex-sub-harmonic-frequencies (CSF) quasi-periodic and other mathematical methods. In the second method, space-time domain is divided into a number of regions, the density of the ECG signal is computed in each region and inputted into a learning algorithm to image and characterize the tissues.

Abstract: A method of evaluating an electrophysiological signal is disclosed. A mathematical reconstruction over at least one cycle of the electrophysiological signal is used to identify an abnormal substrate. A non-transitory computer readable medium is also disclosed. The nontransitory computer readable medium has stored thereon instructions for identifying a pathological substrate from a mathematical reconstruction of an electrophysiological signal, which, when executed by a processor, causes the processor to perform steps comprising using a mathematical reconstruction over many cycles of the electrophysiological signal to identify a pathological state. A system for evaluating an electrophysiological signal includes a processor configured to identify a pathological condition from a mathematical reconstruction of the electrophysiological signal. The system also includes a data input coupled to the processor and configured to provide the processor with the electrophysiological signal.