6 results about "12 lead ecg" patented technology

The present disclosure provides systems and methods for detection of cardiac amyloidosis from electrocardiogram (ECG) signals. In particular, the present disclosure identified critical novel features that can be incorporated in systems and methods for the detection of cardiac amyloidosis from one or more ECG signals.

The application discloses a kind of cardiac transmembrane potential imaging methods based on 12 lead electrocardiogram joint physiological model and attention mechanism, to solve the serious ill-conditioned problem caused by information asymmetry between 12 lead signal and TMP, reconstruct cardiac transmembrane potential from 12 lead ECG signal.In order to obtain the dynamic physiological information of BSP and TMP, the application introduces self-attention mechanism to capture the long-term, periodic dynamic activation process characteristics of cardiac electrophysiology, extract the similarity within the sequence, calculate the attention score, then multiply the score with the input sequence, extract the key segment in the sequence, i.e.the segment that is expected to be "noticed".Meanwhile, the application regards the inverse problem as the potential feature mapping of TMP on BSP, captures the dynamic characteristics of inverse relationship by introducing cross-attention module, learns the inverse feature mapping relationship from a large number of data pairs using neural network, and solves the ill-posed problem of inverse problem.

The present disclosure provides systems and methods for detection of aortic valve stenosis (AVS) from parameters derived from one or more electrocardiogram (ECG) leads. In particular, the present disclosure identified critical novel features that can be incorporated in systems and methods for the detection of aortic valve stenosis (AVS) from such parameters.

The present disclosure provides systems and methods for detection of cardiac amyloidosis from electrocardiogram (ECG) signals. In particular, the present disclosure identified critical novel features that can be incorporated in systems and methods for the detection of cardiac amyloidosis from one or more ECG signals.

The present disclosure provides systems and methods for detection of cardiac amyloidosis from electrocardiogram (ECG) signals. In particular, the present disclosure identified critical novel features that can be incorporated in systems and methods for the detection of cardiac amyloidosis from one or more ECG signals. In some embodiments, it includes annotating a plurality of electrocardiogram (ECG) parameters in a database as associated with one or more cardiac amyloidosis designations or a negative cardiac amyloidosis control designation; instructing a machine learning model to distinguish the plurality of electrocardiogram (ECG) parameters; applying a machine learning analysis to the plurality of distinguished electrocardiogram (ECG) parameters thereby providing a numeric value that represents the contribution of each parameter to the one or more cardiac amyloidosis designation and identifies one or more electrocardiogram (ECG) parameters that are informative of cardiac amyloidosis.

The present disclosure provides systems and methods for detection of aortic valve stenosis (AVS) from parameters derived from one or more electrocardiogram (ECG) leads. In particular, the present disclosure identified critical novel features that can be incorporated in systems and methods for the detection of aortic valve stenosis (AVS) from such parameters.