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AssigneeLepu Medical Technology (Bejing) Co., Ltd.
InventorsChang Liu, Chuanyan Hu, Weiwei Zhou, Haitao Lu, Jiayu Wang, Jun Cao
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A self-learning dynamic electrocardiography analysis method employing artificial intelligence. The method comprises: pre-processing data, performing cardiac activity feature detection, interference signal detection and cardiac activity classification on the basis of a deep learning method, performing signal quality evaluation and lead combination, examining cardiac activity, performing analytic computations on an electrocardiogram event and parameters, and then automatically outputting report data. The method achieves an automatic analysis method for a quick and comprehensive dynamic electrocardiography process, and recording of modification information of an automatic analysis result, while also collecting and feeding back modification data to a deep learning model for continuous training, thereby continuously improving and enhancing the accuracy of the automatic analysis method. Also disclosed is a self-learning dynamic electrocardiography analysis device employing artificial intelligence.
An artificial intelligence self-learning-based ambulatory electrocardiogram analysis method, comprising:
(canceled)
A computer readable storage medium, comprising instructions, wherein when the instructions run on a computer, the computer executes the method of claim.
Dependencies are inferred from the claim text.