Depth model for arrhythmia classification, and method and device utilizing model

Abstract

The invention provides a depth model for arrhythmia classification and a method and device using the model, and the depth model comprises a representation learning part and a sequence learning part. The representation learning part is used for receiving an equal-length sequence analyzed by the original electrocardiosignal; the representation learning part is constructed based on an MSCNN structure and is composed of two convolution block branches stacked in different scales; the convolution kernel of the first branch is large in scale and used for capturing low-frequency information of electrocardiosignals and outputting the low-frequency information in a multi-scale feature mode; the convolution kernel of the second branch is small in scale and used for capturing high-frequency information of the electrocardiosignal and outputting the high-frequency information in a multi-scale feature mode; the multi-scale feature output by the first branch and the multi-scale feature output by the second branch are spliced to form a multi-scale depth feature which is input to a sequence learning part; the sequence learning part is constructed on the basis of a Seq-Seq network taking LSTM as a basic unit, and an attention mechanism layer is arranged between an encoder and a decoder of the Seq-Seq network; the output is a time sequence depth feature.

Description

technical field

[0001] The invention relates to arrhythmia detection technology, in particular to an automatic arrhythmia detection method without filtering noise reduction, abnormal value detection and manual feature extraction. Background technique

[0002] According to statistics, death from acute heart disease accounts for nearly half of the total number of deaths from cardiovascular diseases, and nearly 31% of deaths worldwide are related to cardiovascular diseases. The main cause of sudden cardiac death is arrhythmia. Electrocardiography is the standard non-invasive tool for recording cardiac activity and is currently the most widely used and most reliable means of detecting arrhythmias. The arrhythmia classification of heartbeat signals not only takes a lot of time for cardiologists, but also increases the workload, which is still a relatively challenging task. This requires a lightweight automatic arrhythmia detection algorithm to provide auxiliary decision support ...

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