Classification method for arrhythmia based on one-dimension convolution neural-network and S transformation

Abstract

Provided is a classification method for arrhythmia based on a one-dimension convolution neural-network and S transformation. The method includes the steps of pre-possessing electrocardiosignal; extracting the deep nonlinear characteristic of the electrocardiosignal by using the one-dimension convolution neural-network; extracting the time-frequency domain characteristic of the electrocardiosignalby using the S transformation; combining the deep nonlinear characteristic of the electrocardiosignal with the time-frequency domain characteristic of the electrocardiosignal, continuing to conduct characteristic learning after passing through a whole-connection layer, and obtaining the output characteristic of the whole-connection layer; conducting classification after the output characteristic of the whole-connection layer is connected to the softmax layer of the one-dimension convolution neural-network; outputting a classification result. According to the method, it is not necessary to conduct compression and bilinear interpolation on the electrocardiosignal to obtain the picture form of fixed pixel points to extract the characteristics. According to the method, in the aspect of characteristic extraction, the advantages of a deep learning characteristic and the time-frequency domain characteristic are combined to be composed into more complete characteristics, convergence can be quickened, over-fitting is controlled, and the insensitivity of the interwork to initialization weight is lowered. The accuracy rate of multiple kinds of arrhythmia identification is improved.

Description

technical field [0001] The invention relates to a method for classifying arrhythmia. In particular, it relates to an arrhythmia classification method based on one-dimensional convolutional neural network and S-transform. Background technique [0002] Arrhythmia is an abnormal phenomenon of irregular heart function caused by disturbance of the rate, rhythm or conduction of the heart's electrical signals, and is considered the most common heart disease. It can be detected by analyzing the recorded ECG waveform, which is formed by different potentials associated with the depolarization and repolarization patterns of the atria and ventricles, and provides important information about the condition of the heart. For doctors, it is difficult to analyze long-term ECG signals in a short period of time and observe the small morphological changes of ECG signals, which may cause doctors to lose important information during diagnosis. Another problem faced by ECG signal analysis is the...

AI Technical Summary

Problems solved by technology

However, through experimental verification, although some hidden features can be extracted, the classification effect is not obvious, and the amount of calculation is large.

[0008] The transformation-based method converts the signal from the time domain to the frequency domain. For example, the Fourier transform can obtain the frequency domain characteristics of the signal, but it cannot be used to represent the time-frequency for the non-stationarity of the ECG signal. Interrelationships between domains

Images