Electrocardiosignal processing method

Abstract

The invention discloses an electrocardiosignal processing method. The electrocardiosignal processing method includes the following steps that A, an original electrocardiosignal set theta is collected and preprocessed to construct an input electrocardiosignal signal theta'; B, a sample F' is extracted, a hidden layer is trained, and a self-learning set phi is output; C, a label vector G of the self-learning set phi is detected, and a node is output; D, an input layer, the hidden layer and an output layer of an artificial neural network are stacked, so that a complete electrocardiosignal disease monitoring artificial neural network is established. Thus, basic waveform positioning is not needed, dependence on artificially selected characteristics is avoided, the requirement for priori knowledge is low, important characteristics in data can be automatically and abstractly input, and the dimensionality of an input electrocardiosignal sample is reduced through PCA whitening dimensionality reduction applied during preprocessing.

Description

technical field [0001] The invention relates to the technical field of electrocardiographic signals, in particular to a method for processing electrocardiographic signals. Background technique [0002] Electrocardiography (ECG) is a time-varying potential signal collected through body surface electrodes when the human heart performs physiological activities, which contains rich biological information. As one of the earliest data indicators to be studied and applied in clinical medicine, ECG signals have important scientific research value and practical significance, and can effectively test and predict various diseases related to the heart. In recent years, more and more attention has been paid . But on the other hand, the ECG signal has the characteristics of high sample dimension, relatively small sample size, and nonlinear relationship in feature space, so its analysis is difficult. Currently, machine learning algorithms are generally used for processing. [0003] Such...

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Problems solved by technology

[0012] The technical problem to be solved by the present invention is that, aiming at the problems of the above-mentioned prior art method that the central electrical signal disease detection effect is not good and there are too many redundant signals, and the network structure required for ANN disease monitoring is huge, the training is complicated and the effect is not good, it provides An ECG Signal Processing Method

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