Automatic sleep staging method based on multiple electroencephalogram and electromyography characteristics

Abstract

The invention relates to an automatic sleep staging method based on multiple electroencephalogram and electromyography characteristics. The method comprises the following steps: collecting an electroencephalogram signal and an electromyography signal; utilizing wavelet decomposition to remove high-frequency noises from the electroencephalogram signal and the electromyography signal; extracting an energy ratio of alpha, beta, theta and delta characteristic waves of the electroencephalogram signal after removing the noise, thereby acquiring a first characteristic parameter; utilizing a sample entropy method to extract a sample entropy of the electroencephalogram signal, thereby acquiring a second characteristic parameter; utilizing a wavelet decomposition algorithm to extract a high-frequency characteristic energy ratio in the electromyography signal, thereby acquiring a third characteristic parameter; and inputting the first characteristic parameter, the second characteristic parameter and the third characteristic parameter to a support vector machine and performing training and testing, thereby acquiring a classifying result. According to the invention, the method for extracting multiple EEG and EMG characteristics is adopted and a support vector machine classifier is combined, so that the accuracy of the sleep staging is promoted; a cross validation result proves that the method has certain generalization ability; an experimental result is high in reliability; and the application prospect is excellent.

Description

technical field [0001] The invention relates to a sleep staging method, in particular to an automatic sleep staging method based on multiple features of brain electricity and myoelectricity. Background technique [0002] With fierce competition in modern society, fast-paced work and life have had a huge impact on people's sleep. According to the World Health Organization statistics, 27% of people have sleep disorders. At present, sleep disorder has been confirmed as a disease with public hazards, and more and more people pay more and more attention to it. Staging the sleep state of the human body through various physiological signals is an effective method for objectively evaluating sleep quality. [0003] Extracting the characteristic parameters of the electroencephalogram (EEG) through different analysis methods, and then using a classifier to classify is a classic method of sleep staging. At present, the analysis methods of EEG are mainly analyzed from its time domain,...

AI Technical Summary

Problems solved by technology

If the method of using discrete wavelet transform combined with nonlinear support vector machine meets the requirements of the model for generalization ability, the accuracy rate is only 81.65%.

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