Automatic sleep staging method based on multi-parameter feature combination

Abstract

The invention relates to an automatic sleep staging method based on a multi-parameter feature combination. The method includes the steps of collecting EEG signals, EMG signals, ECG signals and respiration signals, denoising all signals, extracting energy ratios of alpha, beta, theta and delta characteristic waves of the EEG signals, extracting the sample entropy of the EEG signals by a sample entropy algorithm, extracting the high frequency characteristic energy ratio of the EMG signals by a wavelet decomposition algorithm, extracting the sample entropy of the ECG signals by the sample entropy algorithm, extracting the mean value of the respiration signals by an averaging method, inputting the five feature parameters into a support vector machine for training and testing, thereby obtaining classification results. According to the automatic sleep staging method, the method of extracting EEG, EMG, ECG and respiration multiple characteristics is adopted to greatly improve the accuracy and generalization ability of sleep staging. The experimental results are reliable and accurate in sleep staging, thereby providing an effective basis for assessing sleep quality and being of a good application prospect.

Description

technical field [0001] The invention relates to a sleep staging method, in particular to an automatic sleep staging method based on multi-parameter feature fusion. 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. In the prior art, someone divides the sleep state into five phases by performing nonlinear symbolic dynamics...

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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