Automatic sleep staging method based on multi-parameter feature combination

A feature fusion, sleep staging technology, applied in medical science, sensors, diagnostic recording/measurement, etc., can solve problems such as the accuracy rate of only 81.65%, and achieve improved accuracy and generalization ability, good application prospects, and reliability. high effect

CN107007278AInactive Publication Date: 2017-08-04SUZHOU INST OF BIOMEDICAL ENG & TECH CHINESE ACADEMY OF SCI
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Publication Date
2017-08-04
Estimated Expiration
Not applicable · inactive patent

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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.
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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...

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Embodiment Construction

[0026] The present invention will be further described in detail below in conjunction with the accompanying drawings, so that those skilled in the art can implement it with reference to the description.

[0027] 1. Data Acquisition and Preprocessing

[0028] The data used in this case comes from the MIT-BIH polysomnographic database (Goldberger AL, Amaral LAN, Glass L, et al. MIT-BIH Polysomnographic database. [DB / OL]. [2000-06-13]), the database records Signals of multiple physiological parameters during sleep of 16 test subjects were obtained, and the sampling frequency was 250Hz. Among the 16 test subjects, only slp32, slp41, slp45, and slp48 test individuals contained complete EEG, ECG, EMG (from the jaw), respiratory signals and complete sleep stages, so samples slp32, slp41, slp45, and slp48 were selected as experimental subjects. All the data is divided into one section every 30 seconds, and the sleep staging is judged by experts according to the R&K standard. In this ...