Automatic sleep stage method based on electroencephalogram, heart rate variability and coherence between electroencephalogram and heart rate variability

Abstract

The invention discloses an automatic sleep stage method based on electroencephalogram, heart rate variability and the coherence between the electroencephalogram and the heart rate variability. The method comprises the following steps of extracting electroencephalogram signals and heart rate variability signals of a testee, extracting 9 feature vectors from the electroencephalogram signals and the heart rate variability signals, carrying out principal component analysis on the 9 feature vectors, and using a support vector machine classifier to identify features after feature extraction to carry out automatic sleep stage. The method can effectively improve the accuracy and the convenience of an automatic sleep stage system, and the method can obtain considerable social benefits and economical benefits. The optimal implementation scheme is patent transfer, technical cooperation or product development. The automatic sleep stage method is simple in operation and high in sensibility. The products based on the automatic sleep stage method can be applied to various scenes of sleep monitoring, sleep scientific researches and the like.

Description

technical field [0001] The invention relates to the field of automatic sleep staging, in particular to an automatic sleep staging method based on EEG, heart rate variation and their coherence. Background technique [0002] Sleep is an important physiological activity of the human body, and about 1 / 3 of human life is spent in sleep. Sleep is the body's self-repair and self-recovery process, and good sleep is the basic need for maintaining physical and mental health. Sleep staging is the basis and premise for exploring the changes in physiological regulation during sleep and evaluating sleep quality. More importantly, as the core technical module of sleep monitors, sleep staging provides a multi-modal physiological study for sleep. Reliable quantitative indicators. [0003] Sleep staging is to divide sleep into different stages according to the difference of human physiological signals during sleep. It is mainly divided into non-rapid eye movement (NREM) and rapid eye movem...

AI Technical Summary

Problems solved by technology

It simultaneously records EEG, EOG (oculoelectric signal) and EMG (myoelectric signal), on this basis according to the R&K rule [1] , to determine the sleep state and sleep stage, but this method needs to wear at least ten electrodes, and the subjects often feel uncomfortable, which greatly interferes with normal sleep

Moreover, the method of manual interpretation of EEG waveforms is generally used, but this requires a large workload, and depends on the personal experience of the interpreter, often contains subjective factors, and lacks consistent objective standards.

(2) EEG monitoring methods, ECG monitoring methods, blood oxygen saturation monitoring methods, and physical activity change recording methods mostly use a single parameter or simply integrate various physiological parameters as indicators for automatic sleep staging, and the accuracy rate is low

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