EEG signal processing method and system for sleep state monitoring

Abstract

The invention relates to a method and a system for processing electroencephalogram for monitoring sleeping state. The method comprises the following steps: collecting the electroencephalogram generated in the sleeping process of a user; performing wavelet decomposition on the electroencephalogram, regulating wavelet coefficient after decomposition and filtering the noise; extracting a delta wave frequency band, a theta wave frequency band, an alpha wave frequency band and a beta wave frequency band of the electroencephalogram in wavelet reconstruction and respectively calculating the characteristic quantity of the delta wave frequency band, the theta wave frequency band, the alpha wave frequency band and the beta wave frequency band; confirming the characteristic information corresponding to the type of the sleeping state recognition task according to the characteristic quantity of the delta wave frequency band, the theta wave frequency band, the alpha wave frequency band and the beta wave frequency band. On the basis of the technical scheme provided by the invention, the filtering and noise reduction are realized in wavelet decomposition, the characteristic quantity calculation is realized in wavelet reconstruction and the processing efficiency of the electroencephalogram is increased.

Description

technical field [0001] The invention relates to the technical field of sleep aids, in particular to a method and system for processing brain electrical signals for sleep state monitoring. Background technique [0002] During sleep, the human body undergoes a process of self-relaxation and recovery, so good sleep is a basic condition for maintaining good health; however, due to work pressure, irregular life schedule and other reasons, some people have poor sleep quality , manifested as insomnia, waking up in the middle of the night, etc. [0003] There are already some devices on the market to help people fall asleep and improve the quality of sleep. For example, in a specific sleep state, manual intervention such as sound and light signals can be used to avoid waking up the user in a deep sleep state. For devices that assist sleep, in order to really achieve the purpose of improving the user's sleep quality, it is very important to correctly identify the user's sleep state...

AI Technical Summary

Problems solved by technology

[0004] To identify the sleep state of the user, at present, polysomnography (PSG), also known as sleep EEG, is mainly used. Polysomnography uses a variety of vital signs to analyze sleep. Among these sign signals, the brain Electricity is at the core; using the four rhythms of brain waves: delta waves (1-3Hz), theta waves (4-7Hz), alpha waves (8-12Hz), and beta waves (14-30Hz) as characteristic information, In the traditional method, when processing and extracting signal features of EEG signals, db4 wavelet is generally used for 8-layer decomposition and reconstruction, and then the DC component or baseline drift in the EEG signals is filtered out, and then used when extracting signal features The db4 wavelet decomposes the filtered signal in 8 layers, and obtains four rhythms of the EEG signal: δ wave frequency band, θ wave frequency band, α wave frequency band, and β wave frequency band; Inefficient electrical signal processing

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