Waking state detection device in sleep state analysis

Abstract

The invention relates to a waking state detecting device for sleep state analysis, which comprises a brain electrical electrode, a reference electrode, an analog-digital converter, a filter circuit and a processor, wherein the brain electrical electrode and the reference electrode are connected with the analog-digital converter respectively, and are connected to the processor sequentially through the analog-digital converter and the filter circuit; the brain electrical electrode is used for detecting a brain electrical signal of a user during sleep; the analog-digital converter converts the brain electrical signal into a digital signal; the filter circuit performs low-frequency filtering on the brain electrical signal and then inputs the signal to the processor; the processor extracts the real-time brain electrical signal of the user, performs wavelet decomposition on the real-time brain electrical signal and performs signal reconstruction according to the set low-frequency-band wavelet coefficient to obtain the brain electrical signal; the sample entropy of the brain electrical signal is calculated and then is compared with the sample entropy threshold value; when the sample entropy is larger than the sample entropy threshold value, the device judges that the user is in the waking state at present. The waking state detecting device for sleep state analysis provided by the invention can accurately detect the waking state of the brain electrical signal, so that the accuracy rate of sleep state identification is effectively increased.

Description

technical field [0001] The invention relates to the technical field of sleep aids, in particular to an awake state detection device in sleep state analysis. Background technique [0002] At present, there are some auxiliary devices on the market to assist people to fall asleep, that is, sleep aids, so as to improve the sleep quality of users. Sleep state analysis is an important means for auxiliary devices to understand the user's sleep quality. During this process, the user's sleep state needs to be detected to accurately know whether the user is awake or asleep, and then corresponding intervention measures can be carried out. [0003] Polysomnography (PSG), also known as sleep EEG, is currently the "gold standard" for clinical sleep diagnosis and analysis. Polysomnography uses a variety of vital signs to analyze sleep. Among these signs, EEG is at the core; four rhythms of EEG are used: delta waves (1-3Hz), theta waves (4-7Hz), alpha Wave (8-12Hz), frequency characterist...

AI Technical Summary

Problems solved by technology

[0004] In general, to detect whether the user is awake or not is to use brain waves in four frequency bands (delta wave frequency band, theta wave frequency band, alpha wave frequency band and beta wave frequency band) to train the recognition model (classifier) ​​of the awake state. To identify EEG signals, these recognition models are often general recognition models that are trained using other people’s EEG signals, but due to the strong individual specificity of EEG signals, and the strength of EEG is very weak (EEG is microvolt level, the ECG is at the millivolt level), and it is very easy to be interfered by external signals during signal collection, which makes it easy to be affected by interference during the awake state detection process, making it difficult to accurately detect the user’s awake state, which may easily lead to assisted sleep The wrong intervention was implemented, affecting the user's sleep quality

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