Waking state detection method and system in sleep state analysis

Abstract

The invention relates to a method and a system for detecting a waking state in sleep state analysis. The method comprises the following steps: acquiring a real-time electroencephalogram signal of a user after the sleep process of the user starts; implementing wavelet decomposition on the real-time electroencephalogram signal, reconstructing a signal according to a set low-frequency stage wavelet coefficient to obtain an electroencephalogram signal; calculating a sample entropy of the electroencephalogram signal, comparing the sample entropy with a sample entropy threshold calculated in advance; if the sample entropy is higher than the sample entropy threshold, then judging that the user is at a waking state. According to the technical scheme of the method, the influence on the detection result, caused by the interference on the electroencephalogram signal, is avoided, the waking state of the electroencephalogram signal can be accurately detected, and the recognition accuracy of the sleep state is effectively improved.

Description

Technical field [0001] The invention relates to the technical field of assisted sleep, in particular to a method and system for detecting awake state 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, assisted sleep, in order to improve the quality of sleep of users. Sleep state analysis is an important means for assisting devices to understand the user's sleep quality. In this process, the user's sleep state needs to be detected to accurately know whether the user is awake or asleep before corresponding intervention measures can be taken. [0003] Polysomnography (PSG), also known as sleep EEG, is currently the "gold standard" for 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 brain waves are used: delta wave (1-3Hz), θ wave (4-7Hz), α The frequency characteristics of wave (8...

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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