Sleep is the most important physiological process related to human health. The development of society has accelerated the pace of people’s lives and has also increased people’s life pressure. As a result, more and more people suffer from reduced sleep quality, and the resulting diseases are also increasing. In response to this problem, this study proposes a sleep quality detection and management method based on electroencephalogram (EEG). The detection of sleep quality is mainly achieved by staging sleep EEG signals. First, wavelet packet decomposition (WPD) preprocesses the collected original EEG to extract the four rhythm waves of EEG. Second, the relative energy characteristics and nonlinear characteristics of each rhythm wave are extracted. The multisample entropy (MSE) values of different scales are calculated as the main features, and the rest are auxiliary features. Finally, the long short-term memory (LSTM) model is applied to classify the extracted sleep features, and the final result is obtained. Experiments were conducted in the MIT-BIH public database. The experimental results show that the method used in this article has a high accuracy rate for sleep quality detection. For the detected sleep quality data, the data are managed in combination with the mobile terminal software. Management is mainly embodied in two aspects. One is to query and display historical sleep quality data. The second is that when there are periodic abnormalities in the detected sleep quality data, the user will be reminded so that the user can respond in time to ensure physical fitness.
A Smart Detection Method of Sleep Quality Using EEG Signal and Long Short-Term Memory Model
