Abstract
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.
Highlights
Sleep is an indispensable physiological process in people’s daily life
E data in Table 2 show that the accuracy of sleep staging for selected normal sleep samples and sleep disorder samples is higher than 86%. e accuracy of 5 different sleep periods is different, among which the accuracy of Wake period classification is higher and the accuracy of REM period is lower. e reason may be that there are eye movements during the rapid eye movement period. e EOG signal is more obvious than the EEG signal, so this period cannot be accurately divided
Subsequent research will focus on improving the classification algorithm, hoping to increase the classification accuracy to more than 90%. e sleep quality detection method based on EEG signal in this paper has been verified and can be applied in real life
Summary
Sleep is an indispensable physiological process in people’s daily life. Every day, most people spend about 30% of their time sleeping. EEG can accurately and quickly reflect the physiological changes of the human body; it belongs to an advanced biological signal It provides important analytical reference information for neurology, medicine, and other disciplines. It can accurately reflect the activity of the brain, and its application in sleep research is gradually becoming common. Erefore, the higher the accuracy of sleep staging, the more accurate the feedback of sleep quality. Considering that EEG data are time series data in order to establish a sleep quality detection model with high accuracy and fast calculation speed, this research proposes a detection method with higher accuracy and timeliness. The used detection framework is used as the sleep quality detection module to classify and recognize the input original sleep EEG to obtain the final recognition result. In addition to basic sleep data, the cloud platform can collect the user’s sleep quality diagnostic information. ese diagnostic information can assist related companies to develop more accurate products for users
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