Research On Sleep EEG Signal Based On Multi-scale Permutation Entropy And Internal Composition Alignment

With the rapid development of society,the fast-paced life has caused more and more people to have sleep-related problems,which not only greatly affects people’s normal life,but also affects the healthy development of society,so sleep problems The research has great practical significance.Sleep EEG is a related physiological signal produced by the brain during human sleep.It will change differently in different sleep periods.Therefore,the study of sleep EEG in different sleep periods is very meaningful.In this paper,three algorithms including permutation entropy,multi-scale permutation entropy,and Inner Composition Alignment(IOTA)are used to study the sleep EEG of the human body during wakefulness and NREM-I stage(light sleep stage).The experimental results are used to verify the differences of human EEG signals in different sleep periods.The results of the study are as follows:First,in the study of human sleep EEG during wakefulness and NREM-I using the permutation entropy algorithm,we first selected appropriate parameters through experiments,and found that the best effect was when the embedding dimension was 3 and the delay parameter was 1.Then,different experimenters intercepted samples of the same length and sample data of different lengths for research.The experimental results found that there were significant differences in the sleep EEG of the human body in the wakefulness period and the NREM-I stage,and the algorithm could distinguish the difference.Using this algorithm to study the alpha-band and beta-band EEG signals in different sleep periods,the results also found that there are differences in the human body’s EEG signals in different bands during wakefulness and NREM-I,which further proves that the human body is in different sleep periods.There are differences in EEG signals,and the permutation entropy algorithm can be used in the study of sleep EEG staging.Second,using the multi-scale permutation entropy algorithm to study the sleep EEG of the human body in the waking period and NREM-I stage,it is found that under different scale factors,the algorithm can distinguish the human body in the waking period and NREM-I stage.Also in the study of the alpha-band and beta-band EEG signals in different sleep periods,the algorithm can also distinguish different waveband signals in different sleep periods under different scale factors,which further shows that the human body is in There are indeed differences in EEG signals in different sleep periods,and the multi-scale permutation entropy algorithm can also be used for staging studies of sleep EEG.Third,using the IOTA algorithm to study the sleep EEG of the human body during the waking period and NREM-I stage,first select multiple channels based on the physiological signals of the human body during the waking period and the NREM-I stage,and construct it in the waking period.And the functional network of NREM-I stage,and then by selecting the appropriate feature parameter average degree and clustering coefficient to analyze the functional network,it is found that the average degree and clustering coefficient of the human functional network in the NREM-I stage are greater than those in the awake period.The results show that the complexity of the functional network of the human body in the NREM-I stage is greater than that in the awake stage,and the algorithm can be used as an effective way to study sleep EEG staging.Fourth,the paper uses Spring Boot+My SQL to develop an information storage management system for the convenience of input and query of experimental results,which has functions such as system login,data addition,modification,deletion,and query.