Study On Sleep Apnea And Its Effect On Sleep Stage

Sleep is a necessary physiological process of life,and sleep quality directly affects people’s mental state,brain thinking ability,and health.Sleep quality is mainly evaluated by analyzing the status of sleep staging.How to accurately detect sleep stages is a hot issue in sleep quality research,which is of great significance for clinical auxiliary diagnosis and treatment of sleep problems.However,sleep staging is vulnerable to sleep apnea,which disrupts the periodicity of sleep staging and affects the accuracy of sleep staging.Therefore,this paper studies sleep apnea and its impact on sleep staging,aiming at accurately assessing sleep quality,providing auxiliary functions for doctors to diagnose sleep problems,and promoting the development and application of computer technology in health care.This paper’s main contributions are as follows:1.This paper uses the Hilbert Huang transform to extract instantaneous amplitude,energy ratio,marginal spectrum,and other effective features from single-channel respiratory signals.These features are combined with time-domain features,which are input into various machine learning classifiers to detect sleep apnea automatically.The experimental results show that the related features extracted by the Hilbert Huang transform can effectively improve the detection performance of sleep apnea.2.This paper proposes a sleep apnea detection method based on multi-channel features by effectively combining attention mechanism and multi-channel features.The attention mechanism is used to recalibrate the relationship between multi-channel features,optimize feature learning,enhance the features that have a significant effect on sleep apnea detection and suppress the features that have a weak effect.The experimental results show that multi-channel feature fusion can describe the impact of different respiratory signals on sleep apnea detection in more detail and improve the accuracy of sleep apnea detection.3.Considering that the extraction process of temporal features is complex and the time granularity of extracted temporal features is incomplete in sleep staging methods without sleep apnea,a sleep staging method with a multi-level temporal context is proposed.This paper uses a temporal convolution network,weighted fusion strategy,and hidden Markov model to extract temporal features at three levels:intra-epoch,adjacent and long epochs.The complementarity of these temporal features is used to improve the performance of sleep staging.The experimental results show that the temporal features of different levels can provide useful information for sleep staging,thus improving the performance of sleep staging.4.Because sleep apnea affects the structure of sleep staging,sleep staging performance of sleep apnea with different severity is different,resulting in poor generalization ability of sleep staging methods.Therefore,a sleep staging method based on sleep stage transfer is proposed.This paper uses the multi-scale convolutional to learn the high frequency,low frequency,and global temporal features and uses bi-directional long short-term memory and conditional random field technology to learn the transfer relationship between sleep stages.By learning these features,the sleep staging model can better obtain the sleep structure of sleep apnea,which is conducive to building a personalized sleep staging method.The experimental results show that the influence of sleep apnea on sleep staging can be reduced by learning the multi-scale characteristics within sleep stages and the transfer relationship between sleep stages so that the sleep staging method has a better generalization ability.