A Novel Continuous Sleep State Artificial Neural Network Model Based on Multi-Feature Fusion of Polysomnographic Data

Abstract

Jian Cui,1 Yunliang Sun,2 Haifeng Jing,3 Qiang Chen,1 Zhihao Huang,1 Xin Qi,1 Hao Cui1 1Department of Big Data and Fundamental Sciences, Shandong Institute of Petroleum and Chemical Technology, Dongying, Shandong, 257061, People’s Republic of China; 2Department of Respiratory and Sleep Medicine, Bin Zhou Medical University Hospital, Binzhou, Shandong, 256600, People’s Republic of China; 3College of Software and Microelectronics, Peking University, Beijing, 100000, People’s Republic of ChinaCorrespondence: Jian Cui, Department of Big Data and Fundamental Sciences, Shandong Institute of Petroleum and Chemical Technology, 271 Bei Er Lu, Dongying City, Shandong Province, 257061, People’s Republic of China, Tel [phone], Email [email protected]: Sleep structure is crucial in sleep research, characterized by its dynamic nature and temporal progression. Traditional 30-second epochs falter in capturing the intricate subtleties of various micro-sleep states. This paper introduces an innovative artificial neural network model to generate continuous sleep depth value (SDV), utilizing a novel multi-feature fusion approach with EEG data, seamlessly integrating temporal consistency.Methods: The study involved 50 normal and 100 obstructive sleep apnea–hypopnea syndrome (OSAHS) participants. After segmenting the sleep data into 3-second intervals, a diverse array of 38 feature values were meticulously extracted, including power, spectrum entropy, frequency band duration and so on. The ensemble random forest model calculated the timing fitness value for all the features, from which the top 7 time-correlated features were selected to create detailed sleep sample values ranging from 0 to 1. Subsequently, an artificial neural network (ANN) model was trained to delineate sleep continuity details, unravel concealed patterns, and far surpassed the traditional 5-stage categorization (W, N1, N2, N3, and REM).Results: The SDV changes from wakeful stage (mean 0.7021, standard deviation 0.2702) to stage N3 (mean 0.0396, standard deviation 0.0969). During the arousal epochs, the SDV increases from the range (0.1 to 0.3) to the range around 0.7, and decreases below 0.3. When in the deep sleep (≤ 0.1), the probability of arousal of normal individuals is less than 10%, while the average arousal probability of OSA patients is close to 30%.Conclusion: A sleep continuity model is proposed based on multi-feature fusion, which generates SDV ranging from 0 to 1 (representing deep sleep to wakefulness). It can capture the nuances of the traditional five stages and subtle differences in microstates of sleep, considered as a complement or even an alternative to traditional sleep analysis.Keywords: sleep depth value, sleep continuity, EEG features, timing fitness, ANN model

Keywords

• sleep depth value
• sleep continuity
• eeg features
• timing fitness
• ann model