The research of sleep staging based on single-lead electrocardiogram and deep neural network

• Published in: Biomedical engineering letters Vol. 8; no. 1; pp. 87 - 93
• Main Authors: Wei, Ran, Zhang, Xinghua, Wang, Jinhai, Dang, Xin
• Format: Journal Article
• Language: English
• Published: Korea The Korean Society of Medical and Biological Engineering 01.02.2018; Springer Nature B.V; 대한의용생체공학회
• Subjects: Artificial neural networks; Biological and Medical Physics; Biomedical Engineering and Bioengineering; Biomedicine; Biophysics; Classification; EEG; EKG; Electrocardiography; Electroencephalography; Engineering; Feature extraction; Medical and Radiation Physics; Model accuracy; Model testing; Neural networks; NREM sleep; Original; Original Article; Recording; REM sleep; Sleep; Sleep and wakefulness; 의공학; Stacked autoencoder (SAE); Sleep stage; Electrocardiogram (ECG); Deep neural network (DNN)

The polysomnogram (PSG) analysis is considered the golden standard for sleep staging under the clinical environment. The electroencephalogram (EEG) signal is the most important signal for classification of sleep stages. However, in-vivo signal recording and analysis of EEG signal presents us with a...