Long Short-Term Memory Networks for Unconstrained Sleep Stage Classification Using Polyvinylidene Fluoride Film Sensor

• Published in: IEEE journal of biomedical and health informatics Vol. 24; no. 12; pp. 3606 - 3615
• Main Authors: Choi, Sang Ho, Kwon, Hyun Bin, Jin, Hyung Won, Yoon, Heenam, Lee, Mi Hyun, Lee, Yu Jin, Park, Kwang Suk
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.12.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adolescent; Adult; balli-stocardiogram; Brain modeling; Classification; Data mining; Electroencephalography; Female; Fluorides; Heart rate; Humans; IIR filters; Learning algorithms; Long short-term memory; Machine Learning; Male; Middle Aged; Monitoring; Neural Networks, Computer; Polysomnography - instrumentation; Polysomnography - methods; polyvinylidene fluoride; Polyvinylidene fluorides; Polyvinyls - chemistry; Retrospective Studies; Sensors; Signal monitoring; Signal Processing, Computer-Assisted; Sleep; sleep stage classification; Sleep Stages - physiology; unconstrained; Young Adult

Sleep stage scoring is the first step towards quantitative analysis of sleep using polysomnography (PSG) recordings. However, although PSG is a gold standard method for assessing sleep, it is obtrusive and difficult to apply for long-term sleep monitoring. Further, because human experts manually cla...