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
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2020.2979168

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 classify sleep stages, it is time-consuming and exhibits inter-rater variability. Therefore, this article proposes a long short-term memory (LSTM) model for automatic sleep stage scoring using a polyvinylidene fluoride (PVDF) film sensor that can provide unconstrained long-term physiological monitoring. Signals were recorded using a PVDF sensor during PSG. From 60 recordings, 30 were used for training, 10 for validation, and 20 for testing. Sixteen parameters, including movement, respiration-related, and heart rate variability, were extracted from the recorded signals and then normalized. From the selected LSTM architecture, four sleep stage classification performances were evaluated for a test dataset and the results were compared with those of conventional machine learning methods. According to epoch-by-epoch (30 s) analysis, the classification performance for the four sleep stages had an average accuracy of 73.9% and a Cohen's kappa coefficient of 0.55. When compared with other machine learning methods, the proposed method achieved the highest classification performance. The use of LSTM networks with the PVDF film sensor has potential for facilitating automatic sleep scoring, and it can be applied for long-term sleep monitoring at home.