Automatic Sleep Stage Classification Using Nasal Pressure Decoding Based on a Multi-Kernel Convolutional BiLSTM Network

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; pp. 2533 - 2544
• Main Authors: Lee, Minji, Kang, Hyeokmook, Yu, Seong-Hyun, Cho, Heeseung, Oh, Junhyoung, van der Lande, Glenn, Gosseries, Olivia, Jeong, Ji-Hoon
• Format: Journal Article Web Resource
• Language: English
• Published: United States IEEE 2024; Institute of Electrical and Electronics Engineers Inc
• Subjects: Accuracy; Adult; Algorithms; biomedical application; Biomedical applications; Biomedical Engineering; Brain modeling; Deep Learning; Electroencephalography; F1 scores; Feature extraction; Female; healthcare; Healthy Volunteers; Humans; Internal Medicine; Kappa values; Male; Multi-kernel; nasal pressure; Neural Networks, Computer; Neuroscience (all); Neurosciences & behavior; Neurosciences & comportement; Nose; Nose - physiology; Polysomnography; Pressure; Rapid eye movement; Rehabilitation; Sciences sociales & comportementales, psychologie; Sleep; Sleep stage classification; Sleep Stages; Sleep Stages - physiology; Sleep stages classifications; Sleep, REM; Sleep, REM - physiology; Social & behavioral sciences, psychology; Wakefulness; Wakefulness - physiology; Young Adult
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2024.3420715

Sleep quality is an essential parameter of a healthy human life, while sleep disorders such as sleep apnea are abundant. In the investigation of sleep and its malfunction, the gold-standard is polysomnography, which utilizes an extensive range of variables for sleep stage classification. However, undergoing full polysomnography, which requires many sensors that are directly connected to the heaviness of the setup and the discomfort of sleep, brings a significant burden. In this study, sleep stage classification was performed using the single dimension of nasal pressure, dramatically decreasing the complexity of the process. In turn, such improvements could increase the much needed clinical applicability. Specifically, we propose a deep learning structure consisting of multi-kernel convolutional neural networks and bidirectional long short-term memory for sleep stage classification. Sleep stages of 25 healthy subjects were classified into 3-class (wake, rapid eye movement (REM), and non-REM) and 4-class (wake, REM, light, and deep sleep) based on nasal pressure. Following a leave-one-subject-out cross-validation, in the 3-class the accuracy was 0.704, the F1-score was 0.490, and the kappa value was 0.283 for the overall metrics. In the 4-class, the accuracy was 0.604, the F1-score was 0.349, and the kappa value was 0.217 for the overall metrics. This was higher than the four comparative models, including the class-wise F1-score. This result demonstrates the possibility of a sleep stage classification model only using easily applicable and highly practical nasal pressure recordings. This is also likely to be used with interventions that could help treat sleep-related diseases.