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

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, un...