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SleePyCo: Automatic sleep scoring with feature pyramid and contrastive learning by Seongju Lee; Yeonguk Yu; Seunghyeok Back; Hogeon Seo; Kyoobin Lee is a Computer Science article available to read on EtoBox.

What is SleePyCo: Automatic sleep scoring with feature pyramid and contrastive learning about?

Automatic sleep scoring is essential for the diagnosis and treatment of sleep disorders and enables longitudinal sleep tracking in home environments. Conventionally, learning-based automatic sleep scoring on singlechannel electroencephalogram (EEG) is actively studied because obtaining multi-channel signals during sleep is difficult. However, learning representation from raw EEG signals is challenging owing to the following issues: (1) sleep-related EEG patterns occur on different temporal and frequency scales and (2) sleep stages share similar EEG patterns. To address these issues, we propose an automatic Sleep scoring framework that incorporates (1) a feature Pyramid and (2) supervised Contrastive learning, named SleePyCo. For the feature pyramid, we propose a backbone network named SleePyCo-backbone to consider multiple feature sequences on different temporal and frequency scales. Supervised contrastive learning allows the network to extract class discriminative features by minimizing the distance between intra-class features and simultaneously maximizing that between inter-class features. Comparative analyses on four public datasets demonstrate that SleePyCo consistently outper

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Author

Seongju Lee; Yeonguk Yu; Seunghyeok Back; Hogeon Seo; Kyoobin Lee

Publisher

Elsevier BV

Published

2024

Language

EN

Field

Computer Science (Physical Sciences)