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Multimodal Sleep Stage Detection

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Using multiple modalities such as EEG+EOG, EEG+HR instead of just relying on EEG (polysomnography)

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework

Toward Interpretable Sleep Stage Classification Using Cross-Modal Transformers

Do Not Sleep on Traditional Machine Learning: Simple and Interpretable Techniques Are Competitive to Deep Learning for Sleep Scoring

Dreem Open Datasets: Multi-Scored Sleep Datasets to compare Human and Automated sleep staging

Towards More Accurate Automatic Sleep Staging via Deep Transfer Learning

Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram

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