Scholarlink

<p>Sleep disorders affect millions globally, leading to serious health issues. Accurate sleep-wake classification is essential for diagnosis and management. While polysomnography is the gold standard, it is costly and invasive; photoplethysmography (PPG) offers a viable alternative. Using the Cyclic Alternating Pattern Sleep Database (84 participants, 85,542 epochs), we extracted 330 features and reduced dimensionality via statistical tests and the SelectFromModel method. To address class imbalance, we applied Adaptive Synthetic (ADASYN) sampling. A Random Forest model, validated with 20-fold cross-validation on the unbalanced dataset (75 features), achieved an F1 score of 89.05% but struggled with wake detection. With ADASYN balancing and 35 features, it achieved 88.57% sensitivity (sleep) and 71.31% specificity (wake), with an F1 score of 81.40%. This feature-based approach improves PPG-based sleep classification, supporting clinical adoption and integration into wearable devices for remote sleep monitoring.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Interpretable feature-based machine learning for automatic sleep detection using photoplethysmography

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年6月2日

查看全文
  • Karmen Markov,
  • Mohamed Elgendi,
  • Vera Birrer,
  • Carlo Menon

摘要

Sleep disorders affect millions globally, leading to serious health issues. Accurate sleep-wake classification is essential for diagnosis and management. While polysomnography is the gold standard, it is costly and invasive; photoplethysmography (PPG) offers a viable alternative. Using the Cyclic Alternating Pattern Sleep Database (84 participants, 85,542 epochs), we extracted 330 features and reduced dimensionality via statistical tests and the SelectFromModel method. To address class imbalance, we applied Adaptive Synthetic (ADASYN) sampling. A Random Forest model, validated with 20-fold cross-validation on the unbalanced dataset (75 features), achieved an F1 score of 89.05% but struggled with wake detection. With ADASYN balancing and 35 features, it achieved 88.57% sensitivity (sleep) and 71.31% specificity (wake), with an F1 score of 81.40%. This feature-based approach improves PPG-based sleep classification, supporting clinical adoption and integration into wearable devices for remote sleep monitoring.