用短段PPG信号融合提升可穿戴设备睡眠分期准确率
Optimizing Photoplethysmography-Based Sleep Staging Models by Leveraging Temporal Context for Wearable Devices Applications
- 将30秒PPG片段拼接成15分钟长序列，利用时间上下文信息
- 实现75%准确率、0.60的科恩卡帕值，优于单段30秒方法
- 适合资源受限的可穿戴设备，尤其改善深睡和快速眼动期识别
精准的睡眠分期对睡眠障碍诊断和睡眠质量评估至关重要。尽管多导睡眠图（PSG）仍是金标准，但光体积变化描记法（PPG）因成本低且广泛应用于可穿戴设备而更具实用性。然而，现有先进睡眠分期方法通常需要长时间连续信号采集，导致能耗过高，不适合可穿戴设备。较短信号采集虽更可行，但准确性下降。本文基于顶尖方法改进睡眠分期模型，评估不同PPG片段长度下的性能表现。通过将30秒的PPG片段在15分钟内拼接，以利用更长的时间上下文。该方法实现了75%的准确率、0.60的科恩卡帕系数、0.74的加权F1分数和0.60的宏平均F1分数。尽管减小片段尺寸会降低深睡和快速眼动期的敏感性，但本策略仍显著优于单一30秒窗口方法，尤其在这些阶段表现更优。
原文摘要 · Abstract (English)
Accurate sleep stage classification is crucial for diagnosing sleep disorders and evaluating sleep quality. While polysomnography (PSG) remains the gold standard, photoplethysmography (PPG) is more practical due to its affordability and widespread use in wearable devices. However, state-of-the-art sleep staging methods often require prolonged continuous signal acquisition, making them impractical for wearable devices due to high energy consumption. Shorter signal acquisitions are more feasible but less accurate. Our work proposes an adapted sleep staging model based on top-performing state-of-the-art methods and evaluates its performance with different PPG segment sizes. We concatenate 30-second PPG segments over 15-minute intervals to leverage longer segment contexts. This approach achieved an accuracy of 0.75, a Cohen's Kappa of 0.60, an F1-Weighted score of 0.74, and an F1-Macro score of 0.60. Although reducing segment size decreased sensitivity for deep and REM stages, our strategy outperformed single 30-second window methods, particularly for these stages.
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