在飞行中的睡眠评分算法与心率,RR间隔和加速计作为输入
Michele Guagnano1, Sara Groppo1, Luigi Pugliese2
1Department of Control and Computer Engineering, Politecnico di Torino, 10129 Turin, Italy.
Sensors (Basel, Switzerland)
|April 12, 2025
概括
这项研究开发了一种使用智能手表数据 (心率,RR间隔,加速度计) 的即时睡眠评分算法,用于实时识别睡眠阶段. 该算法在睡眠-清醒识别中实现了88.46%的准确性,从而实现了诸如自动驾驶安全等潜在应用.
科学领域:
- 生物医学工程 生物医学工程
- 可穿戴技术可穿戴技术
- 睡眠科学 睡眠科学
背景情况:
- 实时睡眠阶段识别对于诸如自动驾驶安全系统等应用至关重要.
- 目前的方法通常依赖于侵入性脑电图 (EEG) 或专用设备.
- 需要非侵入性,可访问和准确的睡眠评分解决方案.
研究的目的:
- 利用智能手表的数据开发和评估一款飞行中的睡眠评分算法.
- 评估算法在识别不同睡眠阶段 (轻,深,REM) 和清醒状态方面的准确性.
- 为了证明使用广泛可用的可穿戴设备进行睡眠分析的可行性.
主要方法:
- 使用智能手表的心率 (HR),RR间隔和加速度计数据开发了一个即时算法.
- 参与者戴着商业智能手表和一个聚睡眠学 (PSG) 设备进行地面真相验证.
- 该算法的性能与基于PSG的睡眠评分进行了测试.
主要成果:
- 该算法在睡眠-清醒识别中实现了88.46%的准确性,91.42%的精度和93.52%的灵敏度.
- 具体睡眠阶段的准确度是:深度睡眠 (69.38%),轻度睡眠 (50.62%),REM睡眠 (62.02%) 和清醒 (73.48%).
- 开发的算法显示了对非侵入性,实时睡眠监测的承诺.
结论:
- 智能手表数据可以有效地用于飞行中的睡眠阶段分类.
- 该算法为开发可访问的睡眠监测工具提供了基础.
- 这项技术在健康监测和安全关键系统中具有潜在的应用.
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