迈向现实世界可穿戴的嗜睡检测:在讲话时的皮质活动数据可以识别睡眠剥夺
Jihye Moon1, Andrew Peitzsch1, Youngsun Kong1
1Biomedical Engineering, University of Connecticut, Storrs, CT, 06269, USA.
Computers in biology and medicine
|November 24, 2024
概括
发言过程中的皮电活动 (EDA) 准确地检测出睡眠,单独表现优于语音分析. 这一发现支持可穿戴设备可靠的疲劳监测.
科学领域:
- 生理学监测是指对身体进行生理监测.
- 人与计算机的互动.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 准确的睡眠评估对于管理睡眠剥夺导致的疲劳风险至关重要.
- 用于检测嗜睡的语音分析因信号复杂性和不准确的标签而面临限制.
研究的目的:
- 调查电皮活动 (EDA) 在语音产生期间的疗效,以估计嗜睡.
- 为了比较基于EDA和基于语音的机器学习模型对睡眠检测的性能.
主要方法:
- 对30名参与者进行了25小时的睡眠剥夺研究.
- 每隔2小时收集一次皮电活动 (EDA) 和语音数据.
- 机器学习模型使用各种睡眠标签 (认知障碍,自我报告,清醒时间) 进行训练.
主要成果:
- 与单独的语音特征相比,语音产生的EDA特征提供了优异的嗜睡估计 (0.53相关性) 和检测 (0.85准确性).
- 在演讲期间的EDA在其他活动中表现优于EDA,例如用于检测嗜睡的视觉警.
- 机器学习模型使用EDA的清醒时间得分显示出最佳表现.
结论:
- 在发言过程中收集的皮电活动 (EDA) 是检测嗜睡的高效标志物.
- 这种方法为开发用于现实世界疲劳监测的先进可穿戴设备提供了有希望的途径.
- 在未来的设备中将EDA与语音信号集成在一起,可以提高睡眠评估的准确性.
相关概念视频
Sleep-Wake Cycles
Sleep is an essential physiological process vital to maintaining overall well-being. The reticular activating system (RAS), a network of neurons in the brainstem, regulates wakefulness and sleep. While it may seem passive, sleep consists of distinct cycles, each with its unique characteristics and functions. Two key sleep phases are non-rapid eye movement (NREM) and rapid eye movement (REM).
NREM Sleep
NREM sleep comprises four progressive stages that seamlessly merge:
NREM Sleep
NREM sleep comprises four progressive stages that seamlessly merge:
Insufficient Sleep and Sleep Deprivation
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Sleepwalking and Sleep Talking
Somnambulism, commonly known as sleepwalking, involves individuals engaging in activities ranging from simple walking to more complex behaviors such as driving. Sleepwalking typically occurs during the slow-wave sleep stages 3 and 4 early in the night when the person is not dreaming, contradicting the myth that sleepwalkers are acting out their dreams.
Factors that increase the likelihood of sleepwalking include sleep deprivation and alcohol consumption. Contrary to common beliefs, it is safe...
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