使用每日可穿戴产生的生理数据识别抑郁症
Xinyu Shui1, Hao Xu2, Shuping Tan2
1Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China.
Sensors (Basel, Switzerland)
|January 25, 2025
概括
穿戴式传感器可以通过分析生理数据,如脉冲波和皮肤导电性来客观地识别抑郁症. 这项技术对早期检测和监测日常生活中的抑郁症状充满希望.
科学领域:
- 精神病学和数字健康
背景情况:
- 对抑郁症的客观识别对于有效的心理健康护理至关重要.
- 可穿戴设备提供持续的,不引人注目的生理监测,用于检测暗示抑郁状态的微妙变化.
研究的目的:
- 通过在日常活动中从腕带设备收集的多式生理数据来识别患有抑郁症的个人.
- 为了评估可穿戴设备衍生的生生理学数据的分类准确性,用于在不同时间段识别抑郁症.
主要方法:
- 收集了58名患有抑郁症的参与者和58名使用腕带匹配的健康对照者的多式生理数据 (脉冲波,皮肤导电,三轴加速).
- 从生理信号中提取静态和时间动态特征.
- 采用随机森林算法来分类抑郁和非抑郁的个体.
主要成果:
- 实现了分类准确率为90.0% (6小时),84.7% (2小时),80.1% (30分钟) 和76.0% (5分钟) 区分抑郁症个体.
- 证明了使用每日可穿戴设备衍生的生生理学数据用于抑郁症识别的可行性.
结论:
- 与生理数据分析集成的可穿戴技术是抑郁症识别的可行方法.
- 这种方法具有早期发现和监测抑郁症状的潜力,未来可用于个性化干预和实时心理健康护理.
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