使用连接的衫检测焦点到双边的强力克隆性发作
Oumayma Gharbi1,2, Yassine Lamrani1,2, Jérôme St-Jean1,2
1Department of Neuroscience, Université de Montréal, Montréal, Quebec, Canada.
Epilepsia
|May 23, 2024
概括
一个新的连接衫使用机器学习准确地检测焦点到双边强力-克隆发作 (FBTCS). 这种可穿戴技术对低误报率的监测有希望.
科学领域:
- 生物医学工程 生物医学工程
- 神经学 神经学
- 医疗保健中的机器学习
背景情况:
- 焦点到双边性-克隆性 (FBTCS) 构成重大风险,需要为患者的安全和管理进行准确的检测.
- 目前的发作检测方法往往缺乏持续监测能力或是侵入性的.
- 可穿戴技术为非侵入性,实时生理信号监测提供了一个有前途的途径.
研究的目的:
- 开发和评估用于FBTCS检测的机器学习算法.
- 为了利用一种新的多式联网衫来捕获生理数据.
- 用关键指标来评估算法的性能,例如灵敏度和错误报警率.
主要方法:
- 在监测单位招募患者的前景.
- 同时记录心电图 (ECG) 和加速度计 (ACC) 信号,同时使用连接的衫进行视频脑电图监测.
- 从心电图和ACC信号中提取特征,然后训练FBTCS检测的极端梯度增强 (XGBoost) 算法.
- 用于绩效评估的患者智能嵌套交叉验证.
主要成果:
- 在8067小时的监测中,XGBoost算法在检测42名患者的66个FBTCS时获得了84.8%的灵敏度.
- 平均虚假警报率 (FAR) 为0.55/24h,虚假警报时间 (TiW) 为10s/alarm.
- 在曲线下的接收器运行特征面积 (ROC-AUC) 为0.90,中位检测延迟为25.5秒.
结论:
- 连接的衫可以在临床环境中准确地检测FBTCS,具有较低的错误报警率.
- 开发的机器学习算法在发作检测方面表现出高性能.
- 需要在住宅环境中进行进一步的前性研究,以验证实时性能和可用性.
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