手腕智能手表和预测发作模型
Waroth Pipatpratarnporn1, Wichuta Muangthong1, Suda Jirasakuldej2
1Division of Neurology, Department of Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
Epilepsia
|July 28, 2023
概括
手腕智能手表可以检测心率和加速等脑外生物信号,以区分发作与正常活动. 预测模型对发作检测设备有希望,尽管需要进一步验证.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 医疗器械 医疗器械
背景情况:
- 的监测依赖于视频电脑电图 (EEG),这是资源密集的.
- 开发用于发作检测的可穿戴技术可以改善患者监测和管理.
- 脑外生物信号提供了一种非侵入性的方法来识别发作事件.
研究的目的:
- 与基线活动相比,在各种发作类型中表征脑外生物信号.
- 开发和评估用于使用多式联动可穿戴传感器检测整体和特定发作类型的预测模型.
- 评估这些预测模型的诊断性能.
主要方法:
- 预期在监测单位招募焦点患者.
- 同时长期视频EEG和Empatica E4可穿戴设备监控.
- 在和基线期间分析生物信号 (心率,加速,电皮活动).
- 使用一般化估计方程开发预测模型.
主要成果:
- 心率 (HR),加速 (ACC) 和皮电活动 (EDA) 在发作期间显著升高.
- 在双边强力克隆发作 (BTC) 期间,HR和ACC的升高幅度最大.
- HR和ACC是整体扣押,BTC和非BTC的独立预测指标;HR预测了孤立的光环.
- 对整体发作的预测模型实现了77.78%的灵敏度和60%的特异性 (AUC .696).
结论:
- 来自手腕穿戴设备的多式生物信号可以区分发作和正常活动.
- 开发的预测算法显示了集成到商业检测设备的潜力.
- 需要进行更大规模的研究来对预测模型进行外部验证.
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