用可穿戴设备定制抗发作治疗:在没有的情况下进行概念验证研究
Jaiver Macea1, Christos Chatzichristos2, Miguel Bhagubai2
1Laboratory for Epilepsy Research, Department of Neurosciences, Leuven Brain Institute, KU Leuven, Leuven, Belgium.
Epilepsia
|March 21, 2025
概括
在家用可穿戴电脑电图 (wEEG) 监测改善了抗发作药物调整后患者的缺席发作管理. 机器学习 (ML) 管道显著减少了神经病学家的数据审查时间.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
背景情况:
- 典型的缺席发作经常被低估,导致患者的护理不够理想.
- 当前的监控方法可能无法在现实环境中完全捕捉发作活动.
研究的目的:
- 通过在家进行可穿戴电脑电图 (wEEG) 监测,增强典型缺席的患者护理.
- 评估一条机器学习 (ML) 管道,用于使用wEEG数据自动检测缺席.
主要方法:
- 在抗发作药物 (ASM) 调整后,典型的缺席发作患者每周使用wEEG设备12-24小时.
- 长达3秒以上的三赫兹通用尖峰波放电 (SWD) 被确定为缺席的替代物.
- 开发了一个机器学习 (ML) 管道,并将其与手动检查进行比较,以检查时间和诊断性能.
主要成果:
- 在19名患者中,有15名 (79%) 实现了无发作,其中包括7名耐性患者.
- ML管道将24小时的wEEG记录的神经科医生审查时间从27分钟的中位数减少到4.3分钟.
- ML管道表现出高诊断性能,灵敏度为0.8,精度为0.95,F1得分为0.87.
结论:
- 基于家庭的wEEG监测在ASM调整后有效地管理缺席发作.
- 基于ML的管道显著提高了分析wEEG数据的效率和准确性,以检测发作.
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