基于临床信息和患者儿童的短期自主活动记录的多变量发作概率评估的开发
Solveig Vieluf1, Sarah Cantley2, Michele Jackson2
1Division of Epilepsy and Clinical Neurophysiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts; Institute of Sports Medicine, Paderborn University, Paderborn, Germany.
Pediatric neurology
|September 13, 2023
概括
使用可穿戴传感器和临床数据,可以预测发作. 这项研究表明,可穿戴数据与临床信息相结合,可以帮助评估患者的发作概率.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 预测每天发作的可能性有助于安排视频电脑电图 (EEG) 监测.
- 穿戴式传感器数据与临床数据相结合,为发作风险评估提供了一种实用的方法.
- 这项研究代表了发展预测能力的基本步骤.
研究的目的:
- 根据下列记录期间的发作发生情况对儿科监测单位的患者进行分类.
- 评估短期可穿戴记录和临床数据对预测即将发生的发作的有用性.
主要方法:
- 使用可穿戴传感器记录心率 (HR),心率变化 (HRV),皮电活动 (EDA) 和儿科患者的外周体温.
- 收集的临床数据包括人口统计,史,EEG发现,MRI结果和药物状态.
- 采用传统的机器学习技术与交叉验证,以区分有和没有的患者.
主要成果:
- 包括139名患者 (61名有发作,78名没有).
- 在即将发作的患者中发现更低的HR和EDA,以及更高的HRV (P < 0.01).
- 实现了66%的平均分类准确度和接收器操作特征曲线下的0.72面积.
结论:
- 短期可穿戴记录与临床数据相结合,显示了对发作概率评估的重大潜力.
- 这种方法为临床医生提供了一种实用且易于使用的工具.
- 进一步发展可能会导致先进的扣押预测系统.
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