心率变化是否可以识别即将发作的高风险状态?
Louis Cousyn1, Fedele Dono2, Vincent Navarro1
1Paris Brain Institute (Inserm, CNRS, Sorbonne Université), Paris, France; AP-HP, Department of Neurology, Epilepsy Unit, Pitié-Salpêtrière Hospital, Paris, France.
Epilepsy research
|October 2, 2023
概括
每日休息心率变化 (HRV) 可以预测患者即将发生的发作. 这种可访问的方法显示了可穿戴预测设备的前景,尽管需要进一步改进.
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:
- 心率变化 (HRV) 量化了自主神经系统平衡,对于理解生理状态至关重要.
- 自主功能障碍与发作之前的前性发作状态有关.
- 之前的研究探讨了HRV用于使用连续心电图检测前冲动状态.
研究的目的:
- 评估每日静止状态HRV指标在预测发作风险方面的有效性.
- 用HRV数据评估机器学习模型的预测性能.
- 确定将HRV分析集成到门诊预测系统中的可行性.
主要方法:
- 在15名患者的每日休息期间收集了10分钟的警控制的心电图记录.
- 利用机器学习算法来分析156个心电图记录中的HRV特征.
- 执行伪预测的每日预测,以估计发作风险.
主要成果:
- HRV特征识别了前冲动状态,曲线下面面积 (AUC) 的中位数为0.75.
- 每天的预测实现了0.3.3的平均布赖尔分数.
- 成功预测了大约60%的前触点日,在间触点日有24%的错误阳性率.
结论:
- 每天休息HRV捕捉自主变化,表明一个前发作状态.
- 基于HRV的预测显示出 ambulatory发作预测设备的潜力.
- 将HRV与EEG或前兆症状等其他模式结合起来,可以提高预测准确度.
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