心率变化作为预测发作的工具:一个范围审查
Federico Mason1, Anna Scarabello1, Lisa Taruffi1
1Department of Biomedical and Neuromotor Sciences, University of Bologna, 40126 Bologna, Italy.
Journal of clinical medicine
|February 10, 2024
概括
心率变化 (HRV) 分析显示,在中检测发作是有前途的. 先进的HRV指标和机器学习对于准确的,实时的预测系统至关重要,改善患者的生活质量.
科学领域:
- 神经学 神经学
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
背景情况:
- 由于不可预测的发作影响生活质量,症造成了重大负担.
- 需要实时发作检测和预测系统,以改善患者 (PwE) 和护理人员的管理.
- 通过心率变化 (HRV) 评估的自主心脏控制显示了发作之前的变化.
研究的目的:
- 对基于HRV的方法进行文献范围审查,以检测或预测ictal事件.
- 确定当前HRV方法在监测中的有效性和局限性.
主要方法:
- 文学范围审查. 文学范围审查.
- 在PubMed数据库进行系统搜索,从最初的402.0中识别出72篇相关论文.
- 分析基于HRV的方法,包括传统和先进的指标,以及机器学习应用.
主要成果:
- 由于明显的自主性变化,新生儿和儿科患者的发作检测准确度更高.
- 传统的HRV指标往往不足;先进的非线性HRV功能和机器学习更有效.
- 使用HRV的可穿戴系统在具有显著自主性改变的个体中展示了对发作检测的潜力.
结论:
- HRV是发作检测的有效生物标志物,特别是在特定患者群体中.
- 先进的HRV分析和机器学习对于开发强大的实时发作预警系统至关重要.
- 对可穿戴技术的进一步研究和复杂的HRV分析可以显著有益于患者.
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