时间域心率变化特征用于自动充血性心力衰竭预测
Jeban Chandir Moses1, Sasan Adibi1, Maia Angelova1,2
1School of Information Technology, Deakin University, Burwood, VIC, 3125, Australia.
ESC heart failure
|November 27, 2023
概括
使用机器学习的心率变化 (HRV) 分析可以准确检测严重的充血性心力衰竭. 这种非侵入性方法在初级保健机构的早期查方面显示出有前途.
科学领域:
- 心脏病学 心脏病学
- 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:
- 由于有限的诊断工具和重叠的症状,心力衰竭通常在初级保健中被诊断为晚期.
- 对心率变化 (HRV) 的非侵入性监测为早期检测提供了一个潜在的解决方案.
- HRV反映了自主神经系统的活动,对心血管健康评估至关重要.
研究的目的:
- 评估在HRV数据上使用机器学习 (ML) 的可行性,以对健康个人和心力衰竭患者进行分类.
- 探索HRV作为心力衰竭检测的非侵入性生物标志物.
主要方法:
- 利用了54名健康成年人和44名心力衰竭患者 (NYHA 1-3类) 的数字化心电图记录.
- 进行时间域HRV分析,计算RMSSD,SDNN,SDSD,NN50,PNN50和mRRi等参数.
- 应用的ML算法 (SVM,KNN,Naïve Bayes,DT) 具有五倍的交叉验证,以根据HRV参数对受试者进行分类.
主要成果:
- 最好的整体分类准确率是77%使用KNN和DT与5分钟HRV窗口.
- 在分类严重充血性心力衰竭方面,KNN和DT取得了高准确性 (91%).
- 对KNN和DT的曲线下的面积 (AUC) 值总体达到0.77和0.78,严重病例分别为0.88和0.92.
结论:
- HRV分析与ML结合,可以准确预测严重的充血性心力衰竭.
- 这种使用HRV的非侵入性方法显示了对心力衰竭的初级护理查的潜力.
- 进一步的研究可以将ML驱动的HRV分析纳入常规临床实践,以早期检测心力衰竭.
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