使用传统和整体机器学习算法进行基于RR间隔的心房动检测
S K Shrikanth Rao1, Roshan Joy Martis2
1Department of Electronics and Communication Engineering, Vivekananda College of Engineering and Technology, Puttur, Karnataka, India.
Journal of medical signals and sensors
|August 25, 2023
概括
早期发现心房动 (AF) 是至关重要的. 这项研究发现,集体机器学习,特别是随机森林分类器,在从心电图数据中检测AF时达到99.10%的准确性.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 医疗保健中的机器学习
背景情况:
- 心房动 (AF) 是一种严重的疾病,与中风和心力衰竭有关.
- 早期发现AF对于降低死亡率和发病率至关重要.
- 现有的方法需要与先进的机器学习技术进行比较.
研究的目的:
- 为早期AF检测提出和比较传统的机器学习 (TML) 和整体机器学习 (EML) 算法.
- 使用心电图数据评估各种TML和EML分类器的性能.
- 为了确定准确的AF分类最有效的方法.
主要方法:
- 从心电图 (ECG) 数据中提取RR间隔特征.
- 用TML (例如,SVM,KNN) 和EML (例如,随机森林,旋转森林) 算法将心律分类为正常,AF和其他类别.
- 使用PhysioNet挑战2017数据集进行评估,并进行十倍交叉验证.
主要成果:
- 随机森林 (RF) 分类器实现了高分类准确率99.10%.
- 射频分类器的曲线下的面积 (AUC) 为0.998,表明了出色的性能.
- 与TML技术相比,EML方法,特别是射频,表现出更高的性能.
结论:
- 集体学习,特别是随机森林算法,为准确的AF检测提供了一种高度有效的方法.
- 拟议的方法为医疗保健管理系统提供了一个强大的工具,包括起器和除器.
- 这项研究验证了EML对TML的优越性,用于从心电图信号中可靠地识别AF.
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