使用基于模糊的特征选择和最佳分类器的电心电图形态律乱症分类
Krishnakant Chaubey1, Seemanti Saha1
1Department of Electronics & Communication Engineering, National Institute of Technology Patna, Ashok Raj Path, Patna, 800005, Bihar, India.
Biomedical physics & engineering express
|August 21, 2023
概括
这项研究引入了一种高效的算法,用于从心电图信号分类七种类型的心律不整. 拟议的方法实现了高精度,超过了可靠的心律监测的现有技术.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 信号处理 信号处理
背景情况:
- 心律失常对全球死亡率有很大影响.
- 准确检测心律失常需要持续的心电图监测和先进的分析.
- 计算机辅助算法对于解释复杂的心电图数据至关重要.
研究的目的:
- 为心电图信号开发和验证一种新的形态失常症分类算法.
- 为了提高分类准确性,识别和排列重要的特征.
- 为了比较不同机器学习分类器的ECG节拍分类的性能.
主要方法:
- 从心电图节拍中提取了25个属性的新型特征集.
- 基于模糊值的特征选择 (FEBFS) 用于排名和选择特征.
- 使用了带有辐射基函数 (SVM-RBF) 的支持向量机和加权K-近邻 (WKNN) 分类器.
- 在MIT-BIH心律失常数据库上使用10倍交叉验证来评估性能.
主要成果:
- WKNN分类器,K=3和城市街区距离,实现了最高的准确性.
- 获得的平均灵敏度 = 94.89%,正预测性 = 97.13%,特异性 = 99.72%,F1评分 = 95.95%,整体准确度 = 99.15%.
- 拟议的算法与现有的最先进的方法相比,显示出更高的性能.
结论:
- 开发的算法,利用独特的特征集和FEBFS,是高效和可靠的形态心律失常的分类.
- 这些发现突显了先进的信号处理和机器学习在改善心律失常检测方面的潜力.
- 这项工作为临床环境中的节拍心电图分析提供了强大的解决方案.
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Types of Arrhythmias
Sinus Node Arrhythmias
Sinus Bradycardia: Originating from the sinoatrial (SA) node, sinus bradycardia involves slower impulses, resulting in a heart rate of less than 60 beats per minute (bpm). Causes include sleep, vagal stimulation, beta-blockers, hypothyroidism,...
253