山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (4): 38-44.doi: 10.6040/j.issn.1672-3961.0.2021.306
• • 上一篇
袁高腾,周晓峰*,郭宏乐
YUAN Gaoteng, ZHOU Xiaofeng*, GUO Hongle
摘要： 为了提高不同类别心电图(Electrocardiogram,ECG)信号的识别精度,使用小波分析提取心电信号特征,并使用分段距离的特征筛选方法对特征进行筛选排序,去除冗余和干扰特征,挑选出关键特征。通过缩减特征数量,提高分类的精度和效率。结合机器学习分类器对特征进行分类,比较分类效果。结果显示,在MIT-BIH数据集上,本方法的分类精度比不使用特征选择分类精度高0.22%,分类精度最高达到99.67%。试验证明本研究提出的模型能够区分4种常见的ECG信号,较传统方法优势明显。
中图分类号:
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