一种基于机器学习的混合方法,使用不同的数字分辨器和DTCWTT进行ECG的节拍分类
H K Prasad Katamreddi1, Tirumala Krishna Battula1
1ECE Department, Jawaharlal Nehru Technological University Kakinada, Kakinada, Andhra Pradesh, 533003, India.
Computers in biology and medicine
|June 11, 2025
概括
本研究引入了一种先进的机器学习方法,用于分类心电图 (ECG) 节拍. 该方法增强了QRS检测,并使用新型过器和特征提取准确分类心律失常.
科学领域:
- 生物医学工程 生物医学工程
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 自动化心电图 (ECG) 分析对于诊断心脏病状况至关重要.
- 精确的心电图节拍分类有助于识别各种心律失常.
- 现有的方法可能需要改进特征提取和QRS检测.
研究的目的:
- 通过机器学习开发和评估使用心电图节拍分类的系统方法.
- 通过新的过技术,增强心电图信号特征的辨别力.
- 为了提高自动心律失常检测的准确性和稳定性.
主要方法:
- 使用双树复杂波段变换 (DTCWT) 进行手动特征提取用于心电图信号分析.
- 应用四种新型数字波器用于ECG信号的分化和增强QRS复杂检测.
- 整合DTCWT衍生的形态特征与统计特征的综合性特征集.
- 在MIT-BIH心律失常数据库上对各种机器学习分类器进行培训和评估.
主要成果:
- 拟议的方法证明了在将心电图节拍分为六个不同的类别时的高准确性.
- 通过将新型数字分辨器与Pan-Tompkins算法集成来实现增强的QRS检测.
- 综合功能集提高了机器学习分类器用于心律失常识别的性能.
- 在完整的MIT-BIH心律失常数据库上的实验验证证证了该方法的稳定性.
结论:
- 开发的机器学习方法为自动ECG节拍分类提供了有效的解决方案.
- DTCWT,新型过器和统计特征的组合显著提升了ECG信号处理.
- 这项研究有助于为临床应用提供更精确,更可靠的心脏信号自动分析.
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