使用CWT和DCNN进行心血管疾病的心电图信号分类
Tawfikur Rahman1, Rasel Ahommed1, Nibedita Deb2
1Department of Electrical and Electronic Engineering, Faculty of Engineering, International University of Business Agriculture and Technology, Uttara, Dhaka 1230, Bangladesh.
Journal of biomedical physics & engineering
|February 20, 2025
概括
使用连续波形变换 (CWT) 和深度卷积神经网络 (DCNN) 的自动化心电图 (ECG) 分析显著改善了心血管疾病的检测. 这种方法实现了高精度,减少了错误诊断,提高了临床环境中的效率.
科学领域:
- 医疗技术 医疗技术 医学技术
- 医疗保健中的人工智能
- 信号处理 信号处理
背景情况:
- 心血管疾病 (CVD) 诊断依赖于心电图 (ECG),但手动分析耗时且容易出现错误.
- 自动诊断工具对于提高心血管疾病检测的准确性和效率至关重要.
研究的目的:
- 开发一种使用连续波形变换 (CWT) 和深度卷积神经网络 (DCNN) 的自动化心电图分类系统.
- 为了将 1D ECG 信号转换为 2D 谱图,以进行增强的 DCNN 分析.
- 准确检测多个ECG异常,表明心血管疾病.
主要方法:
- 使用CWT将1D心电图信号转换为2D谱图.
- 在各种ECG数据集上训练了一个DCNN模型,包括PhysioNet和MIT-BIH心律失常数据集.
- 使用精度,特异性,回忆,整体准确性和F1得分来评估模型.
主要成果:
- 实现了正常信号的100%精度和100%的平均特异性.
- 显示平均回忆率为97.65%,平均F1得分为98.81%.
- 在检测心血管疾病相关异常方面达到98.67%的整体平均准确度.
结论:
- 综合CWT-DCNN方法显著提高了ECG分类的准确性和能力.
- 这种自动化方法可以减少初级保健的诊断错误,并提高医疗机构的效率.
- 该算法为改善心血管疾病检测和医疗保健实践提供了有价值的工具.
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