扩展性心肌病和缺血性心肌病之间的心电图比较基于经验模式分解和变异模式分解
Yuduan Han1,2, Chonglong Ding1, Shuo Yang1
1Department of Medical Statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, China.
Bioengineering (Basel, Switzerland)
|October 25, 2024
概括
区分缺血性心肌病 (ICM) 和扩张性心肌病 (DCM) 是非常重要的. 电脑心电图信号的变化模式分解 (VMD) 提供了一种非侵入性,高度准确的方法来区分这些条件,优于传统技术.
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:
- 缺血性心肌病 (ICM) 和扩张性心肌病 (DCM) 具有临床相似之处,但在治疗和预后方面有显著差异.
- 准确的早期分化对于患者的治疗结果至关重要,但黄金标准,冠状动脉血管学是侵入性的.
- 使用先进的信号处理技术 (如变化模式分解 (VMD)) 进行心电图 (ECG) 信号分析,提供了一个潜在的非侵入性替代方案.
研究的目的:
- 调查VMD与二光谱和非线性特征提取ECG信号相结合的有效性,以区分ICM和DCM.
- 为了比较VMD与经验模式分解 (EMD) 在分类这些心脏病的性能.
主要方法:
- 87名受试者的心电图信号 (44名DCM,43名ICM) 进行了预处理,进行了无声化,并分为心跳.
- 使用EMD和VMD分解了心电图信号,通过相关性分析选择了五种模式.
- 从选定的模式中提取双光谱和非线性特征,然后使用五种机器学习模型进行分类.
主要成果:
- 提出的基于VMD的方法实现了最高的分类准确率98.30%,用于区分DCM和ICM.
- 在各种模式,心电图引线和分类算法中,VMD 始终表现出比 EMD 优越的性能.
- 这项研究验证了VMD在心电图分析中对心脏状况差异化的有效性.
结论:
- 基于VMD的ECG分析提供了一个非常准确和非侵入性的方法来区分ICM和DCM.
- 这种技术为侵入性诊断方法提供了一个有希望的替代方案,有可能改善患者管理和结果.
- VMD的优越性凸显了其作为心血管诊断中宝贵工具的潜力.
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