一个有效和快速的模型来表征心律失常和充血性心力衰竭
Salim Lahmiri1, Stelios Bekiros2
1Department of Supply Chain and Business Technology Management, John Molson School of Business, Concordia University, Montreal, QC H3H 0A1, Canada.
Diagnostics (Basel, Switzerland)
|April 12, 2025
概括
一个新的系统使用离散等弦变换 (DCT) 的更高的振幅系数 (HAC) 来从心电图中准确检测心律失常 (ARR) 和充血性心力衰竭 (CHF). 这种计算机辅助诊断 (CAD) 工具对于临床使用来说是快速有效的.
科学领域:
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
- 信号处理 信号处理
背景情况:
- 心律不整 (ARR) 和充血性心力衰竭 (CHF) 是具有潜在致命后果的严重心脏疾病.
- 早期和准确检测ARR和CHF对于有效的患者管理和改善结果至关重要.
研究的目的:
- 开发一个快速而准确的自动检测系统,以区分正常鼻 (NS) 节律和ARR.
- 开发一个快速准确的自动检测系统,以区分NS节奏和CHF.
主要方法:
- 作为关键特征,利用了来自心电图 (ECG) 信号的离散等比变换 (DCT) 的更高幅度系数 (HAC).
- 采用统计分类器,特别是k-最近邻近 (k-NN) 算法,用于信号歧视.
- 使用十倍交叉验证评估系统性能.
主要成果:
- DCT有效地压缩了心电图信号,而衍生的HAC显示了正常鼻 (NS) 和异常节奏 (ARR,CHF) 之间的显著差异.
- k-NN分类器实现了高精度:97%的ARR与NS (99%的灵敏度,90%的特异性,0.63s处理时间) 和99%的CHF与NS (99.7%的灵敏度,99.2%的特异性,0.27s处理时间).
- 与最近的相关工作相比,DCT-kNN系统表现出卓越的准确性和速度.
结论:
- 基于DCT的HAC作为有效的生物标志物用于识别ARR和CHF.
- 开发的计算机辅助诊断 (CAD) 系统是高效的,准确的,不需要ECG信号预处理或细分.
- 拟议的系统显示出在临床环境中实际实施的重大前景.
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