对心电图分类的几何洞察:利用相位空间重建和距离来测量
Xinlong Yang1, Tianming Cai1, Junbin Zang1,2
1State Key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instrument, North University of China, Taiyuan, Shanxi, China.
Computer methods in biomechanics and biomedical engineering
|November 11, 2025
概括
我们开发了一种使用相位空间重建和距离测量函数的新几何方法,用于强大的心电图 (ECG) 分类. 这种方法增强了噪声的抵御力,并准确地捕捉了心脏动态,从而提高了诊断潜力.
科学领域:
- 心脏病学 心脏病学
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 电心电图 (ECG) 分析对于诊断心脏疾病至关重要.
- 传统的心电图分类方法可能对噪声敏感,可能无法完全捕捉复杂的心脏动态.
- 开发可靠和可解释的心电图分析技术是一个持续的挑战.
研究的目的:
- 为强大的心电图分类引入一个新的几何框架.
- 为了利用相位空间重建 (PSR) 和距离测量 (DTM) 功能来分析内在的心脏动态.
- 增强电心电图分类算法的抗噪力和区分能力.
主要方法:
- 嵌入一个维的心电图信号到高维的相位空间使用PSR.
- 应用了中位数的核密度估计,以确定相位空间中的高密度区域.
- 构建基于DTM的伪距离来表示心律特征.
- 评估了MIT-BIH和PTB ECG数据集的框架.
主要成果:
- 使用后勤回归实现了89.08%的准确性.
- 即使在高斯噪声下,使用梯度提升树木实现了91.42%的准确性.
- 证明了DTM特征是对ECG分析的传统统计特征的补充.
结论:
- 拟议的几何框架为ECG分析提供了一种非线性,可解释和噪声强大的方法.
- 基于DTM的特征显示了提高ECG分类的准确性和可靠性的巨大潜力.
- 这种方法为先进的心脏诊断提供了一个有希望的途径.
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