通过非线性分析揭示心脏病心电图复杂度变化，提升分类准确率。
Exploring Complexity Changes in Diseased ECG Signals for Enhanced Classification
- 用非线性时间序列分析量化心电图复杂度变化
- 引入跨导联度量后分类AUC达0.90，提升显著
- 适合心血管疾病智能诊断研究者参考
心脏电活动的复杂动态可通过心电图（ECG）反映。本研究利用非线性时间序列分析，探究心脏病理下心电图复杂度的变化。基于大型PTB-XL数据集，从导联II提取非线性特征，并结合导联II、V2、AVL的斯皮尔曼相关性和互信息构建跨通道度量。几乎所有指标在健康与患病群体间均存在显著差异（p<0.001），且在5个诊断大类间也具显著性。将这些复杂度度量纳入机器学习模型后，分类准确率以ROC曲线下面积（AUC）衡量，从基线0.86提升至0.87（仅含非线性度量），进一步增至0.90（包含跨时序度量）。
原文摘要 · Abstract (English)
The complex dynamics of the heart are reflected in its electrical activity, captured through electrocardiograms (ECGs). In this study we use nonlinear time series analysis to understand how ECG complexity varies with cardiac pathology. Using the large PTB-XL dataset, we extracted nonlinear measures from lead II ECGs, and cross-channel metrics (leads II, V2, AVL) using Spearman correlations and mutual information. Significant differences between diseased and healthy individuals were found in almost all measures between healthy and diseased classes, and between 5 diagnostic superclasses ($p<.001$). Moreover, incorporating these complexity quantifiers into machine learning models substantially improved classification accuracy measured using area under the ROC curve (AUC) from 0.86 (baseline) to 0.87 (nonlinear measures) and 0.90 (including cross-time series metrics).
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