Considering the variability of amplitude and phase patterns in
electrocardiogram (ECG) signals due to cardiac activity and individual
differences, existing entropy-based studies have not fully utilized these two
patterns and lack integration. To address this gap, this paper proposes a novel
fusion entropy metric, morphological ECG entropy (MEE) for the first time,
specifically designed for ECG morphology, to comprehensively describe the
fusion of amplitude and phase patterns. MEE is computed based on beat-level
samples, enabling detailed analysis of each cardiac cycle. Experimental results
demonstrate that MEE achieves rapid, accurate, and label-free localization of
abnormal ECG arrhythmia regions. Furthermore, MEE provides a method for
assessing sample diversity, facilitating compression of imbalanced training
sets (via representative sample selection), and outperforms random pruning.
Additionally, MEE exhibits the ability to describe areas of poor quality. By
discussing, it proves the robustness of MEE value calculation to noise
interference and its low computational complexity. Finally, we integrate this
method into a clinical interactive interface to provide a more convenient and
intuitive user experience. These findings indicate that MEE serves as a
valuable clinical descriptor for ECG characterization. The implementation code
can be referenced at the following link:
this https URL

提出了一种新的融合熵度量方法 —— 形态心电图熵（MEE），用于全面描述振幅和相位模式的融合，通过对节拍级样本进行计算，可以对每个心脏周期进行详细分析，实验结果表明，MEE 能够快速、准确、无标签地定位异常心电图心律失常区域，这个熵度量方法还能用于评估样本多样性，有助于不平衡训练集的压缩，MEE 优于随机修剪法，而且能描述低质量区域，具有较强的噪声干扰鲁棒性和低计算复杂度。最后，将该方法集成到临床交互界面中，提供更便捷直观的用户体验，研究发现表明，MEE 是心电图特征描述的有价值临床指标。