Amplitude-Phase Fusion for Enhanced Electrocardiogram Morphological
  Analysis
Shuaicong Hu Yanan Wang Jian Liu Jingyu Lin Shengmei Qin Zhenning Nie Zhifeng Yao Wenjie Cai Cuiwei Yang
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:
https://github.com/fdu-harry/ECG-MEE-metric.