Amplitude-Phase Fusion for Enhanced Electrocardiogram Morphological Analysis
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929314149171200 |
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| author | Hu, Shuaicong Wang, Yanan Liu, Jian Lin, Jingyu Qin, Shengmei Nie, Zhenning Yao, Zhifeng Cai, Wenjie Yang, Cuiwei |
| author_facet | Hu, Shuaicong Wang, Yanan Liu, Jian Lin, Jingyu Qin, Shengmei Nie, Zhenning Yao, Zhifeng Cai, Wenjie Yang, Cuiwei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_09729 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Amplitude-Phase Fusion for Enhanced Electrocardiogram Morphological Analysis Hu, Shuaicong Wang, Yanan Liu, Jian Lin, Jingyu Qin, Shengmei Nie, Zhenning Yao, Zhifeng Cai, Wenjie Yang, Cuiwei Signal Processing Information Theory Machine Learning Methodology I.5.2 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. |
| title | Amplitude-Phase Fusion for Enhanced Electrocardiogram Morphological Analysis |
| topic | Signal Processing Information Theory Machine Learning Methodology I.5.2 |
| url | https://arxiv.org/abs/2404.09729 |