基于心电图向量的机器学习模型将俯卧位心电图转换为标准心电图的建立与验证
Zhang, H., Li, Z., Li, S., Shao, X., Zhang, F., Xue, Z., Chen, Z., Liu, J., Hong, S., Geng, S., Geng, X., Zhou, J., Tse, G., Liu, X., Tao, H., Liu, T., & Chen, K. (2025). Establishment and validation of an electrocardiogram vector-based machine learning model for the conversion of prone position electrocardiograms into standard electrocardiograms. European Heart Journal – Digital Health. https://doi.org/10.1093/ehjdh/ztaf146
Zhang H, Li Z, Li S, Shao X, Zhang F, Xue Z, et al. Establishment and validation of an electrocardiogram vector-based machine learning model for the conversion of prone position electrocardiograms into standard electrocardiograms. European Heart Journal – Digital Health. 2025; doi: 10.1093/ehjdh/ztaf146
Zhang H, Li Z, Li S, et al. Establishment and validation of an electrocardiogram vector-based machine learning model for the conversion of prone position electrocardiograms into standard electrocardiograms[J]. European Heart Journal – Digital Health. 2025. DOI: 10.1093/ehjdh/ztaf146.
@article{zhang2025,
author = {Haoran Zhang and Zejia Li and Shaofeng Li and Xing Shao and Fangfang Zhang and Zhikang Xue and Zhonglin Chen and Jiayi Liu and Shida Hong and Shijing Geng and Xinhao Geng and Jianda Zhou and Gary Tse and Xin Liu and Haoyu Tao and Tong Liu and Kuiyang Chen},
title = {Establishment and validation of an electrocardiogram vector-based machine learning model for the conversion of prone position electrocardiograms into standard electrocardiograms},
journal = {European Heart Journal – Digital Health},
year = {2025},
doi = {10.1093/ehjdh/ztaf146},
note = {PMID: 41853637},
}
TY - JOUR AU - Haoran Zhang AU - Zejia Li AU - Shaofeng Li AU - Xing Shao AU - Fangfang Zhang AU - Zhikang Xue AU - Zhonglin Chen AU - Jiayi Liu AU - Shida Hong AU - Shijing Geng AU - Xinhao Geng AU - Jianda Zhou AU - Gary Tse AU - Xin Liu AU - Haoyu Tao AU - Tong Liu AU - Kuiyang Chen TI - Establishment and validation of an electrocardiogram vector-based machine learning model for the conversion of prone position electrocardiograms into standard electrocardiograms T2 - European Heart Journal – Digital Health PY - 2025 DO - 10.1093/ehjdh/ztaf146 AN - PMID:41853637 ER -
俯卧位心电图在重症监护和特殊检查环境中经常获取，但其解读受到体位变化的显著影响，导致ST段偏移和T波改变，可能干扰STEMI等心血管疾病的诊断。本研究旨在开发一种将俯卧位心电图转换为标准心电图的方法，以方便医师诊断STEMI和其他心血管疾病。前瞻性采集了标准心电图、心电向量图（VCG）和俯卧位心电图数据，建立基于心电图向量的机器学习模型。研究使用多种机器学习算法进行建模，并通过独立数据集进行验证。结果显示，该模型能够有效校正俯卧位对心电图波形的影响，将俯卧位心电图转换为接近标准体位的等效心电图。转换后的心电图在STEMI诊断准确性方面显著提高。该研究为解决临床实践中俯卧位心电图解读困难提供了新的解决方案，具有重要的临床实用价值，特别是在重症监护和麻醉监测等场景中。