Liang Chengwei, Yang Fan, Huang Xiaobing, Zhang Lijuan, Wang Ying
Department of Automation, Xiamen University, Xiamen, Fujian, China.
National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen, Fujian, China.
Hypertens Res. 2025 Feb;48(2):681-692. doi: 10.1038/s41440-024-01938-7. Epub 2024 Oct 12.
Arterial hypertension is a major risk factor for cardiovascular diseases. While cardiac ultrasound is a typical way to diagnose hypertension-mediated heart change, it often fails to detect early subtle structural changes. Electrocardiogram(ECG) represents electrical activity of heart muscle, affected by the changes in heart's structure. It is crucial to explore whether ECG can capture slight signals of hypertension-mediated heart change. However, reading ECG records is complex and some signals are too subtle to be captured by cardiologist's visual inspection. In this study, we designed a deep learning model to predict hypertension on ECG signals and then to identify hypertension-associated ECG segments. From The First Affiliated Hospital of Xiamen University, we collected 210,120 10-s 12-lead ECGs using the FX-8322 manufactured by FUKUDA and 812 ECGs using the RAGE-12 manufactured by NALONG. We proposed a deep learning framework, including MML-Net, a multi-branch, multi-scale LSTM neural network to evaluate the potential of ECG signals to detect hypertension, and ECG-XAI, an ECG-oriented wave-alignment AI explanation pipeline to identify hypertension-associated ECG segments. MML-Net achieved an 82% recall and an 87% precision in the testing, and an 80% recall and an 82% precision in the independent testing. In contrast, experienced clinical cardiologists typically attain recall rates ranging from 30 to 50% by visual inspection. The experiments demonstrate that ECG signals are sensitive to slight changes in heart structure caused by hypertension. ECG-XAI detects that R-wave and P-wave are the hypertension-associated ECG segments. The proposed framework has the potential to facilitate early diagnosis of heart change.
动脉高血压是心血管疾病的主要危险因素。虽然心脏超声是诊断高血压介导的心脏变化的典型方法，但它常常无法检测到早期细微的结构变化。心电图（ECG）代表心肌的电活动，会受到心脏结构变化的影响。探索心电图是否能够捕捉到高血压介导的心脏变化的细微信号至关重要。然而，解读心电图记录很复杂，一些信号过于细微，心脏病专家通过目视检查难以捕捉到。在本研究中，我们设计了一个深度学习模型，用于根据心电图信号预测高血压，进而识别与高血压相关的心电图片段。我们从厦门大学附属第一医院收集了210,120份由福田公司生产的FX - 8322记录的10秒12导联心电图，以及812份由纳龙公司生产的RAGE - 12记录的心电图。我们提出了一个深度学习框架，包括用于评估心电图信号检测高血压潜力的多分支、多尺度长短期记忆（LSTM）神经网络MML - Net，以及用于识别与高血压相关的心电图片段的面向心电图的波形对齐人工智能解释管道ECG - XAI。MML - Net在测试中的召回率为82%，精确率为87%，在独立测试中的召回率为80%，精确率为82%。相比之下，经验丰富的临床心脏病专家通过目视检查通常能达到30%至50%的召回率。实验表明，心电图信号对高血压引起的心脏结构细微变化敏感。ECG - XAI检测到R波和P波是与高血压相关的心电图片段。所提出的框架具有促进心脏变化早期诊断的潜力。
