Jin Yanrui, Li Zhiyuan, Wang Mengxiao, Liu Jinlei, Tian Yuanyuan, Liu Yunqing, Wei Xiaoyang, Zhao Liqun, Liu Chengliang
State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China.
MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai, China.
Commun Med (Lond). 2024 Feb 28;4(1):31. doi: 10.1038/s43856-024-00464-4.
Long-term monitoring of Electrocardiogram (ECG) recordings is crucial to diagnose arrhythmias. Clinicians can find it challenging to diagnose arrhythmias, and this is a particular issue in more remote and underdeveloped areas. The development of digital ECG and AI methods could assist clinicians who need to diagnose arrhythmias outside of the hospital setting.
We constructed a large-scale Chinese ECG benchmark dataset using data from 272,753 patients collected from January 2017 to December 2021. The dataset contains ECG recordings from all common arrhythmias present in the Chinese population. Several experienced cardiologists from Shanghai First People's Hospital labeled the dataset. We then developed a deep learning-based multi-label interpretable diagnostic model from the ECG recordings. We utilized Accuracy, F1 score and AUC-ROC to compare the performance of our model with that of the cardiologists, as well as with six comparison models, using testing and hidden data sets.
The results show that our approach achieves an F1 score of 83.51%, an average AUC ROC score of 0.977, and 93.74% mean accuracy for 6 common arrhythmias. Results from the hidden dataset demonstrate the performance of our approach exceeds that of cardiologists. Our approach also highlights the diagnostic process.
Our diagnosis system has superior diagnostic performance over that of clinicians. It also has the potential to help clinicians rapidly identify abnormal regions on ECG recordings, thus improving efficiency and accuracy of clinical ECG diagnosis in China. This approach could therefore potentially improve the productivity of out-of-hospital ECG diagnosis and provides a promising prospect for telemedicine.
长期监测心电图（ECG）记录对于诊断心律失常至关重要。临床医生可能会发现诊断心律失常具有挑战性，而在更偏远和欠发达地区，这一问题尤为突出。数字心电图和人工智能方法的发展可以帮助需要在医院外诊断心律失常的临床医生。
我们使用2017年1月至2021年12月期间收集的272,753名患者的数据构建了一个大规模的中国心电图基准数据集。该数据集包含中国人群中所有常见心律失常的心电图记录。上海第一人民医院的几位经验丰富的心脏病专家对该数据集进行了标注。然后，我们从心电图记录中开发了一种基于深度学习的多标签可解释诊断模型。我们使用测试数据集和隐藏数据集，利用准确率、F1分数和AUC-ROC来比较我们的模型与心脏病专家以及六个比较模型的性能。
结果表明，我们的方法在6种常见心律失常方面的F1分数达到83.51%，平均AUC-ROC分数为0.977，平均准确率为93.74%。隐藏数据集的结果表明我们方法的性能超过了心脏病专家。我们的方法还突出了诊断过程。
我们的诊断系统具有优于临床医生的诊断性能。它还有助于临床医生快速识别心电图记录上的异常区域，从而提高中国临床心电图诊断的效率和准确性。因此，这种方法有可能提高院外心电图诊断的效率，并为远程医疗提供广阔前景。
