State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, People's Republic of China.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing, People's Republic of China.
Physiol Meas. 2022 Oct 31;43(10). doi: 10.1088/1361-6579/ac7695.
. The ECG is a standard diagnostic tool for identifying many arrhythmias. Accurate diagnosis and early intervention for arrhythmias are of great significance to the prevention and treatment of cardiovascular disease. Our objective is to develop an algorithm that can automatically identify 30 arrhythmias by using varying-dimensional ECG signals.. In this paper, we firstly proposed a novel multi-scale 2D CNN that can effectively capture pathological information from small-scale to large-scale from ECG signals to identify 30 arrhythmias from 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs. Secondly, we explored the effects of varying convolution kernels sizes and branch subnetworks on the model's performance for each arrhythmia. Thirdly, we introduced the weighted focal loss to alleviate the positive-negative class imbalance problem in the multi-label arrhythmias classification. Fourthly, we explored the utility of reduced-lead ECGs in detecting arrhythmias by comparing the performances of models on varying-dimensional ECGs.. As a follow-up entry after the PhysioNet/Computing in Cardiology Challenge (2021), our proposed approach achieved the official test scores of 0.52, 0.47, 0.53, 0.51, and 0.50 for the 12-lead, 6-lead, 4-lead, 3-lead, and 2-lead ECGs on the hidden test set (comparable to that of 6th, 11th, 4th, 5th, and 7th out of 39 teams in the Challenge).. A multi-scale framework capable of detecting 30 arrhythmias from varying-dimensional ECGs was proposed in our work. We preliminarily verified that the multi-scale perception fields may be necessary to capture more comprehensive pathological information for arrhythmias detection. Besides, we also verified that the weighted focal loss may alleviate the positive-negative class imbalance and improve the model's generalization performance on the cross-dataset. In addition, we observed that some reduced-lead models, such as the 4-lead and 3-lead models, can even achieve performance that is almost comparable to that of the 12-lead model. The excellent performance of our proposed framework demonstrates its great potential in detecting a wide range of arrhythmias.
心电图是识别许多心律失常的标准诊断工具。心律失常的准确诊断和早期干预对心血管疾病的预防和治疗具有重要意义。我们的目标是开发一种算法，该算法可以使用不同维度的心电图信号自动识别 30 种心律失常。在本文中，我们首先提出了一种新颖的多尺度 2D CNN，该 CNN 可以有效地从小规模到大规模从心电图信号中捕获病理信息，从而从 12 导联、6 导联、4 导联、3 导联和 2 导联心电图中识别 30 种心律失常。其次，我们探讨了不同卷积核大小和分支子网对模型对每种心律失常性能的影响。第三，我们引入了加权焦点损失以减轻多标签心律失常分类中的正负类不平衡问题。第四，我们通过比较不同维度心电图上模型的性能，探讨了减少导联心电图在检测心律失常中的实用性。作为 PhysioNet/Computing in Cardiology Challenge（2021）的后续参赛作品，我们的方法在隐藏测试集上为 12 导联、6 导联、4 导联、3 导联和 2 导联心电图分别获得了 0.52、0.47、0.53、0.51 和 0.50 的官方测试分数（在挑战赛的 39 个团队中，排名第 6、11、4、5 和 7）。我们的工作提出了一种能够从不同维度的心电图中检测 30 种心律失常的多尺度框架。我们初步验证了多尺度感知场可能是捕获心律失常更全面病理信息所必需的。此外，我们还验证了加权焦点损失可以减轻正负类不平衡，并提高模型在跨数据集上的泛化性能。此外，我们观察到一些减少导联的模型，例如 4 导联和 3 导联模型，甚至可以达到几乎与 12 导联模型相当的性能。我们提出的框架的出色性能表明了其在检测广泛心律失常方面的巨大潜力。
