The study intended to explore the effect of different neural network algorithms in the electrocardiogram (ECG) classification of patients with congenital heart disease (CHD). Based on the single convolutional neural network (CNN) ECG algorithm and the recurrent neural network (RNN) ECG algorithm, a multimodal neural network (MNN) ECG algorithm was constructed utilizing the MIT-BIH database as training set and test set. Furthermore, the MNN ECG algorithm was optimized to establish an improved MNN (IMNN) algorithm, which was applied to the diagnosis of CHD patients. The CHD patients admitted between August 2016 and August 2019 were selected for analysis to compare the classification effect and accuracy rate of IMNN, MNN, CNN ECG, and RNN ECG algorithms. It was found that the RNN ECG algorithm had higher classification sensitivity and true positive rate in terms of normal or bundle (NB) branch block beat, supraventricular abnormal (SA) rhythm, abnormal ventricular (AV) beat, and fusion beat (FB) than the CNN ECG algorithm (), and the classification sensitivity and true positive rate of IMNN algorithm in the four aspects were significantly higher than those of MNN algorithm (). The classification accuracy of CNN ECG algorithm and RNN ECG algorithm was above 98%, while that of MNN algorithm and IMNN algorithm was better than that of CNN ECG algorithm and RNN ECG algorithm, and the accuracy rate can reach 98.5% or more. Moreover, the accuracy rate of the IMNN algorithm can reach more than 98%. In conclusion, IMNN not only has a good classification ability in the simulated environment but also performs well in the actual environment, which is worthy of clinical promotion.

中文翻译：

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先天性心脏病患者心电图的神经网络算法分类

该研究旨在探讨不同神经网络算法在先天性心脏病 (CHD) 患者心电图 (ECG) 分类中的作用。基于单卷积神经网络（CNN）心电图算法和循环神经网络（RNN）心电图算法，以MIT-BIH数据库为训练集和测试集构建多模态神经网络（MNN）心电图算法。此外，对MNN心电图算法进行了优化，建立了改进的MNN（IMNN）算法，应用于CHD患者的诊断。选取2016年8月至2019年8月收治的CHD患者进行分析，比较IMNN、MNN、CNN ECG和RNN ECG算法的分类效果和准确率。)，且IMNN算法在四个方面的分类灵敏度和真阳性率均显着高于MNN算法(）。CNN心电图算法和RNN心电图算法的分类准确率均在98%以上，而MNN算法和IMNN算法的分类准确率优于CNN心电图算法和RNN心电图算法，准确率可达98.5%以上。而且，IMNN算法的准确率可以达到98%以上。综上所述，IMNN不仅在模拟环境下具有良好的分类能力，在实际环境中也表现良好，值得临床推广。