This work aimed to analyze the electrocardiogram (ECG) characteristics and signal classification of patients with coronary heart disease (CHD) diagnosed by coronary angiography, so as to provide a theoretical basis for the clinical adoption of ECG images. 106 patients with CHD who were admitted to the XXX hospital from January 15, 2019, to May 30, 2020, underwent coronary intervention therapy, and their ECG indicators were recorded during the operation. Then, the LetNet-SoM algorithm designed in this work, as well as the traditional algorithms GoogLeNet and SqueezeNet, was applied to the patient’s ECG classification. It was found that part of ECG wave (QRS) and corrected Q-T interval (QTC) of patients after treatment were higher than those before treatment (), but PR interval, RR interval, Tpeak-Tend (TpTe) interval, and QT interval were not substantially different from those before treatment (). The diagnostic accuracy, sensitivity, and specificity of LetNet-SoM algorithm for patients with CHD were better than those of traditional algorithms, with evident difference (). The classification time of LetNet-SoM algorithm was lower in contrast to that of traditional algorithms, and the difference was also notable (). The R wave and T wave indicators of patients after treatment were higher than before treatment, with notable difference (). The difference between the patient’s S wave indicator before and after treatment was not statistically significant (). The positive rate of S wave amplitude, QRS, and QTC was 68.15%, 60.52%, and 51.36%, respectively. In short, the LetNet-SoM algorithm designed based on lightweight neural network had excellent performance in classification and diagnosis of ECG, and it had the value of further popularization and application. The ECG signals were important indicators in the diagnosis of CHD, among which the S wave amplitude, QRS, and QTC were the most sensitive ones.

中文翻译：

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冠心病患者心电图信号特征的算法研究

【摘要】：目的分析冠状动脉造影确诊冠心病(CHD)患者的心电图(ECG)特征及信号分类,为心电图图像的临床应用提供理论依据。对2019年1月15日至2020年5月30日在XXX医院收治的106例冠心病患者进行冠脉介入治疗，并在术中记录心电图指标。然后，将本文设计的 LetNet-SoM 算法，以及传统算法 GoogLeNet 和 SqueezeNet 应用于患者的心电图分类。发现部分患者治疗后心电波（QRS）和校正QT间期（QTC）高于治疗前（)，但 PR 间期、RR 间期、Tpeak-Tend (TpTe) 间期和 QT 间期与治疗前无显着差异（）。LetNet-SoM算法对冠心病患者的诊断准确性、敏感性和特异性均优于传统算法，差异明显（）。LetNet-SoM算法的分类时间比传统算法低，差异也很显着（）。治疗后患者R波、T波指标均高于治疗前，差异显着（）。患者治疗前后S波指标差异无统计学意义（）。S波振幅、QRS、QTC阳性率分别为68.15%、60.52%和51.36%。总之，基于轻量级神经网络设计的LetNet-SoM算法在心电分类诊断方面具有优异的性能，具有进一步推广应用的价值。心电信号是诊断冠心病的重要指标，其中S波幅度、QRS、QTC最为敏感。