Algorithmic Study of the Characteristics of Electrocardiograph Signals in Patients with Coronary Heart Disease

Abstract

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 ( $P<0.05$ ), but PR interval, RR interval, Tpeak-Tend (TpTe) interval, and QT interval were not substantially different from those before treatment ( $P>0.05$ ). The diagnostic accuracy, sensitivity, and specificity of LetNet-SoM algorithm for patients with CHD were better than those of traditional algorithms, with evident difference ( $P<0.05$ ). The classification time of LetNet-SoM algorithm was lower in contrast to that of traditional algorithms, and the difference was also notable ( $P<0.05$ ). The R wave and T wave indicators of patients after treatment were higher than before treatment, with notable difference ( $P<0.05$ ). The difference between the patient’s S wave indicator before and after treatment was not statistically significant ( $P>0.05$ ). 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.