Healthcare knowledge of relationship between time series electrocardiogram and cigarette smoking using clinical records

Kuo-Kun Tseng,Jiaqian Li,Ching Wen Yang,Yih-Jing Tang,Fang-Ying Lin

doi:10.1186/s12911-020-1107-2

Abstract

BackgroundIn the few studies of clinical experience available, cigarette smoking may be associated with ischemic heart disease and acute coronary events, which can be reflected in the electrocardiogram (ECG). However, there is no formal proof of a significant relationship between cigarette smoking and electrocardiogram results. In this study, we therefore investigate and prove the relationship between electrocardiogram and smoking using unsupervised neural network techniques.MethodsIn this research, a combination of two techniques of pattern recognition; feature extraction and clustering neural networks, is specifically investigated during the diagnostic classification of cigarette smoking based on different electrocardiogram feature extraction methods, such as the reduced binary pattern (RBP) and Wavelet features. In this diagnostic system, several neural network models have been obtained from the different training subsets by clustering analysis. Unsupervised neural network of clustering cigarette smoking was then implemented based on the self-organizing map (SOM) with the best performance.ResultsTwo ECG datasets were investigated and analysed in this prospective study. One is the public PTB diagnostic ECG databset with 290 samples (age 17–87, mean 57.2; 209 men and 81 women; 73 smoking and 133 non-smoking). The other ECG database is from Taichung Veterans General Hospital (TVGH) and includes 480 samples (240 smoking, and 240 non-smoking). The diagnostic accuracy regarding smoking and non-smoking in the PTB dataset reaches 80.58% based on the RBP feature, and 75.63% in the second dataset based on Wavelet feature.ConclusionsThe electrocardiogram diagnostic system performs satisfactorily in the cigarette smoking habit analysis task, and demonstrates that cigarette smoking is significantly associated with the electrocardiogram.

Highlights

In the few studies of clinical experience available, cigarette smoking may be associated with ischemic heart disease and acute coronary events, which can be reflected in the electrocardiogram (ECG)
Cigarette smoking is a major preventable risk factor for coronary artery disease and sudden cardiac death [1], which is directly reflected in the ECG
Throughout the experiments, we found that the reduced binary pattern (RBP) feature is significantly more suitable for the Physikalisch-Technische Bundesanstalt (PTB) dataset, that is, when we use the RBP feature with different scales to first clustering and subsequently classify, the accuracy of the electrocardiogram diagnostic system is higher than that with the wavelet feature

Summary

Introduction

In the few studies of clinical experience available, cigarette smoking may be associated with ischemic heart disease and acute coronary events, which can be reflected in the electrocardiogram (ECG). Electrocardiography has a basic role in cardiology as it involves effective, simple, non-invasive and low-cost procedures for the diagnosis of cardiovascular disorders. Such disorders have a high epidemiologic incidence, and are of particular significance due to their impact on patient life and social costs. ECG technology has existed for over a century since its use for the first time in the clinic in 1903 In this century, the rapidly developing ECG technology has made great contributions to human life and health, as well as to developments in biological and clinical studies, and has been an indispensable routine examination technology in the clinic.

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