Healthcare knowledge of relationship between time series electrocardiogram and cigarette smoking using clinical records
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RESEARCH
Open Access
Healthcare knowledge of relationship between time series electrocardiogram and cigarette smoking using clinical records Kuo-Kun Tseng1, Jiaqian Li1, Yih-Jing Tang2*, Ching Wen Yang3 and Fang-Ying Lin4* From 5th China Health Information Processing Conference Guangzhou, China. 22-24 November 2019
Abstract Background: 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). 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. Methods: In 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. Results: Two 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. Conclusions: The electrocardiogram diagnostic system performs satisfactorily in the cigarette smoking habit analysis task, and demonstrates that cigarette smoking is significantly associated with the electrocardiogram. Keywords: Electrocardiogram, Smoking, Diagnostic system, Neural networks
* Correspondence: [email protected]; [email protected]; [email protected] 2 Department of Family Medicine, Center for Geriatrics and Gerontology, Taichung Veterans General Hospital, Taichung, Taiwan 4 School of Economics and Management, Harbin Institute of Technology (Shenzhen), Shenzhen, China Full list of author information is available at the end of the article
Background 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. The ECG signal is a onedimensional data set representing the time of electrical
© The Author(s). 2020 Op
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