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

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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.

Related collections

Most cited references 17

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Cigarette smoking is the most preventable cause of cardiovascular morbidity and mortality. Smoking has been associated with a two-to fourfold increased risk of coronary heart disease, a greater than 70% excess rate of death from coronary heart disease, and an elevated risk of sudden death. These risks are compounded in the presence of hypertension, hypercholesterolemia, glucose intolerance, and diabetes, all of which exhibit a synergistic effect with smoking. The relationship between smoking and the risk of peripheral vascular disease has also been well documented. Smokers account for approximately 70% of patients with atherosclerosis obliterans and virtually all those with thromboangiitis obliterans. An association between smoking and cerebrovascular disease remains a matter of debate, although a higher risk of stoke and stroke-related mortality has been observed in smokers than in nonsmokers. Smoking has also been implicated in the development of cor pulmonale, but a direct association with congestive heart failure has not been established. Nicotine and carbon monoxide appear to play major roles in the cardiovascular effects of smoking. Both components adversely alter the myocardial oxygen supply/demand ratio and have been shown to produce endothelial injury, leading to the development of atherosclerotic plaque. Adverse effects on the lipid profile have been noted as well, but the relationship between these changes and the risk of cardiovascular disease remains to be confirmed. Notably, smoking cessation results in a dramatic reduction in the risk of mortality from both coronary heart disease and stroke. In light of the fact that the incidence of smoking has declined primarily among educated sectors of the U.S. population, future efforts must focus on providing effective education, including smoking cessation techniques, to the less-educated groups.

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Surface electromyograms (EMGs) are valuable in the pathophysiological study and clinical treatment for dystonia. These recordings are critically often contaminated by cardiac artefact. Our objective of this study was to evaluate the performance of an adaptive noise cancellation filter in removing electrocardiogram (ECG) interference from surface EMGs recorded from the trapezius muscles of patients with cervical dystonia. Performance of the proposed recursive-least-square adaptive filter was first quantified by coherence and signal-to-noise ratio measures in simulated noisy EMG signals. The influence of parameters such as the signal-to-noise ratio, forgetting factor, filter order and regularization factor were assessed. Fast convergence of the recursive-least-square algorithm enabled the filter to track complex dystonic EMGs and effectively remove ECG noise. This adaptive filter procedure proved a reliable and efficient tool to remove ECG artefact from surface EMGs with mixed and varied patterns of transient, short and long lasting dystonic contractions.

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Author and article information

Contributors

Kuo-Kun Tseng: kktseng@hit.edu.cn

Jiaqian Li: ljqxy26@hotmail.com

Yih-Jing Tang: yjtang@vghtc.gov.tw , yjtang@vghtc.gov.tw

Ching Wen Yang: cwhello7@gmail.com

Fang-Ying Lin: r.lin@hit.edu.cn

Conference

Journal ID (nlm-ta): BMC Med Inform Decis Mak

Journal ID (iso-abbrev): BMC Med Inform Decis Mak

Title: BMC Medical Informatics and Decision Making

Publisher: BioMed Central (London )

ISSN (Electronic): 1472-6947

Publication date (Electronic): 9 July 2020

Publication date PMC-release: 9 July 2020

Publication date Collection: 2020

Volume: 20

Issue : Suppl 3 Issue sponsor : Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.

Electronic Location Identifier: 127

Affiliations

[1 ]GRID grid.19373.3f, ISNI 0000 0001 0193 3564, School of Computer Science, , Harbin Institute of Technology (Shenzhen), ; Shenzhen, China

[2 ]GRID grid.410764.0, ISNI 0000 0004 0573 0731, Department of Family Medicine, Center for Geriatrics and Gerontology, , Taichung Veterans General Hospital, ; Taichung, Taiwan

[3 ]GRID grid.410764.0, ISNI 0000 0004 0573 0731, Computer & Communication Center, , Taichung Veterans General Hospital, ; Taichung, Taiwan

[4 ]GRID grid.19373.3f, ISNI 0000 0001 0193 3564, School of Economics and Management, , Harbin Institute of Technology (Shenzhen), ; Shenzhen, China

Article

Publisher ID: 1107

DOI: 10.1186/s12911-020-1107-2

PMC ID: 7346312

PubMed ID: 32646409

SO-VID: c829c5ac-1562-47b7-9636-46829694efc3

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Conference name: 5th China Health Information Processing Conference

Conference acronym: CHIP 2019

Conference location: Guangzhou, China

Conference date: 22-24 November 2019