{"title":"Heart rate dynamics in the prediction of coronary artery disease and myocardial infarction using artificial neural network and support vector machine.","authors":"Rahul Kumar, Yogender Aggarwal, Vinod Kumar Nigam","doi":"10.32725/jab.2022.008","DOIUrl":null,"url":null,"abstract":"BACKGROUND Atherosclerosis leads to coronary artery disease (CAD) and myocardial infarction (MI), a major cause of morbidity and mortality worldwide. The computer-aided prognosis of atherosclerotic events with the electrocardiogram (ECG) derived heart rate variability (HRV) can be a robust method in the prognosis of atherosclerosis events. METHODS A total of 70 male subjects aged 55 ± 5 years participated in the study. The lead-II ECG was recorded and sampled at 200 Hz. The tachogram was obtained from the ECG signal and used to extract twenty-five HRV features. The one-way Analysis of variance (ANOVA) test was performed to find the significant differences between the CAD, MI, and control subjects. Features were used in the training and testing of a two-class artificial neural network (ANN) and support vector machine (SVM). RESULTS The obtained results revealed depressed HRV under atherosclerosis. Accuracy of 100% was obtained in classifying CAD and MI subjects from the controls using ANN. Accuracy was 99.6% with SVM, and in the classification of CAD from MI subjects using SVM and ANN, 99.3% and 99.0% accuracy was obtained respectively. CONCLUSIONS Depressed HRV has been suggested to be a marker in the identification of atherosclerotic events. The good accuracy observed in classification between control, CAD, and MI subjects, revealed it to be a non-invasive cost-effective approach in the prognosis of atherosclerotic events.","PeriodicalId":14912,"journal":{"name":"Journal of applied biomedicine","volume":"20 2","pages":"70-79"},"PeriodicalIF":2.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of applied biomedicine","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.32725/jab.2022.008","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/6/21 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"MEDICINE, RESEARCH & EXPERIMENTAL","Score":null,"Total":0}
引用次数: 1
Abstract
BACKGROUND Atherosclerosis leads to coronary artery disease (CAD) and myocardial infarction (MI), a major cause of morbidity and mortality worldwide. The computer-aided prognosis of atherosclerotic events with the electrocardiogram (ECG) derived heart rate variability (HRV) can be a robust method in the prognosis of atherosclerosis events. METHODS A total of 70 male subjects aged 55 ± 5 years participated in the study. The lead-II ECG was recorded and sampled at 200 Hz. The tachogram was obtained from the ECG signal and used to extract twenty-five HRV features. The one-way Analysis of variance (ANOVA) test was performed to find the significant differences between the CAD, MI, and control subjects. Features were used in the training and testing of a two-class artificial neural network (ANN) and support vector machine (SVM). RESULTS The obtained results revealed depressed HRV under atherosclerosis. Accuracy of 100% was obtained in classifying CAD and MI subjects from the controls using ANN. Accuracy was 99.6% with SVM, and in the classification of CAD from MI subjects using SVM and ANN, 99.3% and 99.0% accuracy was obtained respectively. CONCLUSIONS Depressed HRV has been suggested to be a marker in the identification of atherosclerotic events. The good accuracy observed in classification between control, CAD, and MI subjects, revealed it to be a non-invasive cost-effective approach in the prognosis of atherosclerotic events.
期刊介绍:
Journal of Applied Biomedicine promotes translation of basic biomedical research into clinical investigation, conversion of clinical evidence into practice in all medical fields, and publication of new ideas for conquering human health problems across disciplines.
Providing a unique perspective, this international journal publishes peer-reviewed original papers and reviews offering a sensible transfer of basic research to applied clinical medicine. Journal of Applied Biomedicine covers the latest developments in various fields of biomedicine with special attention to cardiology and cardiovascular diseases, genetics, immunology, environmental health, toxicology, neurology and oncology as well as multidisciplinary studies. The views of experts on current advances in nanotechnology and molecular/cell biology will be also considered for publication as long as they have a direct clinical impact on human health. The journal does not accept basic science research or research without significant clinical implications. Manuscripts with innovative ideas and approaches that bridge different fields and show clear perspectives for clinical applications are considered with top priority.