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.
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.
Authors: U Rajendra Acharya; K Paul Joseph; N Kannathal; Choo Min Lim; Jasjit S Suri Journal: Med Biol Eng Comput Date: 2006-11-17 Impact factor: 2.602
Authors: U Rajendra Acharya; Oliver Faust; Vinitha Sree; G Swapna; Roshan Joy Martis; Nahrizul Adib Kadri; Jasjit S Suri Journal: Comput Methods Programs Biomed Date: 2013-09-10 Impact factor: 5.428
Authors: R M Carney; J A Blumenthal; P K Stein; L Watkins; D Catellier; L F Berkman; S M Czajkowski; C O'Connor; P H Stone; K E Freedland Journal: Circulation Date: 2001-10-23 Impact factor: 29.690
Authors: E Buccelletti; E Gilardi; E Scaini; L Galiuto; R Persiani; A Biondi; F Basile; N Gentiloni Silveri Journal: Eur Rev Med Pharmacol Sci Date: 2009 Jul-Aug Impact factor: 3.507