This study aimed to develop a nonlinear support vector machine (SVM) model to detect ischemic events based on a dataset of QRS-derived and ST indices from nonischemic and acute ischemic episodes. The study included 67 patients undergoing elective percutaneous coronary intervention (PCI) with 12-lead continuous and signal-averaged ECG recordings before and during PCI. Fifty-four indices were initially considered from each episode. The dataset was randomly divided into training (80%) and testing (20%) subsets. The training subset was used to optimize the SVM parameters algorithm and for determining the most important statistically significant indices, by using repeated k-fold cross-validation (with N=25 repetitions and k=5). The described procedure was run on 25 randomized training/testing subsets to assess the average performance. On average, the most important indices were the QRSvector difference and the ST segment level at J-point + 60 ms computed from the synthesized vector magnitude, and the summed high-frequency QRS components of all 12 leads at 150 – 250 Hz band. The performance of testing was: classification error = 12.5(8.3 - 16.7)%, sensibility = 83.3(75.0 - 91.7)%, specificity = 91.7(83.3 - 91.7)%, positive predictive value = 90.9(83.0 - 92.3)% and negative predictive value = 85.7(80.0 - 91.7)%. The method used to construct the SVM model is robust enough and looks promising in detecting acute myocardial ischemia and myocardial infarction risk.

Peer Reviewed