Myocardial ischemia events detection based on support vector machines using QRS and ST features
Tipus de documentComunicació de congrés
EditorComputing in Cardiology
Condicions d'accésAccés obert
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.
CitacióMagrans, R., Gomis, P., Caminal, P. Myocardial ischemia events detection based on support vector machines using QRS and ST features. A: Computing in Cardiology Annual Conference. "Computing in cardiology 2016, volume 43, Vancouver, Canada". Vancouver: Computing in Cardiology, 2016, p. 273-416-1-273-416-4.
Versió de l'editorhttp://www.cinc.org/archives/2016/pdf/117-295.pdf