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The ECG acquisition procedure is one of the mostly used elements during initial patient examination upon hospital admission. It provides significant information about the circulatory system, electrolytic balance and even substance abuse. The test is quick, cheap, and safe for the patient due to the noninvasive nature. Nevertheless, the signal can vary significantly between individual people due to multiple factors, including differences in anatomical build of patients. Also, the ECG signal can include noise from multiple sources, especially when sampled using a mobile device. It is important for the classification algorithm to be robust enough to work in noisy conditions for as many cases as possible. The classification method described in this paper proceeds in several distinctive steps. The first operation is data preparation and wavelet filtering. Afterwards the QRS complexes are detected using the Pan-Tompkins method. The following steps include peak detection and polynomial approximations to calculate the positions and length of both P and T waves. The diagnostically relevant parameters are later used for classification using Naive Bayes and Support Vector Machine classifiers. The results obtained from the classification are presented for a group of over 50 patients both before and after normalized physical exercise.