Fuzzy Support Vector Machines for ECG Arrhythmia Detection

Besides cardiovascular diseases, heart attacks are the main cause of death around the world. Pre-monitoring or pre-diagnostic helps to prevent heart attacks and strokes. ECG plays a key role in this regard. In recent studies, SVM with different kernel functions and parameter values are applied for classification on ECG data. The classification model of SVM can be improved by assigning membership values for inputs. SVM combined with fuzzy theory, FSVM, is exercised on UCI Arrhythmia Database. Five different membership functions are defined. It is shown that the accuracy of classification can be improved by defining appropriate membership functions. ANFIS is used in order to interpret the resulting classification model. The ANFIS model of the ECG data is compared to and found consistent with the medical knowledge.