Comparison of classification algorithms in classification of ECG beats by time series

Today one of the most important health problems are fatal heart related diseases. Early diagnosis and treatment of heart disease can prevent sudden death. Detected through the human body and seen as a result of activity of the heart´s electrical signals is called electrocardiogram (ECG). ECG signal, which can be easily obtained without causing any harm to patient´s body, is a good indicator of the disorder during operation of the hearth. In this study, Normal beats (N), left bundle branch block (LBBB), right bundle branch block (RBBB) and Paced beat(P) beats are classified and the classification performance has been analyzed. Time series of the signal is used as an input vector for classification algorithms instead of extracting features from the signal. Independent component analysis (ICA) is used for feature reduction. Neural networks, k-nearest neighbour, Bayes, and Decision trees classification algorithms were used. In this study, kNN showed best accuracy rates.