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Clinically, analyzing the ECG records is often quite time-consuming. Therefore, it is important to perform rapid analysis and detect abnormalities in ECG signals such as arrhythmias. For this reason, computer-aided diagnostic systems are being developed. With the developed systems, abnormalities in the ECG record can be easily detected. Thus, the developed systems are useful for clinicians in determining the diagnosis and treatment methods of heart diseases. In this study, an IIR based elliptic digital filter was used for removing the baseline wander in the ECG signal. Morphologically based features of the ECG signal are extracted. Then, the most meaningful of these features were selected by using the SelectKBest method and the f_ classif score function. Five basic arrhythmias labelled according to the AAMI standard have been classified using machine learning methods utilizing these feature data sets. Logistic Regression (LR), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM) and Multi Layer Perceptron (MLP) machine learning methods were used as classifiers for arrhythmia diagnosis. The performance results obtained with these classifiers were examined and the LR method, which predicts the five basic arrhythmias as satisfactorily high was proposed as a classifier. The performance results of the LR method were obtained as Accuracy 99.766%, Sensitivity 99.416%, Specificity 99.854% and F1-Score 99.416%. In this study, the arrhythmia diagnosis method has been proposed which predicts five basic arrhythmias satisfactorily high. The proposed method can be used beneficially in computer-aided diagnostic systems. In this study, Google Colaboratory was used for the software and hardware needs of machine learning methods.

Cloud Computing, Feature Extraction, Google Colaboratory, Machine Learning, Signal Processing

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