Research Information System
2nd International Conference on Applied Engineering, Konya, Turkey, 10 March - 13 November 2022, pp.489-492
The irregularities in the heartbeat are called arrhythmias and can be an essential subject for heart
health. The artifacts in the mechanical structure or the electrical signaling system of the heart result in these
irregularities. Arrhythmias depending on the different artifacts, cause different patterns on
electroencephalogram recordings of patients. Early diagnosis of cardiac arrhythmia is quite crucial to saving
patient lives. The main goal of this study is to detect the presence of cardiac arrhythmia and classify it into
sixteen different groups from the electrocardiogram (ECG) recordings. First, the classification
performances of four well-known classification algorithms, namely, multilayer perceptron (MLP), support
vector machines (SVM), random forest (RF), and K-means clustering, have been obtained using an
imbalanced electrocardiogram (ECG) dataset. The classifier performance criteria such as accuracy and
precision are used to determine the best classifier structure for each arrhythmia class. Then, to obtain a
better classifier performance, an ensemble network which is a cascaded form of two RF classifiers, is
constructed. The designed ensemble network consisting of two RF networks results in an accuracy of
73.63%.