Deteksi Aritmia pada Elektrokardiogram dengan Metode Jaringan Syaraf Tiruan Kelas Jamak menggunakan Fitur Interval RR, Lebar QRS, dan Gradien Gelombang R Mar’atus Solikhah, Nuryani, Darmanto

Abstract

Research for arrhythmias detection using Multilayer Perceptron-Backpropagation (MLP-BP) Artificial Neural Network (ANN) multiclass method has been successfully implemented. It utilized RR interval, QRS width, and R wave gradient features. Arrhythmia types used in this study were Premature Ventricular Contraction (PVC), Premature Atrial Contraction (PAC), and Left Bundle Branch Block (LBBB). This study was conducted by varying features number as the input of ANN. The variation includes two and three kinds of features. The best results were found when three features were included. The best performance were 94.63%, 93.94%, and 94.49% in terms of sensitivity, sprcificity and accuracy, respectively.