Electrocardiograph Signals Diagnosis Using Adaptive Neuro-Fuzzy Inference System

Electrocardiograph (ECG) is a bioelectrical signal that is obtained by non-invasive method to register the electrical activities of the heart. This paper provides an attempt to develop computerized system for ECG signal filtering and classification. The proposed system encompass: pre-processing of the signal, extraction of pattern features through Independent Component Analysis (ICA), power spectrum, and RR interval calculation. These processes provide an input feature vector to the Adaptive Neuro Fuzzy Inference System (ANFIS) that acts as a signal classifier. All of the classification process steps are implemented in MATLABenvironment. This paper also provides a Graphical User Interface (GUI) that makes classification process easier. Three cases of ECG waveforms that are selected from MIT-BIH database are considered for the system test; they are Normal (N), Ventricle Fibrillation (VF), and Ventricular tachycardia (VTachy). An accuracy of 96.66% has been achieved by the proposed system