Pattern Recognition Application in ECG Arrhythmia Classification

In this paper, we propose a pattern recognition algorithm for arrhythmia recognition. Irregularity in the electrical activity of the heart (arrhythmia) is one of the leading reasons for sudden cardiac death in the world. Developing automatic computer aided techniques to diagnose this condition with high accuracy can play an important role in aiding cardiologists with decisions. In this work, we apply an adaptive segmentation approach, based on the median value of R-R intervals, on the de-noised ECG signals from the publically available MIT-BIH arrhythmia database and split signal into beat segments. The combination of wavelet transform and uniform one dimensional local binary pattern (1-D LBP) is applied to extract sudden variances and distinctive hidden patterns from ECG beats. Uniform 1-D LBP is not sensitive to noise and is computationally effective. ELM classification is adopted to classify beat segments into five types, based on the ANSI/AAMI EC57:1998 standard recommendation. Our preliminary experimental results show the effectiveness of the proposed algorithm in beat classification with 98.99% accuracy compared to the state of the art approaches.