ECG biometric using multilayer perceptron and radial basis function neural networks

Abstract

This paper proposes a new method to identify people using Electrocardiogram (ECG), particularly the QRS complex which has been proven to be stable against heart rate variability and convenient to be used alone as a biometric feature. 324 QRS complexes are extracted from ECGs of 18 subjects in Physionet's MIT-BIH Normal Sinus Rhythm Database (NSRDB). Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural networks are used to classify those QRS complexes. If the training data are chosen carefully to cover a wide range of input values (i.e. QRS complexes), then the classification accuracy rates can reach above 98% using MLP and 97% using RBF.