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Biometrics is an interesting study due to the incredible progress in security. Electrocardiogram (ECG) signal analysis is an active research area for diagnoses. Various techniques have been proposed in human identification systems based on ECG. This work investigates in ECG as a biometric trait which is based on uniqueness. In this paper, a proposed non-fiducial identification system is presented with comparative study using radial basis functions (RBF) neural network, back propagation (BP) neural network, and support vector machine (SVM) as classification methods. The discrete wavelet transform method is applied to extract features from the ECG signal. The experimental results show that the proposed scheme achieves a high identification rate compared to the existing techniques. Furthermore, the two classifiers, RBF and BP, are integrated to achieve higher rates of human identification.

Human Identification Using Electrocardiogram Signal as a Biometric Trait