Biometric human recognition system based on ECG

Abstract

The ECG (electrocardiogram) is an emerging technology for biometric human identification. In this paper, the performance of an ECG biometric recognition system is evaluated. Signal processing techniques are utilized to extract the ECG features. In preprocessing stage, digital filters eliminate the noises and hence improve the signal to noise ratio. The process of ventricular complex (QRS Complex) detection depends on Pan and Tompkins approach that achieves an efficient QRS detection, and hence enhancing the feature extraction process. The main classifiers applied to the extracted features are Neural Network (NN), Fuzzy Logic (FL), Nearest Mean Classifier (NMC), Linear Discriminant Analysis (LDA), and Euclidean Distance (ED) are utilized to classify QRS fragments. ECG of an unknown subject is acquired; the classifiers are applied to wavelet coefficient features set between the unknown subject and all enrolled subjects. The Performance of the different approaches is evaluated by utilizing Sensitivity, Specificity, and efficiency, EER (Equal Error Rate) and ROC (Receiver Operating Characteristic) curve. The experiments are conducted on 112 individuals MIT-BIH database and the accuracy is up to 98.99%.