Electrocardiogram (ECG): A new burgeoning utility for biometric recognition

Abstract

Recently, Electrocardiogram (ECG) has been emerged as a new biometric trait. ECG as a biological signal has the advantage of being an aliveness indicator. Moreover, it is difficult to be spoofed or falsified. In this chapter, a comprehensive survey on the employment of ECG in biometric systems is provided. An overview of the ECG, its benefits and challenges, followed by a series of case studies are presented. Based on the survey, ECG based biometric systems can be fiducial or non-fiducial according to the utilized features. Most of the non-fiducial approaches relax the challenging fiducial detection process to include only the R peak yielding to more reliable features. However, the drawback of such approaches is that they usually resulted in high dimension feature space. Hence, a non-fiducial ECG biometric system based on decomposing the RR cycles in wavelet coefficient structures using discrete biorthogonalwavelet transform is introduced. These structures were reduced through a proposed two-phase reduction process. The first phase globally evaluates the different parts of the wavelet structure (five details and one approximation parts) and maintains those parts that preserve the system performance. However, the second phase excludes more coefficients by locally evaluating the coefficients of each part based on an information gain criterion. Our experiments were carried out with four Physionet datasets using Radial basis functions (RBF) neural network classifier. Critical issues like stability over time, ability to reject impostors and generalization to other datasets have been addressed. The results indicated that with only 35% of the derived coefficients the system performance not only can be preserved, but also it can be improved.