Symbolic representations of time series applied to biometric recognition based on ECG signals

One reason for researching new biometric modalities is to improve the capabilities of security systems against threats. Biometric modalities based on biomedical signals, in particular the electrocardiogram signal (ECG), have been widely adopted. These can be represented by time series. However, in this context, a critical issue is how to extract features from ECG signals effectively. Several techniques have been put forward regarding the best way to represent time series, in particular techniques that are based on symbolic values, and significant results have been achieved by means of these techniques for addressing different types of problems. In this paper, we present twenty symbolic representations of time series applied to the extraction of nonfiducial features from ECG signals that aim at biometric recognition. In addition, we put forward three novel symbolic representations, namely, Representation based on Kmeans (R-Kmeans), Symbolic Aggregate approXimation based on Kmeans (SAX-Kmeans), and Extended Symbolic Aggregate approXimation based on Kmeans (ESAX-Kmeans). Experimental results conducted on two publicly available datasets indicate that the novel representations can improve the performance of recognition compared with the others.