QRS-complex of ECG-based biometrics in a two-level classifier

This research is based on an ECG biometrics system which segments the QRS-complex, extracts the non-fiducial features and sends the data to a two-level classifier. For spectral analysis, the discrete Fourier transform (DFT), and discrete cosine transform (DCT) were used to transform the signal, before principal component analysis (PCA) is used to reduce the feature vectors. From here, statistical parameters were computed for the classifier, where the first level is denoted called feature matching (FM) and the second level is the Neural Networks algorithm (NN). The system is tested on two databases. Database I consists of 45 subjects with 10 recordings each (recorded on the same day) while Database II consists of 35 subjects with 20 recordings each (recorded on separate days). The accuracy measures were is 99.176% and 96.67% respectively.