Human Electrocardiogram for Biometrics Using DTW and FLDA

This paper proposes a new approach for person identification and novel person authentication using single lead human Electrocardiogram. Nine Feature parameters were extracted from ECG in spatial domain for classification. For person identification, Dynamic Time Warping (DTW) and Fisher´s Linear Discriminant Analysis (FLDA) with K-Nearest Neighbor Classifier (NNC) as single stage classification yielded a recognition accuracy of 96% and 97% respectively. To further improve the performance of the system, two stage classification techniques have been adapted. In two stage classifications FLDA is used with k-NNC at the first stage followed by DTW classifier at the second stage which yielded 100% recognition accuracy. During person authentication we adapted the QRS complex based threshold technique. The overall performance of the system was 96% for both legal and intruder situations is verified for MIT-BIH normal database size of 375 recording from 15 individual ECG.