Effective Feature Extraction of ECG for Biometric Application

Biometric systems performing identity recognition based upon extracted informative data from an individual are vital for security applications. The vital characteristics of an ECG signal depend upon its Characteristic points' P, Q, R, S and T. In this paper, an effective feature extraction method is proposed, in which for each record of ECG, the best 6-PQRST fragments are extracted according to priority basis and their positions are normalized. A total of 72 different features are calculated, finally the performance of feature set is examined and compared using ANN. The proposed algorithm is tested for MIT-BIH ECG ID database signals.