Analysis Of Ecg Biosignal Recognition For Client Identifiction

The most common application for a recognition system of speech signal, finger print, iris, etc. are used for biometric applications. While other biometric signals like electrocardiogram (ECG) and the Heart Sound (HS) are generally used to identify cluster-related diseases. Nonetheless, performance of a traditional biometric system can be easily compromised as it is prone to spoof attack. This paper proposes a unimodal biometric security system that is based on ECG. Physiological biometrics characteristic are based on a human body's, such as the hand geometry, face, palm, ECG and even brain signal. The biosignal data collected by a biometric system would initially be segmented. The Mel-Frequency Cepstral Coefficients (MFCC) method is used for extracting each segmented feature. The Hidden Markov Model (HMM) is used to model the client, and categorize unknown input based on the model. The recognition system involved training and testing of the collected features, known as Client Identification (CID). In this paper, 20 clients were tested with this developed system. The best overall performance for 20 clients at 16 kHz was 71.4% for ECG trained at 50% of the training data, while the worst overall performance was 66.6% for 30% training data.