Application of data mining on polynomial based approach for ECG biometric

In this paper, the application of data mining techniques on polynomial based approach for better electrocardiogram (ECG) authentication mechanism is presented. Polynomials being used for ECG data processing have a history of nearly two decades. Recently it has been bringing about promising solutions for heart beat recognition problem. General polynomial based approach are used in this research and by using the polynomial coefficients extracted as unique features from the ECG signals, data mining techniques was applied for person identification. A total of 18 ECG recordings from MIT/BIH Normal Sinus Rhythm database (NSRDB) were used for development and evaluation. QRS complexes from each dataset was divided into two parts, the training and the testing dataset which was used to prove the validity of the data mining technique applied. Experimental results was classified using Multilayer Perceptron (MLP) in order to confirm the identity of an individual and was compared with the previous research using polynomials without the use of data mining technique. Our experimentation on a public ECG database suggest that the proposed data mining technique on polynomial based approach significantly improves the identification accuracy by 96% as compared to 87% from the existing study.