Electrocardiogram (ECG) is a new biometric trait that has an advantage as it is an internal modality which indicates to be difficult of counterfeiting. The ECG records the heart activity and it is unique as different individuals have distinct heart structure. Hence, it provides vital information for differentiating one individual from another. One of the techniques to extract the salient information from the ECG signal is by using discrete wavelet transform (DWT). However, in order to develop a reliable ECG based biometric system using DWT, there are many parameters that need to be determined. In this study, we study the effect of different parameters used for de-noising orders, threshold levels and type of mother wavelets to the final authentication performance of our developed system. For the authentication stage, Support Vector Machine (SVM) is used as classifier. The system performance is evaluated based on Genuine Acceptance Rate (GAR), False Acceptance Rate (FAR) and Equal Error Rate (EER) by using the scores given by the SVM. The combination of de-noising order 3, heursure threshold method and db6 mother wavelet yields the top performance with GAR of 92.5% at FAR of 5% and EER of 6.9499% have been achieved. At the end of this paper, a simple ECG based security system is then constructed so as to show the feasibility of this study to be implemented in real-time situation.