A New Approach for Personal Identification Based on dVCG

We propose a new approach to personal identification using derived vectorcardiogram (dVCG). The dVCG was calculated from recorded ECG using inverse Dower transform. Twenty-one features were extracted from the resulting dVCG. To analyze the effect of each feature and to improve efficiency while maintaining the performance, we performed feature selection using the Relief-F algorithm using these 21 features. Each set of the eight highest ranked features and all 21 features were used in SVM learning and in tests, respectively. The classification accuracy using the entire feature set was 99.53 %. However, using only the eight highest ranked features, the classification accuracy was 99.07 %, indicating only a 0.46 % decrease in accuracy compared with the accuracy achieved using the entire feature set. Using only the eight highest ranked features, the conventional ECG method resulted in a 93 % recognition rate, whereas our method achieved >99 % recognition rate, over 6 % higher than the conventional ECG method. Our experiments show that it is possible to perform a personal identification using only eight features extracted from the dVCG.