Individual identification with high frequency ECG : preprocessing and classification by neural network

In this research, we proposed that high frequency component of HFECG was applicable biometric feature for new identification system. We developed identification method by using neural network (NN), and aimed at the improvement of the classification rate. Preprocessing prior to NN is performed by justification on time axis and normalization on amplitude. As a result, an average of 99% classification rate was obtained from 9 subjects. We also made an attempt to identify in shorter time by shifting of the HFECG by a few samples to NN.