Abstract
In this paper, we propose personal identification method based on Convolutional Neural Networks (CNN) by various two-dimensional (2D) transform of Electrocardiogram (ECG) signals. For this purpose, various 2D time-frequency representation are peformed by Short-Time Fourier Transform (STFT), Fourier Synchrosqueezed Transform (FSST), and Wavelet Synchrosqueezed Transform (WSST) from one-dimensional ECG signals. The individual identification performance is achieved by transfer learning based on the pretrained GoogleNet and ResNet-101. The performance of experimental results are compared by the well-known PTB-ECG database.
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