## 4 Experimental Results
### Experimental Results  
The experiment was performed using a computer with the following specifications: Nvidia GeForce GTX 1080 Ti, Intel i7-6850K central processing unit at 3.60 GHz, Windows 10 64-bit operating system, and 64 GB random-access memory. In this study, ECG biometrics using pre-configured models of the CNN with various time-frequency representations were evaluated. The signals were preprocessed to reduce the noise, and the R peak points were detected to normalize the center point of the ECG data. In other words, one data point was extracted based on each detected R peak point. However, the number of R peaks detected for each recording was different, as each person had a different heart rate, and the detection rate of the R peak was different for each signal. To construct  
Figure 9: Environment for acquiring the Chosun University (CU)-ECG signals. the same amount of data for each class, the total number of R peak points detected for each class was calculated. The class was excluded when the number of detected R peaks was too small. When the number of detected R peaks was large, a few detected R peaks were removed to ensure the same amount of data in each class. Considering only the data from lead I, a frame length of 784 centered on the detected R peak point was obtained. The data were then transformed to the time-frequency representations. Here, the spectrogram, log spectrogram, mel spectrogram, scalogram, and MFCC were considered as the time-frequency representations. The transformed ECG data were 2D, which could be applied to the 2D CNNs. Figure 10 shows the process of making the input image from the raw ECG signal. The pre-configured models based on the 2D CNN were the VGGNet, Xception, ResNet, and DenseNet models discussed earlier. The 2D ECG image was input to the CNN, and the neural network was trained to classify its identity. The depth of the neural network in the VGGNet, ResNet, and DenseNet models was 19, 101, and 201, respectively. We did not consider a potential issue from the variability from the selection of the ECG sensor devices. A different ECG sensor device (or product) would change the shape of the ECG signals and accuracy. PTB-ECG is an open access database and includes ECG data from 290 people. However, the data from 211 people were used to construct the PTB-ECG database as the ECG data from the remaining 79 had only few R peak points. The R peak points from 211 people were compared and adjusted to ensure that the maximum number of data per class was 120. The database size of PTB-ECG was 784 \(\times\) 25, 320 (120 data/class \(\times\) 211 classes). The row refers to the dimension of the data, while the column indicates the number of data. To train the CNNs, PTB-ECG was divided into the training, validation, and test datasets with ratios of 0.45, 0.05, and 0.5, respectively. To shorten the training time, the training set was composed of 45% normally less than other literature works in the field of machine learning, and a larger ratio for the training set results in too high accuracy to compare performance. The sizes of the training, validation, and test datasets were 784 \(\times\) 11,394, 784 \(\times\) 1266, and 784 \(\times\) 12,660, respectively. Figure 11 shows the structure of PTB-ECG. The CNNs were trained using a mini-batch size of 30, an initial learning rate of 0.0001, the Adam (adaptive moment estimation) training optimizer, and an epoch varied according to the model. PCA (principle component analysis)-L2 was executed by entering vectors reshaped from the diminished input image and measuring Euclidean distance (L2). Figure 10: Process of making the input image from the raw ECG signal. The PCA dimension was 20. Table 1 shows the accuracies achieved by PCA-L2 using time-frequency representations on PTB-ECG. PCANet [42] is a neural network that has a CNN architecture based on PCA. PCANet was executed with 4 as the patch size, 4 filters, 4 as the block size, and 0.5 as the block overlap ratio.