## 5 Conclusions  
We evaluated ECG biometrics using pre-configured models of CNNs with various time-frequency representations. Biometrics technology records a person's physical or behavioral characteristics in a digital signal via a sensor and analyzes it to identify the person. Biometrics offer great convenience to human lifestyles throughout society. An ECG signal is obtained by detecting and amplifying a minute electrical signal flowing on the skin using a noninvasive electrode when the heart muscle depolarizes at  
Figure 16: Comparison between the mean accuracies of various models based on CNN.  
Figure 15: Comparison between the mean accuracies of the different time-frequency representations.  
each heartbeat. In biometrics, the ECG is especially advantageous in security applications because the heart is located within the body and moves while the subject is alive. However, a few body states generate noisy biometrics. The analysis of signals in the frequency domain has a robust effect on the noise. As the ECG is noise-sensitive, various studies have applied time-frequency transformations that are robust to noise, with CNNs achieving a good performance in image classification. Various studies have applied the time-frequency representations of 1D ECG signals to 2D CNNs. Combinations of the time-frequency representations and CNN deep models have not been investigated. In this study, we employed MFCC, spectrogram, log spectrogram, mel spectrogram, and scalogram time-frequency transforms and the VGGNet, GoogLeNet, ResNet, and DenseNet deep CNN models. The PTB-ECG and CU-ECG databases were used in this study. The MFCC accuracies were 0.45%, 2.60%, 3.90%, and 0.25% higher than the spectrogram, log spectrogram, mel spectrogram, and scalogram accuracies, respectively. The Xception accuracies were 3.91%, 0.84%, and 1.14% higher than the VGGNet-19, ResNet-101, and DenseNet-201 accuracies, respectively. The mean accuracies of PCA-L2 and PCANet were 97.81 and 98.54 in PTB-ECG, 88.75 and 90.42 in CU-ECG, and 2.4 and 84.78 in MIT-BIH-arrhythmia-ECG, respectively. The Xception in PTB-ECG was 0.1% higher than PCA-L2 and 0.72% lower than PCANet. The Xception in CU-ECG was 3.38% and 1.71% higher than PCA-L2 and PCANet, respectively. The Xception with time-frequency representation showed close accuracy in PTB-ECG, which was easily successfully classified. However, the Xception outperformed other methods in CU-ECG, which was not easily successfully classified. Further studies would focus on new and better time-frequency representations.  
Conceptualization, K.-C.K. and Y.-H.B.; methodology, K.-C.K. and Y.-H.B.; software, K.-C.K. and Y.-H.B.; validation, K.-C.K. and Y.-H.B.; formal analysis, K.-C.K. and Y.-H.B.; investigation, K.-C.K. and Y.-H.B.; resources, K.-C.K.; data curation, K.-C.K.; writing, original draft preparation, Y.-H.B.; writing, review and editing, K.-C.K.; visualization, K.-C.K. and Y.-H.B.; supervision, K.-C.K.  
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2017R1A6A1A03015496). This work was supported by the Human Resources Program in Energy Technology of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), which was granted financial resources from the Ministry of Trade, Industry, and Energy (No. 20174030201620).  
The authors declare no conflicts of interest.