KIEE - The Transactions P of the Korean Institute of Electrical Engineers

1

D. Jeong, 2020, Artificial intelligence security threat, crime, and forensics: Taxonomy and open issues, IEEE Access, Vol. 8, pp. 184560-184574

2

J. W. Kim, P. K. Rhee, 2018, Image Recognition based on Adaptive Deep Learning, The Journal of the Institute of Internet, Broadcasting and Communication, Vol. 18, No. 1, pp. 113-117

3

L. Lu, J. Mao, W. Wang, G. Ding, Z. Zhang, Aug. 2020, A Study of Personal Recognition Method Based on EMG Signal, IEEE Transactions on Biomedical Circuits and Systems, Vol. 14, No. 4, pp. 681-691

4

H. S. Sin, C. Y. Hahm, N. K. Kim, M. K. Kim, S. H. Lee, Y. S. Kim, 2014, Trends of Emotional Information & Communication Technology, Electronics and Telecommunications Trends, Vol. 29, No. 5, pp. 30-39

5

A. Barros, D. Rosário, P. Resque, E. Cerqueira, 2019, Heart of IoT: ECG as biometric sign for authentication and identification, 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 307-312

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X. Jiang, et al., 2021, Cancelable HD-sEMG-Based Biometrics for Cross-Application Discrepant Personal Identification, IEEE Journal of Biomedical, Health Informatics, Vol. 25, No. 4, pp. 1070-1079

7

K. Rabuzin, M. Baca, M. Sajko, 2006, E-learning: Biometrics as a Security Factor, International Multi- Conference on Computing in the Global Information Technology, pp. 64-64

8

M. Ingale, R. Cordeiro, S. Thentu, Y. Park, N. Karimian, 2020, ECG biometric authentication: A comparative analysis, IEEE Access, Vol. 8, pp. 117853-117866

9

Q. Zhang, D. Zhou, X. Zeng, 2017, HeartID: A multiresolution convolutional neural network for ECG-Based biometric human identification in smart health applications, IEEE Access, Vol. 5, pp. 11805-11816

10

R. Bousseljot, D. Kreiseler, A. Schnabel, 1995., Nutzung der EKG-Signaldatenbank CARDIODAT der PTB über das Internet, Biomedizinische Technik, pp. 317

11

G. B. Moody, R. G. Mark., 2001, The impact of the MIT-BIH Arrhythmia Database, IEEE Engineering in Medicine and Biology Magazine, Vol. 20, No. 3, pp. 45-50

12

T. S. Lugovaya, 2005, Biometric human identification based on electrocardiogram

13

S. A. Israel, J. M.Irvine, A. Cheng, M. D. Wiederhold, B. K. Wiederhold, 2005, ECG to identify individuals, Pattern Recognition, Vol. 38, No. 1, pp. 113-142

14

M. Jahiruzzaman, A. B. M. A. Hossain, 2015, ECG based biometric human identification using chaotic encryption, 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), pp. 1-5

15

S. S. Abdeldayem, T. Bourlai, 2020, A novel approach for ECG-Based human identification using spectral correlation and deep learning, IEEE Transactions on Biometrics, Behavior, and Identity Science, Vol. 2, No. 1, pp. 1-14

16

R. D. Labati, E. Muñoz, V. Piuri, R. Sassi, F. Scotti, 2019, Deep-ECG: Convolutional neural networks for ECG biometric recognition, Pattern Recognition Letters, Vol. 126, pp. 78-85

17

Y. H. Byeon, K. C. Kwak, 2019, Pre-Configured Deep Convolutional Neural Networks with Various Time- Frequency Representations for Biometrics from ECG Signals, Applied Sciences, Vol. 9, No. 22, pp. 2076-3417

18

L. Biel, O. Pettersson, L. Philipson, P. Wide, 2001, ECG analysis: a new approach in human identification, IEEE Transactions on Instrumentation and Measurement, Vol. 50, No. 3, pp. 808-812

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D. Jyotishi, S. Dandapat, 2020, An LSTM-Based Model for Person Identification Using ECG Signal, IEEE Sensors Letters, Vol. 4, No. 8, pp. 1-4

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J. S. Kim, S. G. Kim, S. B. Pan, 2020, Personal recognition using convolutional nearal network with ECG coupling image, Journal of Ambient Intelligence and Humanized Computing, Vol. 11, pp. 1923-1932

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M. Hammad, P. Pławiak, K. Wang, U. R. Acharya, 2021, ResNet-Attention model for human authen ticationusing ECG signals, Expert Systems, Vol. 38, No. 6

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Y. H. Byeon, S. B. Pan, K. C. Kwak, 2019., Intelligent deep models based on scalograms of electrocardiogram signals for biometrics, Sensors, Vol. 19, No. 4

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K. El-Shennawy, 2014, Communication theory and signal processing for transform coding, Bentham Science Publishers

24

T. Oberlin, S. Meignen, V. Perrier, 2014, The fourier-based synchrosqueezing transform, 2014 IEEE International Conference on Acoustic,s Speech and Signal Processing (ICASSP), pp. 315-319

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A. Kumar, C. P. Gandhi, Y. Zhou, G. Vashishtha, R. Kumar, J. Xiang, 2020, Improved CNN for the diagnosis of engine defects of 2-wheeler vehicle using wavelet synchro-squeezed transform (WSST), Knowledge-Based Systems, Vol. 208

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A. Lumini, L. Nanni, 2019, Deep learning and transfer learning features for plankton classification, Ecological Informatics, Vol. 51, pp. 33-43

27

K. He, X. Zhang, S. Ren, J. Sun, 2016, Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition, pp. 770-778

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KIEEThe Transactions P ofthe Korean Institute of Electrical Engineers