Abstract
Personal identification method using the Electrocardiogram (ECG) signal is an active research area since the ECG signal cannot be forged and can be acquired without active awareness by the subject. In this paper, we propose a personal recognition system using the 2-D coupling image of the ECG signal. The proposed system uses the 2-D coupling image generated from three periods of the ECG signal as input data to the network whose design is based on a Convolutional Neural Network (CNN) that is specialized for image processing. Waveform of the 2-D coupling image which is the input data to the network cannot be visually confirmed and it has the advantage of being able to augment the QRS-complex which is a personal unique information. We confirm recognition performance of 99.2% from the experiment result for the proposed personal recognition system using MIT-BIH data.
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Acknowledgements
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2017R1A6A1A03015496) and the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2018R1A2B6001984).
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Kim, J.S., Kim, S.H. & Pan, S.B. Personal recognition using convolutional neural network with ECG coupling image. J Ambient Intell Human Comput 11, 1923–1932 (2020). https://doi.org/10.1007/s12652-019-01401-3
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DOI: https://doi.org/10.1007/s12652-019-01401-3