ECG biometric using 2D Deep Convolutional Neural Network

We propose a novel multi-scale continuous wavelet transform feature method to accurately obtain micro-texture and multi-scale ECG characteristics and demonstrate how it could benefit from the state-of-the-art deep convolutional neural network techniques. In other words, we performed transfer learnin...

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Bibliographic Details
Published inProceedings of IEEE International Symposium on Consumer Electronics pp. 1 - 6
Main Authors Thentu, Siddartha, Cordeiro, Renato, Park, Youngee, Karimian, Nima
Format Conference Proceeding
LanguageEnglish
Published IEEE 10.01.2021
Subjects
Biometrics (access control)
Continuous wavelet transforms
Convolution
Deep learning
Electrocardiography
Frequency conversion
Transfer learning
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Summary:We propose a novel multi-scale continuous wavelet transform feature method to accurately obtain micro-texture and multi-scale ECG characteristics and demonstrate how it could benefit from the state-of-the-art deep convolutional neural network techniques. In other words, we performed transfer learning with popular CNN architectures such as InceptionV3, VGG16, VGG19, Inception ResNetV2, MobileNetV2, and Xception which have been trained on the ImageNet. Our proposed ECG biometric framework achieves an average identification rate of 99.96% on CEBDB, 99.47% on PTB dataset with 290 subjects. We also evaluate the effectiveness of the proposed algorithm with the other two public ECG datasets with diverse behaviors.
ISSN:2158-4001
DOI:10.1109/ICCE50685.2021.9427616