ECG biometric using 2D Deep Convolutional Neural Network
Digest of Technical Papers - IEEE International Conference on Consumer Electronics
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.
Siddartha Thentu, Renato Cordeiro, Youngee Park, and Nima Karimian. "ECG biometric using 2D Deep Convolutional Neural Network" Digest of Technical Papers - IEEE International Conference on Consumer Electronics (2021). https://doi.org/10.1109/ICCE50685.2021.9427616