Manual interpretation and classification of ECG signals lack both accuracy and reliability. These continuous time-series signals are more effective when represented as an image for CNN-based classification. A continuous Wavelet transform filter is used here to get corresponding images. In achieving the best result generic CNN architectures lack sufficient accuracy and also have a higher run-time. To address this issue, we propose an ensemble method of transfer learning-based models to classify ECG signals. In our work, two modified VGG-16 models and one InceptionResNetV2 model with added feature extracting layers and ImageNet weights are working as the backbone. After ensemble, we report an increase of 6.36% accuracy than previous MLP-based algorithms. After 5-fold cross-validation with the Physionet dataset, our model reaches an accuracy of 99.98%.
@article{arxiv.2207.00002,
title = {A Transfer-Learning Based Ensemble Architecture for ECG Signal Classification},
author = {Tareque Bashar Ovi and Sauda Suara Naba and Dibaloke Chanda and Md. Saif Hassan Onim},
journal= {arXiv preprint arXiv:2207.00002},
year = {2022}
}