  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%.
