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arXiv · 2207.00002

A Transfer-Learning Based Ensemble Architecture for ECG Signal Classification

Tareque Bashar Ovi·Sauda Suara Naba·Dibaloke Chanda·Md. Saif Hassan Onim
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Abstract

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%.

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Tareque Bashar Ovi, Sauda Suara Naba, Dibaloke Chanda, Md. Saif Hassan Onim. 2022-06-21. A Transfer-Learning Based Ensemble Architecture for ECG Signal Classification. https://arxiv.org/abs/2207.00002

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