# A Transfer-Learning Based Ensemble Architecture for ECG Signal Classification Source: [A Transfer-Learning Based Ensemble Architecture for ECG Signal Classification]() Collection index: [llms.txt]() Data updated: 2025-10-26T06:52:33.836Z Authors: Tareque Bashar Ovi, Sauda Suara Naba, Dibaloke Chanda, Md. Saif Hassan Onim Published: 2022-06-21T15:14:59.000Z ## 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%. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()