Performance Comparison Between Transform-Based Deep Learning Approaches for ECG Signal Classification - Alboghbaish, Ebrahim; Eleyan, Alaa; Eleyan, Gulden (International Conference on Electrical and Electronics Engineering, 2024)
In this paper, we studied and compared the benefits of applying transformation as a preprocessing step to the input of the deep learning algorithms. A comparison was conducted between the discrete wavelet transform (DWT) and fast Fourier transform (FFT) as feature extractors for the ECG signals. The generated feature vectors were fed to a convolutional neural network and long short-term memory (CNN-LSTM) model for classification. Extensive experiments were executed on ECG signals collected from MIT-BIH and BIDMC databases with 3 classes. The obtained results showed the combined power of transformation and deep learning which led to better classification accuracies.