A novel three-channel deep learning model using feature fusion of spectrogram, HOG, and LBP features achieved superior ECG classification accuracy compared to existing algorithms.
A novel deep learning model using feature fusion of spectrograms, HOG, and LBP features improves ECG arrhythmia classification accuracy.
This paper introduces a novel deep learning model for ECG signal classification using feature fusion. The proposed methodology transforms the ECG time series into a spectrogram image using a short-time Fourier transform (STFT). This spectrogram is further processed to generate a histogram of oriented gradients (HOG) and local binary pattern (LBP) features. Three separate 2D convolutional neural networks (CNNs) then analyze these three image representations in parallel. To enhance performance, the extracted features are concatenated before feeding them into a gated recurrent unit (GRU) model. The proposed approach is extensively evaluated on two ECG datasets (MIT-BIH + BIDMC and MIT-BIH) with three and five classes, respectively. The experimental results demonstrate that the proposed approach achieves superior classification accuracy compared to existing algorithms in the literature. This suggests that the model has the potential to be a valuable tool for accurate ECG signal classification, aiding in the diagnosis and treatment of various cardiovascular disorders.
Eleyan et al. (Wed,) conducted a other in Arrhythmia. Three-channel deep learning model with feature fusion (CNNs and GRU) vs. Existing algorithms in the literature was evaluated on Classification accuracy. A novel three-channel deep learning model using feature fusion of spectrogram, HOG, and LBP features achieved superior ECG classification accuracy compared to existing algorithms.