Key result
A proposed deep learning and optimization-based methodology using EfficientNet-B0 and SABES achieved 99.31% accuracy in classifying five different classes of ECG signals.
A novel deep learning and optimization-based methodology achieved 99.31% accuracy in classifying five types of ECG beats.
Hypothesis-generating for automated ECG classification; prospective clinical validation required before practice change.
The work proposes a methodology for five different classes of ECG signals. The methodology utilises moving average filter and discrete wavelet transformation for the remove of baseline wandering and powerline interference. The preprocessed signals are segmented by R peak detection process. Thereafter, the greyscale and scalograms images have been formed. The features of the images are extracted using the EfficientNet-B0 deep learning model. These features are normalised using z-score normalisation method and then optimal features are selected using the hybrid feature selection method. The hybrid feature selection is constructed utilising two filter methods and Self Adaptive Bald Eagle Search (SABES) optimisation algorithm. The proposed methodology has been applied to the ECG signals for the classification of the five types of beats. The methodology acquired 99.31% of accuracy.
No takes yet. Share an insight, caveat, or question.
Sharma et al. (2024) studied Arrhythmia. Deep learning and optimisation-based methodology (EfficientNet-B0 and SABES) was evaluated on Classification accuracy of five types of beats. A proposed deep learning and optimization-based methodology using EfficientNet-B0 and SABES achieved 99.31% accuracy in classifying five different classes of ECG signals.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: