SACNN: a spatial attentive 2D CNN for improved accuracy and interpretability in ECG image classification

Deep Learning models have shown strong performance in ECG analysis. However, many existing models operate as black-box systems or depend on post-hoc explainable techniques, limiting transparency. Moreover, attention-based mechanisms have focused on 1D ECG signals with limited exploration of ECG images.

In this research, we propose a Spatial Attention-based 2D Convolutional Neural Network (SACNN) with an intrinsic attention mechanism that enhances model transparency by highlighting regions emphasized during feature extraction. The incorporation of Spatial attention into the 2D-CNN within the learning process aided in visualizing image regions emphasized by the model without requiring post-hoc explainability methods. The experimental analysis showed classification performance with an accuracy of up to 0.98 on a single split and consistent performance with a mean accuracy of 0.93 under stratified k-fold cross-validation.

Statistical hypothesis testing confirmed that the performance improvements of SACNN over the evaluated baseline CNN models were statistically significant (p