*Result*: Exploring the power of photoplethysmogram matrix for atrial fibrillation detection with integrated explainability.

*Atrial Fibrillation (AF) detection is paramount for cardiovascular health due to its potential complications. In this study, we investigate the utility of Photoplethysmogram (PPG) for continuous heart rate monitoring and detection of patients with AF. Our approach centres on creating PhotoplethysmoMatrices (PPMs) and leverages Explainable Artificial Intelligence (XAI) techniques to enable accurate AF patient classification with interpretability. Our method involves transforming PPG data into multiple PPM images, which represent aligned peaks within the PPG signal, presented as heatmaps. The diagnostic architecture is a lightweight and efficient Convolutional Neural Network (CNN) combined with attention mechanisms for model transparency. Remarkably, our approach achieves a 100% classification accuracy across multiple experiments, even with variations in the number of users and training images. Furthermore, the attention module underscores the significance of peak positioning and shifting in AF patient detection. Overall, our research makes a substantial contribution to the field of AF patient classification using PPG signals. The combination of image-based preprocessing techniques and explainable architectures enhances accuracy and interpretability, promising improved diagnostic capabilities in clinical settings. • PPG signals transformed into images (PPG matrices) for accurate AF classification. • Enhanced accuracy: Achieved 100% accuracy surpassing existing methods. • Explainability: Detects PPG's biomarkers over images in AF classification. • Outperformed other approaches in AF classification using images from PPG signals. [ABSTRACT FROM AUTHOR]*