## 6 Conclusions  
This work proposes the use of TL and KD with deep neural networks trained on large-scale ECG datasets for the purpose of AF classification in smaller PPG datasets. The objectives are to investigate the feasibility of a unified, modality-agnostic knowledge representation for AF classification and to validate performance measures during distillation and subsequent compression.  
We purport that using both raw signals and derived HRV/PRV features can increase the robustness of model behavior in free-living conditions. This can be particularly beneficial when adapting learned representations across modalities due to the implicit translation invariance to noise and motion artifacts.  
Empirically, we find that TL and KD enabled a much smaller model (32\(\times\) smaller) to achieve performance boosts of approximately 13.6% and 1.38% in terms of accuracy, specificity, sensitivity, F1 score, and MCC across two very different types of PPG datasets by leveraging and condensing innate information from training on a large ECG dataset. Additionally, we employ interpretability methods using Grad-CAM to elucidate the inner workings of the convolutional layers and estimate the contribution of HRV/PRV-derivedfeatures. We highlight that KD may not offer much additional utility if the available data quality and size are sufficiently high.  
Future research could involve the acquisition and testing of the proposed method with additional PPG datasets using foundational model architectures, performing quantization for edge device employment, and conducting longitudinal studies with a controlled cohort of patients to attain qualitative evidence of low-cost wearable monitoring.  
Conceptualization, R.A., A.S. and F.A.; data curation, Z.S.; investigation, J.R. and Z.S.; methodology, J.R.; project administration, R.A. and A.S.; resources, F.A. and A.S.; software, J.R.; supervision, R.A., A.S. and F.A.; validation, J.R.; writing--original draft, J.R. and Z.S.; writing--review and editing, R.A., A.S. and F.A. All authors have read and agreed to the published version of the manuscript.  
The work in this paper was supported, in part, by the Open Access Program from the American University of Sharjah. This paper represents the opinions of the author(s) and does not mean to represent the position or opinions of the American University of Sharjah.  
Not applicable.  
The ECG dataset adopted in this research is openly available from Physionet at [[https://doi.org/10.13026/C2](https://doi.org/10.13026/C2) MW2D]([https://doi.org/10.13026/C2](https://doi.org/10.13026/C2) MW2D) (accessed on 1 January 2022). The first PPG dataset analyzed in this research is openly available at [[https://www.synapse.org/#Synapse-syn2365056/wiki/608635](https://www.synapse.org/#Synapse-syn2365056/wiki/608635)]([https://www.synapse.org/#Synapse-syn2365056/wiki/608635](https://www.synapse.org/#Synapse-syn2365056/wiki/608635)) (accessed on 13 October 2023). The second PPG dataset analyzed in this research is openly available at [[https://ppg-beats.readthedocs.io/en/latest/datasets/mimic_perform_af/](https://ppg-beats.readthedocs.io/en/latest/datasets/mimic_perform_af/)]([https://ppg-beats.readthedocs.io/en/latest/datasets/mimic_perform_af/](https://ppg-beats.readthedocs.io/en/latest/datasets/mimic_perform_af/)) (accessed on 1 January 2024).  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} AF & Atrial Fibrillation \\ ANS & Autonomous Nervous System \\ Attn & Attention \\ BiLSTM & Bidirectional Long Short-Term Memory \\ CNN & Convolutional Neural Network \\ ECG & Electrocardiogram \\ GAN & Generative Adversarial Network \\ Grad-CAM & Gradient-weighted Class Activation Mapping \\ HRV & Heart Rate Variability \\ KD & Knowledge Distillation \\ MCC & Matthews Correlation Coefficient \\ MLKD & Multi-Level Knowledge Distillation \\ NSR & Normal Sinus Rhythm \\ PPG & Photoplethysmography \\ PRV & Pulse Rate Variability \\ \end{tabular}