Key result
A CNN architecture enriched with Kolmogorov-Arnold Network (KAN) layers effectively reduced the number of parameters and enhanced performance for PPG signal analysis.
Why the study?
PPG signals recover key physiological parameters, motivating the development of automated quality recognition models using neural network architectures.
CNN architectures enriched with KAN layers offer improved performance and reduced parameter count for automated quality recognition of PPG signals compared to traditional CNN-LSTM models.
Answers are grounded in this paper and cite their sources.
May support efficient PPG quality assessment in cardiac monitoring; leaves open validation in prospective clinical cohorts.
Photoplethysmogram (PPG) signals recover key physiological parameters as pulse, oximetry, and ECG. In this paper, we first employ a hybrid architecture combining the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) for the analysis of PPG signals to enable an automated quality recognition. Then, we compare its performance to a simpler CNN architecture enriched with Kolmogorov-Arnold Network (KAN) layers. Our results suggest that the usage of KAN layers is effective at reducing the number of parameters, while also enhancing the performance of CNNs when equipped with standard Multi-Layer Perceptron (MLP) layers.
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Mehrab et al. (2025) studied Photoplethysmogram (PPG) signal quality recognition. CNN architecture enriched with Kolmogorov-Arnold Network (KAN) layers vs. Hybrid architecture combining CNN and LSTM was evaluated on Automated quality recognition performance and number of parameters. A CNN architecture enriched with Kolmogorov-Arnold Network (KAN) layers effectively reduced the number of parameters and enhanced performance for PPG signal analysis.
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