ArticleEntropy (Basel, Switzerland)2025
Kolmogorov-Arnold and Long Short-Term Memory Convolutional Network Models for Supervised Quality Recognition of Photoplethysmogram Signals.
Article in Entropy (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- PFSKANs: A Novel Pixel-Level Feature Selection Model Based on Kolmogorov-Arnold Networks.Sensors (Basel, Switzerland) · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
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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Registered trials
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