Kolmogorov-Arnold and Long Short-Term Memory Convolutional Network Models for Supervised Quality Recognition of...

Area of Science:

• Biomedical Signal Processing
• Artificial Intelligence in Healthcare
• Physiological Monitoring

Background:

• Photoplethysmogram (PPG) signals are crucial for extracting physiological data like pulse, oximetry, and ECG.
• Automated quality recognition of PPG signals is essential for reliable health monitoring.
• Deep learning architectures offer potential for advanced PPG signal analysis.

Purpose of the Study:

• To develop and evaluate a hybrid Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) architecture for automated PPG signal quality recognition.
• To compare the performance of the hybrid CNN-LSTM model against a simpler CNN architecture augmented with Kolmogorov-Arnold Network (KAN) layers.
• To assess the efficacy of KAN layers in improving CNN performance and reducing model complexity.

Main Methods:

• Implementation of a hybrid CNN-LSTM model for PPG signal analysis.
• Development of a comparative CNN model incorporating KAN layers.
• Evaluation of model performance based on automated quality recognition metrics.
• Analysis of parameter reduction achieved by KAN layers.

Main Results:

• The hybrid CNN-LSTM architecture demonstrated effectiveness in automated PPG signal quality recognition.
• CNNs enhanced with KAN layers showed improved performance compared to standard CNNs with Multi-Layer Perceptron (MLP) layers.
• KAN layers were found to be effective in reducing the number of parameters within the CNN architecture.

Conclusions:

• Hybrid deep learning models, such as CNN-LSTM, are suitable for automated PPG signal quality assessment.
• Kolmogorov-Arnold Network (KAN) layers offer a promising approach to enhance CNN efficiency and performance in physiological signal analysis.
• The integration of KAN layers presents a viable strategy for developing more streamlined and effective AI models for PPG data.