Optimal CNN Model for Obstructive Sleep Apnea Detection using Particle Swarm Optimization

Abstract

Obstructive Sleep Apnea (OSA) is a prevalent sleep disorder with significant health risks. It is characterized by the narrowing of the upper airway during sleep, leading to vibrations in the airway structures and the production of snoring sounds. Recently, Convolutional Neural Networks (CNNs) have been leveraged to extract meaningful features from snoring sound data, enabling early and accurate detection of OSA. The effectiveness of these neural network optimizations depends on the starting values of the model, the gradient algorithm used, and the complexity of the problem. This study introduces an improved Particle Swarm Optimization (PSO) strategy that linearly adjusts the learning rate coefficient to enhance accuracy and convergence speed. Our approach was evaluated on a collected and pre-processed dataset based on the PSG-Audio database. Experimental results demonstrate that our method significantly outperforms the conventional optimization algorithm and existing PSO techniques, achieving a remarkable accuracy of 99.1%. These findings confirm the potential of our optimized model for OSA detection.