Obstructive Sleep Apnea Syndrome Identification Using CNN-LSTM Hybrid Model

Abstract

Obstructive Sleep Apnea (OSA) is a common sleep problem. It causes breathing issues during sleep. This disturbs oxygen intake and sleep patterns. Detecting OSA early and accurately is important for treatment. In our study, we look into using advanced deep learning for OSA detection. We focus on Long Short-Term Memory (LSTM) networks, Convolutional Neural Networks (CNN), and a new combined method. We also check the impact of different functions and methods on OSA detection accuracy. Our tests reveal some interesting facts. The Nadam optimizer gives the best OSA detection at 95.22% accuracy. The combined CNN and LSTM method achieves 95.38% accuracy. Using LSTM with the sigmoid function, we get a 93.80% accuracy. These results stress the importance of choosing suitable models and methods for OSA detection. Our study helps improve diagnostic tools for OSA. These findings can aid in early and improved treatment of OSA, giving people a better quality of life and health.