Detection of OSA Through the Application of Deep Learning on Polysomnography Data

Abstract

paper presents a comprehensive study on the application of deep learning techniques to accurately detect sleep apnea. The study leverages a dataset obtained from the Sleep Laboratory of the Department of Chest Diseases of Yozgat Bozok University, with the aim of developing an effective decision support system capable of identifying cases of sleep disorders with high accuracy. The proposed methodology focusses on the use of deep neural networks (DNNs) to enhance the accuracy and reliability of sleep apnea detection. By employing meticulous data collection, preprocessing, and analysis, the study demonstrates the potential of DNNs to capture intricate and high-dimensional features within complex sleep data, allowing precise and reliable diagnosis. The experimental results showcase the effectiveness of the proposed DNN-based classifier design, achieving an accuracy of 96.48 %. The study's contributions lie in the enhancement of sleep disorder diagnosis through the integration of deep learning techniques, offering promising implications for clinical practice. Early detection of sleep disorders has the potential to significantly improve patient outcomes and overall quality of life and lays the foundation for further advancements in the field of sleep medicine.