Abstract
Sleep Apnea Syndrome (SAS) is a serious respiratory disorder that can lead to a range of complications, including hypertension, arrhythmias, cognitive impair- ment, and metabolic disturbances. Due to the insidious nature of its symptoms, patients often fail to recognize the condition, and clinical screening is both time- consuming and resource-intensive. To address these challenges, we have developed a comprehensive dataset that integrates data from Polysomnography (PSG) devices with synchronized audio recordings. This dataset has been rigorously annotated by expert medical professionals based on PSG monitoring data, ensur- ing its accuracy and reliability. Our objective is to provide a publicly available, standardized, and high-quality data resource for the development and applica- tion of deep learning models in the field of sleep apnea syndrome. This dataset is designed to enhance diagnostic accuracy and efficiency while promoting advanced scientific research and technological innovation in this domain.