A multimodal dataset for training deep learning models aimed at detecting and analyzing sleep apnea

Sleep Apnea Syndrome (SAS) is a serious respiratory disorder that can lead to a range of complications, including hypertension, arrhythmias, cognitive impairment, 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, ensuring its accuracy and reliability. Our objective is to provide a publicly available, standardized, and high-quality data resource for the development and application 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.

睡眠呼吸暂停综合征（Sleep Apnea Syndrome, SAS）是一种可引发多种并发症的严重呼吸系统疾病，并发症涵盖高血压、心律失常、认知功能障碍以及代谢紊乱。由于其症状具有隐匿性，患者往往难以自行察觉该病症，且临床筛查既耗时又耗费资源。为应对上述挑战，我们构建了一套融合多导睡眠图（Polysomnography, PSG）设备数据与同步音频录制内容的综合数据集。该数据集已由专业医疗人员基于多导睡眠图监测数据进行了严格标注，确保了其准确性与可靠性。我们的目标是提供一套可公开获取、标准化且高质量的数据资源，用于睡眠呼吸暂停综合征领域深度学习模型的开发与应用。本数据集旨在提升诊断的准确性与效率，同时推动该领域的前沿科学研究与技术创新。

搜集汇总

背景与挑战

背景概述

该数据集是一个多模态数据集，结合了多导睡眠图（PSG）设备和同步音频记录的数据，用于训练深度学习模型以检测和分析睡眠呼吸暂停综合征（SAS）。数据集经过医学专家严格注释，旨在提供公开、标准化和高质量的数据资源，以提升诊断准确性和效率。