A finer-grained high altitude EEG dataset for hypoxia levels assessment

The study reports on a high-altitude EEG dataset comprising 64-channel EEG signals from 23 subjects, aiming at achieving a finer-grained assessment of hypoxia levels. Four hypoxia levels were induced by creating a gradient of oxygen partial pressure through changes in altitude and external hypoxia stimulation. The dataset was collected in a hypoxic chamber that simulates altitude changes, allowing for a refined classification of different hypoxia levels based on ranges of oxygen saturation. The total recorded EEG data amounts to approximately 10.25 hours. Validation results indicate that the four hypoxia levels can be effectively recognized using EEG signals. Compared to binary classification, our fine-grained dataset allows for more precise detection of hypoxia levels. This dataset is anticipated to have significant research and practical value in developing accurate methods for identifying hypoxia levels. As a valuable and standardized resource, it will enable extensive analysis and comparison for researchers in the field of high-altitude hypoxia.

本研究报道了一套高空脑电图（Electroencephalogram, EEG）数据集，包含23名受试者的64通道脑电信号，旨在实现对缺氧程度的精细化评估。研究通过改变海拔高度与施加外部缺氧刺激，构建氧分压梯度，以此诱导出四种缺氧等级。该数据集采集于模拟海拔变化的低氧舱中，可基于血氧饱和度范围对不同缺氧等级进行精细化分类。总记录脑电数据时长约为10.25小时。验证结果表明，利用脑电信号可有效识别这四种缺氧等级。相较于二分类任务，本精细化数据集可实现对缺氧等级更为精准的检测。本数据集有望在开发精准的缺氧等级识别方法方面展现重要的研究与应用价值。作为一套宝贵且标准化的研究资源，它将为高空缺氧领域的研究人员提供开展广泛分析与对比的基础。