A Multichannel Continuous Clinical Electromyography Dataset from Neurosurgery

In this dataset, we present a six-channel EMG dataset obtained from five cranial nerves during the cerebellopontine angle tumor surgery from 11 patients using the technology of cIONM. It’s the first dataset to automatically recognize or forecast EMG patterns during cIONM. We also spent time labeling and classifying the data so that the real intraoperative EMG data are more generalizable and clinically meaningful compared to the previous guided, fixed-posture EMG data performed in the laboratory. This dataset can be used to develop and validate more deep-learning-based or machine-learning-based algorithms, models, or tools for monitoring neurological function related to surgery and to study the mechanisms and effects of nerve injury or repair during surgery. In addition, the dataset can provide useful information for medical practice to improve the safety and success of surgery.本数据集包含11名桥小脑角肿瘤手术患者的5条颅神经的六通道肌电图（Electromyography, EMG）数据，采集采用了颅部术中神经生理监测（cranial Intraoperative Neurophysiological Monitoring, cIONM）技术。本数据集是首个可用于自动识别或预测cIONM术中肌电模式的数据集。研究团队对数据进行了标注与分类，使得相较于此前实验室中在受控固定姿势下采集的肌电数据，本真实术中肌电数据具备更强的泛化性与临床价值。本数据集可用于开发、验证更多基于深度学习或机器学习的手术相关神经功能监测算法、模型与工具，同时可用于研究术中神经损伤或修复的机制与效果。此外，本数据集可为临床实践提供有效信息，助力提升手术安全性与成功率。