Yin Zixiao, Yu Huiling, Yuan Tianshuo, Smyth Clay, Anjum Md Fahim, Zhu Guanyu, Ma Ruoyu, Xu Yichen, An Qi, Gan Yifei, Merk Timon, Qin Guofan, Xie Hutao, Zhang Ning, Wang Chunxue, Jiang Yin, Meng Fangang, Yang Anchao, Neumann Wolf-Julian, Starr Philip, Little Simon, Li Luming, Zhang Jianguo
Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
Movement Disorder and Neuromodulation Unit, Department of Neurology, Charité-Campus Mitte, Charite-Universitatsmedizin Berlin, Chariteplatz 1, 10117, Berlin, Germany.
NPJ Digit Med. 2024 May 10;7(1):122. doi: 10.1038/s41746-024-01115-7.
Sleep disturbances profoundly affect the quality of life in individuals with neurological disorders. Closed-loop deep brain stimulation (DBS) holds promise for alleviating sleep symptoms, however, this technique necessitates automated sleep stage decoding from intracranial signals. We leveraged overnight data from 121 patients with movement disorders (Parkinson's disease, Essential Tremor, Dystonia, Essential Tremor, Huntington's disease, and Tourette's syndrome) in whom synchronized polysomnograms and basal ganglia local field potentials were recorded, to develop a generalized, multi-class, sleep specific decoder - BGOOSE. This generalized model achieved 85% average accuracy across patients and across disease conditions, even in the presence of recordings from different basal ganglia targets. Furthermore, we also investigated the role of electrocorticography on decoding performances and proposed an optimal decoding map, which was shown to facilitate channel selection for optimal model performances. BGOOSE emerges as a powerful tool for generalized sleep decoding, offering exciting potentials for the precision stimulation delivery of DBS and better management of sleep disturbances in movement disorders.
睡眠障碍严重影响神经系统疾病患者的生活质量。闭环深部脑刺激(DBS)有望缓解睡眠症状,然而,这项技术需要从颅内信号中自动解码睡眠阶段。我们利用了121例运动障碍患者(帕金森病、特发性震颤、肌张力障碍、亨廷顿舞蹈病和图雷特综合征)的夜间数据,这些患者同时记录了同步多导睡眠图和基底神经节局部场电位,以开发一种通用的、多类别的、针对睡眠的解码器——BGOOSE。即使存在来自不同基底神经节靶点的记录,这个通用模型在患者和疾病条件下的平均准确率也达到了85%。此外,我们还研究了皮层脑电图在解码性能中的作用,并提出了一种最优解码图谱,结果表明该图谱有助于为实现最优模型性能而进行通道选择。BGOOSE成为一种强大的通用睡眠解码工具,为DBS的精准刺激传递以及更好地管理运动障碍中的睡眠障碍提供了令人兴奋的潜力。