Mirchi Nykan, Warsi Nebras M, Zhang Frederick, Wong Simeon M, Suresh Hrishikesh, Mithani Karim, Erdman Lauren, Ibrahim George M
Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Division of Neurosurgery, Hospital for Sick Children, Department of Surgery, University of Toronto, Toronto, ON, Canada.
Front Hum Neurosci. 2022 Jun 27;16:913777. doi: 10.3389/fnhum.2022.913777. eCollection 2022.
Advances in intracranial electroencephalography (iEEG) and neurophysiology have enabled the study of previously inaccessible brain regions with high fidelity temporal and spatial resolution. Studies of iEEG have revealed a rich neural code subserving healthy brain function and which fails in disease states. Machine learning (ML), a form of artificial intelligence, is a modern tool that may be able to better decode complex neural signals and enhance interpretation of these data. To date, a number of publications have applied ML to iEEG, but clinician awareness of these techniques and their relevance to neurosurgery, has been limited. The present work presents a review of existing applications of ML techniques in iEEG data, discusses the relative merits and limitations of the various approaches, and examines potential avenues for clinical translation in neurosurgery. One-hundred-seven articles examining artificial intelligence applications to iEEG were identified from 3 databases. Clinical applications of ML from these articles were categorized into 4 domains: i) seizure analysis, ii) motor tasks, iii) cognitive assessment, and iv) sleep staging. The review revealed that supervised algorithms were most commonly used across studies and often leveraged publicly available timeseries datasets. We conclude with recommendations for future work and potential clinical applications.
颅内脑电图（iEEG）和神经生理学的进展使得人们能够以高保真的时间和空间分辨率研究以前难以触及的脑区。iEEG研究揭示了一种丰富的神经编码，它支持健康的脑功能，而在疾病状态下会失效。机器学习（ML）作为人工智能的一种形式，是一种现代工具，或许能够更好地解码复杂的神经信号并增强对这些数据的解读。迄今为止，已有多篇出版物将ML应用于iEEG，但临床医生对这些技术及其与神经外科手术的相关性的认识一直有限。本研究对ML技术在iEEG数据中的现有应用进行了综述，讨论了各种方法的相对优点和局限性，并探讨了神经外科临床转化的潜在途径。从3个数据库中确定了107篇研究人工智能在iEEG中应用的文章。这些文章中ML的临床应用分为4个领域：i）癫痫分析，ii）运动任务，iii）认知评估，以及iv）睡眠分期。综述显示，监督算法在各项研究中使用最为普遍，且经常利用公开可用的时间序列数据集。我们最后对未来工作和潜在临床应用提出了建议。
