论文标题
使用句子理解脑电图数据锁定综合症机器学习分类
Locked in Syndrome Machine Learning Classification using Sentence Comprehension EEG Data
论文作者
论文摘要
锁定综合征患者经常被误诊，并且由于与意识障碍，缺乏客观生物标志物和难以识别的发病机理而面临悲观预后。使用脑电图（EEG）数据，生物标志物在识别类似条件方面表现出希望。这些数据，特别是以事件相关电位（ERP）的形式，尽管在不同的应用中成功，但遭受了方法论的限制和解释障碍。这项工作中记录的研究探索了一个机器学习范式，该范式涉及从句子理解任务中检索到的N400 ERP数据，以应对这些障碍，并提出了一种新的辅助诊断工具，以解决意识和意识障碍。支持向量机（SVC）和随机森林分类器（RF）能够对具有乐观性能指标的无意识的人进行有意识的人进行分类。基于这些结果，提出的模型及其连续性为开发用于分类LIS患者的辅助诊断工具的宝贵机会，有助于诊断，改善预后，刺激恢复和降低死亡率。
Locked-in Syndrome patients are often misdiagnosed and face pessimistic prognosis because of similarities with disorders of consciousness, a lack of objective biomarkers and a difficult-to-recognize pathogenesis. Biomarkers show promise in identifying similar conditions, utilizing electroencephalography (EEG) data. This data, particularly in the form of event-related potentials (ERPs), while successful in varying applications, suffers from methodological constraints and interpretation obstacles. The study documented in this body of work explores a machine learning paradigm with regards to N400 ERP data retrieved from a sentence comprehension task to tackle these hindrances and proposes a new auxiliary diagnostic tool for LIS and possibly disorders of consciousness. A support vector machine (SVC) and a random forest classifier (RF) were able to classify conscious individuals from unconscious ones with optimistic performance metrics. Based on these results, the proposed models and continuations thereof present valuable opportunities for the development of an auxiliary diagnostic tool for the classification of LIS patients, aiding diagnosis, improving prognosis, stimulating recovery and reducing mortality rates.
