用于精神科 EEG 数据分类的 Motif Discovery 框架
Motif Discovery Framework for Psychiatric EEG Data Classification
AI总结：
本文提出基于 motif discovery 的精神科 EEG 分类框架，利用治疗第 7 天脑电动态特征预测抗抑郁反应，并在多类精神疾病数据集上取得高精度。
摘要：
在当前的医疗实践中，由于抗抑郁药的显著效果存在延迟，正在接受抑郁症治疗的患者必须等待四到六周，临床医生才能评估药物反应。在更早阶段识别治疗反应非常重要，因为这可以减轻与治疗相关的情绪和经济负担。我们利用治疗第 7 天 EEG 记录的动态特性，将患者治疗反应预测视为一个分类问题。我们提出了一种新颖框架，该框架应用 motif discovery（模体发现）从 EEG 数据中提取有意义的特征，以区分抑郁症治疗应答者和无应答者。我们还将该框架应用于其他精神科 EEG 数据集的分类任务，即具有精神分裂症症状的患者、患有难治性癫痫的儿科患者，以及阿尔茨海默病和痴呆患者。我们在所有数据集中均取得了较高的分类精度。结果表明，EEG 的动态特性可在诊断以及最早于治疗第 7 天预测抑郁症治疗反应方面支持临床医生决策。据我们所知，我们的工作总体上是首个在抑郁症诊断中使用 motifs 的研究。
英文摘要：
In current medical practice, patients undergoing depression treatment must wait four to six weeks before a clinician can assess medication response due to the delayed noticeable effects of antidepressants. Identification of a treatment response at any earlier stage is of great importance, since it can reduce the emotional and economic burden connected with the treatment. We approach the prediction of a patient response to a treatment as a classification problem, by utilizing the dynamic properties of EEG recordings on the 7th day of the treatment. We present a novel framework that applies motif discovery to extract meaningful features from EEG data distinguishing between depression treatment responders and non-responders. We applied our framework also to classification tasks in other psychiatric EEG datasets, namely to patients with symptoms of schizophrenia, pediatric patients with intractable seizures, and Alzheimer disease and dementia. We achieved high classification precision in all data sets. The results demonstrate that the dynamic properties of the EEGs may support clinicians in decision making both in diagnosis and in the prediction depression treatment response as early as on the 7th day of the treatment. To our best knowledge, our work is the first one using motifs in the depression diagnostics in general.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
