利用预训练的视觉变压器来使用原始休息状态EEG对酒精使用障碍进行分类
bioRxiv : the preprint server for biology
|February 6, 2026
概括
深度学习模型显示了使用电脑电图 (EEG) 数据诊断酒精使用障碍 (AUD) 的潜力. 虽然准确性不高,但这种方法为开发用于AUD的新神经生理诊断工具提供了基础.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 精神病学是一个精神病学.
背景情况:
- 酒精使用障碍 (AUD) 是一种广泛的神经精神疾病,影响着数百万人,但缺乏客观的诊断生物标志物.
- 目前对AUD的诊断方法有限,这凸显了对新型神经生理学工具的需求.
研究的目的:
- 研究深度学习的有效性,特别是EEGViT模型,在使用原始静止电脑图 (EEG) 数据对AUD患者进行分类.
- 探索基于变压器的精神病分类模型的潜力,并开发基于EEG的诊断工具.
主要方法:
- 利用来自"酒遗传学合作研究" (COGA) 的大量数据集,包括来自2,710名参与者的5,402个EEG记录.
- 应用了人口统计匹配和低样本,以管理混因素和阶级不平衡,保持原始的EEG特征.
- 采用了混合深度学习架构EEGViT,用于端到端对AUD,CUD和OUD进行分类,并根据性别和年龄分层分析.
主要成果:
- AUD深度学习模型实现了大约56%的整体分类准确度,性别之间存在差异 (54%的男性,58%的女性).
- 对于大麻使用障碍 (CUD) 和阿片类药物使用障碍 (OUD) 的模型显示了更高的准确性,约为63%.
- 时间分析显示,在以后的EEG记录间隔中,模型性能有所改善,这表明了动态神经模式.
结论:
- 基于变压器的深度学习模型显示,使用原始EEG数据对AUD进行分类是有前途的,尽管目前的准确性不高.
- 这些发现为开发针对AUD和其他物质使用障碍的客观,基于EEG的诊断工具提供了一个基本步骤.
- 需要进一步的研究和模型改进,以提高精神病诊断的准确性和临床实用性.
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