用Mamba模型训练大脑电波基础模型，提升癫痫诊断准确率
Mentality: A Mamba-based Approach towards Foundation Models for EEG
- 基于Mamba的选通状态空间模型，自监督预训练后微调用于脑电信号分析
- 在独立测试集上实现0.72的AUROC，优于传统方法
- 适合需要高效处理高维非线性脑电数据的研究者和临床开发者
本研究探索了基于Mamba的选通状态空间模型作为脑电图（EEG）基础模型的潜力，以增强神经疾病诊断中的EEG分析。由于EEG具有噪声大、高维和非线性等特点，传统机器学习方法难以捕捉其复杂的时空动态。尽管深度学习在序列建模方面取得进展，但针对大规模、通用化表达的EEG模型仍较少。本文通过在包含发作与非发作脑电记录的大规模数据集上进行自监督重建预训练，再微调至癫痫检测任务，验证了该方法的有效性，在独立测试集上达到0.72的AUROC。这一成果为构建可临床应用的大规模脑电基础模型迈出关键一步。
原文摘要 · Abstract (English)
This work explores the potential of foundation models, specifically a Mamba-based selective state space model, for enhancing EEG analysis in neurological disorder diagnosis. EEG, crucial for diagnosing conditions like epilepsy, presents significant challenges due to its noisy, high-dimensional, and nonlinear nature. Traditional machine learning methods have made advances in automating EEG analysis but often fail to capture its complex spatio-temporal dynamics. Recent advances in deep learning, particularly in sequence modeling, offer new avenues for creating more generalized and expressive models capable of handling such complexities. By training a Mamba-based model on a large dataset containing seizure and non-seizure EEG recordings through a self-supervised reconstruction task followed by a seizure detection task, we demonstrate the model's effectiveness, achieving an AUROC of 0.72 on a held-out test set. This approach marks a significant step toward developing large-scale, clinically applicable foundation models for EEG data analysis.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。