通用时序模型可有效提取脑电图特征，助力医疗诊断。
Are foundation models useful feature extractors for electroencephalography analysis?
- 用通用时序模型提取脑电图特征，替代专用模型。
- 在年龄预测与癫痫检测任务中表现接近专业模型。
- 适合数据少的临床场景，降低对大样本依赖。
基础模型在自然语言处理和计算机视觉中的成功，促使人们探索其在时间序列分析中的应用。尽管通用时间序列模型在多种任务中表现良好，但在数据有限的医学场景中的有效性仍待验证。本文针对脑电图（EEG）分析，评估通用时序模型在年龄预测、癫痫发作检测及临床相关事件分类任务中的表现，并与专用脑电模型对比其诊断性能与特征提取质量。结果表明，通用模型表现具有竞争力，能有效捕捉与年龄和疾病相关的生物标志物特征。这些发现说明，基础时序模型可减少对大规模特定任务数据集和模型的依赖，具备临床实用价值。
原文摘要 · Abstract (English)
The success of foundation models in natural language processing and computer vision has motivated similar approaches in time series analysis. While foundational time series models have proven beneficial on a variety of tasks, their effectiveness in medical applications with limited data remains underexplored. In this work, we investigate this question in the context of electroencephalography (EEG) by evaluating general-purpose time series models on age prediction, seizure detection, and classification of clinically relevant EEG events. We compare their diagnostic performance against specialised EEG models and assess the quality of the extracted features. The results show that general-purpose models are competitive and capture features useful to localising demographic and disease-related biomarkers. These findings indicate that foundational time series models can reduce the reliance on large task-specific datasets and models, making them valuable in clinical practice.
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