基于脑电图的大型模型，提升癫痫与精神分裂症检测准确率
NeuroDx-LM: A Clinical Large-Scale Model for EEG-based Neurological Disorder Detection
- 通过时频自适应嵌入捕捉脑电信号复杂模式
- 分阶段训练策略使癫痫和精神分裂症检测达最新水平
- 适合临床神经疾病辅助诊断研究者使用
在脑电图（EEG）上预训练的大规模模型在神经系统疾病检测等临床应用中展现出潜力。然而，其实际部署面临标注数据有限及临床表现不佳的挑战。为此，我们提出NeuroDx-LM，一种专为脑电图神经疾病检测设计的新颖大规模模型。关键贡献包括：(i) 选择性时频嵌入机制，可自适应捕捉脑电信号中的复杂时序与频谱模式；(ii) 基于特征感知的渐进式训练策略，分两阶段优化特征表示：第一阶段学习脑电活动的基本判别特征；第二阶段进一步提取更精细的特征以实现精准诊断。我们在CHB-MIT和精神分裂症数据集上评估了NeuroDx-LM，分别在癫痫与精神分裂症检测任务中达到当前最优性能。结果表明，基于脑电图的大规模模型具有显著提升临床适用性的潜力。代码已公开于https://github.com/LetItBe12345/NeuroDx-LM。
原文摘要 · Abstract (English)
Large-scale models pre-trained on Electroencephalography (EEG) have shown promise in clinical applications such as neurological disorder detection. However, the practical deployment of EEG-based large-scale models faces critical challenges such as limited labeled EEG data and suboptimal performance in clinical scenarios. To address these issues, we propose NeuroDx-LM, a novel large-scale model specifically designed for detecting EEG-based neurological disorders. Our key contributions include (i) a Selective Temporal-Frequency Embedding mechanism that adaptively captures complex temporal and spectral patterns in EEG signals; and (ii) a Progressive Feature-Aware Training strategy that refines feature representation in a two-stage process. In the first stage, our model learns the fundamental discriminative features of EEG activities; in the second stage, the model further extracts more specialized fine-grained features for accurate diagnostic performance. We evaluated NeuroDx-LM on the CHB-MIT and Schizophrenia datasets, achieving state-of-the-art performance in EEG-based seizure and schizophrenia detection, respectively. These results demonstrate the great potential of EEG-based large-scale models to advance clinical applicability. Our code is available at https://github.com/LetItBe12345/NeuroDx-LM.
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