用轻量模型提取脑电特征，精准区分阿尔茨海默病患者与健康人。
DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features
- 通过时空分块编码，仅用29万参数捕捉脑电信号关键特征。
- 在274人数据集上达100%准确率，比现有方法提升1.41%-15.35%。
- 模型极小，适合临床快速筛查，尤其适合资源有限场景。
阿尔茨海默病（AD）的早期检测至关重要，及时干预可改善患者预后。基于脑电图（EEG）的诊断因其无创、易获取且成本低而受到关注，但面临数据稀缺、深度学习精度不足及专家解读耗时等问题。本文提出一种新型轻量化高精度模型 DeepTokenEEG，用于区分AD患者、其他神经系统疾病患者及健康对照者。该模型采用时空分块编码器，仅需0.29百万参数即可有效捕捉时频域中的AD生物标志物。在包含180例AD患者和94名健康对照的274人联合数据集上，该方法在特定频段最高准确率达100%，较现有最优方法提升1.41%-15.35%。结果表明，DeepTokenEEG具备早期筛查潜力，其紧凑结构也利于实际部署。
原文摘要 · Abstract (English)
The detection of Alzheimers disease (AD) is considered crucial, as timely intervention can improve patient outcomes. Electroencephalogram (EEG)-based diagnosis has been recognized as a non-invasive, accessible, and cost-effective approach for AD detection; however, it faces challenges related to data availability, accuracy of modern deep learning methods, and the time-consuming nature of expert-based interpretation. In this study, a novel lightweight and high-performance model, DeepTokenEEG, was designed for the diagnosis of AD and the classification of EEG signals from AD patients, individuals with other neurological conditions, and healthy subjects. Unlike traditional heavy-weight models, DeepTokenEEG ultilizes spatial and temporal tokenizer that effectively captures AD-related biomarkers in both temporal and frequency domain with only 0.29 million paramaters. Trained in a combined dataset of 274 subjects, including 180 AD cases, and 94 healthy controls, the proposed method achieves a maximum recorded accuracy of 100% on specific frequency bands, representing an improvement of 1.41-15.35% over state-of-the-art methods on the same dataset. These results indicate the potential of DeepTokenEEG for early detection and screening of AD, with promising applicability for deployment due to its compact size.
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