State-space models (SSMs) have garnered attention for effectively processing long data sequences, reducing the need to segment time series into shorter intervals for model training and inference. Traditionally, SSMs capture only the temporal dynamics of time series data, omitting the equally critical spectral features. This study introduces EEG-SSM, a novel state-space model-based approach for dementia classification using EEG data. Our model features two primary innovations: EEG-SSM temporal and EEG-SSM spectral components. The temporal component is designed to efficiently process EEG sequences of varying lengths, while the spectral component enhances the model by integrating frequency-domain information from EEG signals. The synergy of these components allows EEG-SSM to adeptly manage the complexities of multivariate EEG data, significantly improving accuracy and stability across different temporal resolutions. Demonstrating a remarkable 91.0 percent accuracy in classifying Healthy Control (HC), Frontotemporal Dementia (FTD), and Alzheimer's Disease (AD) groups, EEG-SSM outperforms existing models on the same dataset. The development of EEG-SSM represents an improvement in the use of state-space models for screening dementia, offering more precise and cost-effective tools for clinical neuroscience.

翻译：状态空间模型（SSMs）因能有效处理长数据序列而受到关注，减少了为模型训练和推理而将时间序列分割为较短区间的需求。传统上，SSMs仅捕获时间序列数据的时域动态，忽略了同样关键的频域特征。本研究提出了EEG-SSM，一种基于状态空间模型、利用脑电图数据进行痴呆症分类的新方法。我们的模型包含两个主要创新部分：EEG-SSM时域组件和EEG-SSM频域组件。时域组件旨在高效处理不同长度的脑电序列，而频域组件则通过整合脑电信号的频域信息来增强模型。这些组件的协同作用使EEG-SSM能够熟练处理多变量脑电数据的复杂性，显著提高了模型在不同时间分辨率下的准确性和稳定性。在区分健康对照组、额颞叶痴呆症和阿尔茨海默病组时，EEG-SSM表现出91.0%的卓越准确率，优于同一数据集上的现有模型。EEG-SSM的开发代表了在利用状态空间模型进行痴呆症筛查方面的进步，为临床神经科学提供了更精确且更具成本效益的工具。