Fabietti Marcos, Mahmud Mufti, Lotfi Ahmad, Leparulo Alessandro, Fontana Roberto, Vassanelli Stefano, Fasolato Cristina
IEEE Trans Neural Syst Rehabil Eng. 2023;31:2839-2848. doi: 10.1109/TNSRE.2023.3288835. Epub 2023 Jul 6.
Early diagnosis of Alzheimer's disease (AD) is a very challenging problem and has been attempted through data-driven methods in recent years. However, considering the inherent complexity in decoding higher cognitive functions from spontaneous neuronal signals, these data-driven methods benefit from the incorporation of multimodal data. This work proposes an ensembled machine learning model with explainability (EXML) to detect subtle patterns in cortical and hippocampal local field potential signals (LFPs) that can be considered as a potential marker for AD in the early stage of the disease. The LFPs acquired from healthy and two types of AD animal models (n = 10 each) using linear multielectrode probes were endorsed by electrocardiogram and respiration signals for their veracity. Feature sets were generated from LFPs in temporal, spatial and spectral domains and were fed into selected machine-learning models for each domain. Using late fusion, the EXML model achieved an overall accuracy of 99.4%. This provided insights into the amyloid plaque deposition process as early as 3 months of the disease onset by identifying the subtle patterns in the network activities. Lastly, the individual and ensemble models were found to be robust when evaluated by randomly masking channels to mimic the presence of artefacts.
阿尔茨海默病（AD）的早期诊断是一个极具挑战性的问题，近年来人们尝试通过数据驱动的方法来解决。然而，考虑到从自发神经元信号中解码高级认知功能存在固有的复杂性，这些数据驱动的方法受益于多模态数据的整合。这项工作提出了一种具有可解释性的集成机器学习模型（EXML），以检测皮质和海马局部场电位信号（LFP）中的细微模式，这些模式可被视为疾病早期AD的潜在标志物。使用线性多电极探针从健康动物和两种类型的AD动物模型（每种n = 10）获取的LFP，通过心电图和呼吸信号验证了其准确性。从LFP的时间、空间和频谱域生成特征集，并将其输入到每个域的选定机器学习模型中。通过后期融合，EXML模型实现了99.4%的总体准确率。通过识别网络活动中的细微模式，这为疾病发作3个月时的淀粉样斑块沉积过程提供了见解。最后，当通过随机屏蔽通道以模拟伪迹的存在进行评估时，发现个体模型和集成模型都具有鲁棒性。
