DICEAM, 19009University Mediterranea of Reggio Calabria, Via Graziella Feo di Vito, 89124, Reggio Calabria, Italy.
Clin EEG Neurosci. 2023 Jan;54(1):51-60. doi: 10.1177/15500594211063662. Epub 2021 Dec 10.
An explainable Artificial Intelligence (xAI) approach is proposed to longitudinally monitor subjects affected by Mild Cognitive Impairment (MCI) by using high-density electroencephalography (HD-EEG). To this end, a group of MCI patients was enrolled at IRCCS Centro Neurolesi Bonino Pulejo of Messina (Italy) within a follow-up protocol that included two evaluations steps: T0 (first evaluation) and T1 (three months later). At T1, four MCI patients converted to Alzheimer's Disease (AD) and were included in the analysis as the goal of this work was to use xAI to detect individual changes in EEGs possibly related to the degeneration from MCI to AD. The proposed methodology consists in mapping segments of HD-EEG into channel-frequency maps by means of the power spectral density. Such maps are used as input to a Convolutional Neural Network (CNN), trained to label the maps as "T0" (MCI state) or "T1" (AD state). Experimental results reported high intra-subject classification performance (accuracy rate up to 98.97% (95% confidence interval: 98.68-99.26)). Subsequently, the explainability of the proposed CNN is explored via a Grad-CAM approach. The procedure detected which EEG-channels (i.e., head region) and range of frequencies (i.e., sub-bands) were more active in the progression to AD. The xAI analysis showed that the main information is included in the delta sub-band and that, limited to the analyzed dataset, the highest relevant areas are: the left-temporal and central-frontal lobe for Sb01, the parietal lobe for Sb02, the left-frontal lobe for Sb03 and the left-frontotemporal region for Sb04.
提出了一种可解释的人工智能（xAI）方法，通过使用高密度脑电图（HD-EEG）对轻度认知障碍（MCI）患者进行纵向监测。为此，在IRCCS Centro Neurolesi Bonino Pulejo of Messina（意大利）的一项随访方案中招募了一组 MCI 患者，该方案包括两个评估步骤：T0（第一次评估）和 T1（三个月后）。在 T1，有四名 MCI 患者转为阿尔茨海默病（AD），并被纳入分析，因为这项工作的目的是使用 xAI 来检测 EEG 中可能与从 MCI 到 AD 退化相关的个体变化。所提出的方法包括通过功率谱密度将 HD-EEG 片段映射到通道-频率图中。这些地图被用作卷积神经网络（CNN）的输入，该网络经过训练可将地图标记为“T0”（MCI 状态）或“T1”（AD 状态）。实验结果报告了较高的个体内分类性能（准确率高达 98.97%（95%置信区间：98.68-99.26%））。随后，通过 Grad-CAM 方法探索了所提出的 CNN 的可解释性。该过程检测到哪些 EEG 通道（即头部区域）和频率范围（即子带）在向 AD 进展中更活跃。xAI 分析表明，主要信息包含在 delta 子带中，并且在分析的数据集有限的情况下，最相关的区域为：左颞叶和中央额叶区域（Sb01），顶叶区域（Sb02），左额叶区域（Sb03）和左额颞叶区域（Sb04）。
