OBJECTIVE This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsEEG), structural magnetic resonance imaging (sMRI) or both. METHODS For the classification exercise, the ANNs had two architectures that included stacked (autoencoding) hidden layers recreating input data in the output. The classification was based on LORETA source estimates from rsEEG activity recorded with 10-20 montage system (19 electrodes) and standard sMRI variables in 89 ADD and 45 healthy control participants taken from a national database. RESULTS The ANN with stacked autoencoders and a deep leaning model representing both ADD and control participants showed classification accuracies in discriminating them of 80%, 85%, and 89% using rsEEG, sMRI, and rsEEG + sMRI features, respectively. The two ANNs with stacked autoencoders and a deep leaning model specialized for either ADD or control participants showed classification accuracies of 77%, 83%, and 86% using the same input features. CONCLUSIONS The two architectures of ANNs using stacked (autoencoding) hidden layers consistently reached moderate to high accuracy in the discrimination between ADD and healthy control participants as a function of the rsEEG and sMRI features employed. SIGNIFICANCE The present results encourage future multi-centric, prospective and longitudinal cross-validation studies using high resolution EEG techniques and harmonized clinical procedures towards clinical applications of the present ANNs.

中文翻译：

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堆叠自动编码器作为使用静息状态 EEG 和 MRI 测量的准确阿尔茨海默病分类支持的新模型

目标这项回顾性和探索性研究测试了人工神经网络 (ANN) 根据从静息状态脑电图 (rsEEG)、结构磁共振成像 (sMRI) 或两者中提取的输入变量检测阿尔茨海默病痴呆 (ADD) 患者的准确性。方法 对于分类练习，人工神经网络有两种架构，包括堆叠（自动编码）隐藏层，在输出中重新创建输入数据。该分类是基于 LORETA 源估计的 rsEEG 活动，使用 10-20 蒙太奇系统（19 个电极）和标准 sMRI 变量记录的 89 个 ADD 和 45 个来自国家数据库的健康对照参与者。结果 带有堆叠自动编码器和代表 ADD 和控制参与者的深度学习模型的人工神经网络在使用 rsEEG、sMRI 和 rsEEG + sMRI 特征区分他们时分别显示出 80%、85% 和 89% 的分类准确度。具有堆叠自动编码器和专用于 ADD 或控制参与者的深度学习模型的两个 ANN 使用相同的输入特征显示出 77%、83% 和 86% 的分类准确率。结论 根据所采用的 rsEEG 和 sMRI 特征，使用堆叠（自动编码）隐藏层的两种 ANN 架构在区分 ADD 和健康对照参与者方面始终达到中等至高度的准确度。意义 目前的结果鼓励未来的多中心、