The early detection of Alzheimer’s disease can potentially make eventual treatments more effective. This work presents a deep learning model to detect early symptoms of Alzheimer’s disease using synchronization measures obtained with magnetoencephalography. The proposed model is a novel deep learning architecture based on an ensemble of randomized blocks formed by a sequence of 2D-convolutional, batch-normalization and pooling layers. An important challenge is to avoid overfitting, as the number of features is very high (25755) compared to the number of samples (132 patients). To address this issue the model uses an ensemble of identical sub-models all sharing weights, with a final stage that performs an average across sub-models. To facilitate the exploration of the feature space, each sub-model receives a random permutation of features. The features correspond to magnetic signals reflecting neural activity and are arranged in a matrix structure interpreted as a 2D image that is processed by 2D convolutional networks.

The proposed detection model is a binary classifier (disease/non-disease), which compared to other deep learning architectures and classic machine learning classifiers, such as random forest and support vector machine, obtains the best classification performance results with an average F1-score of 0.92. To perform the comparison a strict validation procedure is proposed, and a thorough study of results is provided.

早期发现阿尔茨海默病可能会使最终的治疗更有效。这项工作提出了一种深度学习模型，可以使用脑磁图获得的同步测量来检测阿尔茨海默病的早期症状。所提出的模型是一种新颖的深度学习架构，它基于由一系列 2D 卷积、批量归一化和池化层形成的随机块集合。一个重要的挑战是避免过度拟合，因为与样本数量（132 名患者）相比，特征数量非常多（25755）。为了解决这个问题，该模型使用了一组相同的子模型，所有子模型都共享权重，最后阶段对子模型进行平均。为了促进特征空间的探索，每个子模型接收特征的随机排列。

所提出的检测模型是一个二元分类器（疾病/非疾病），与其他深度学习架构和经典机器学习分类器（如随机森林和支持向量机）相比，以平均 F1-score 获得最佳分类性能结果0.92。为了进行比较，提出了严格的验证程序，并提供了对结果的彻底研究。