Early diagnosis of mild cognitive impairment and Alzheimer’s disease using multimodal feature-based deep learning models in a Chinese elderly population

Abstract

Background

Alzheimer’s disease (AD) and mild cognitive impairment (MCI) are progressive neurodegenerative disorders with no effective treatments currently, underscoring the urgent need for early diagnosis. Electroencephalography and event-related potentials (ERP) provide noninvasive, cost-effective methods with high temporal resolution for detecting cognitive decline, while traditional Chinese medicine (TCM) features such as body constitutions have been identified as risk factors for MCI. Recent developments in artificial intelligence (AI) especially deep learning architectures have further improved the diagnostic accuracies of AD and MCI. This study aimed to assess the efficacy of deep learning models based on fused ERP and TCM features in the cross-subject classification of cognitive impairment.

Methods

Visual oddball ERP tasks under Neutral, Happiness, or Sadness stimulus were conducted among 30 healthy controls (HC, 12 males and 18 females), 30 MCI (10 males and 20 females), and 30 AD (10 males and 20 females) patients. Deep learning models, including EEGNet, Convolutional Neural Network - Long Short-Term Memory, Graph Convolutional Network, (GCN), and multi-scale feature reconstruction GCN, were employed to extract differential entropy features from ERP data, and multilayer perceptron was utilized to extract features from TCM questionnaires. After feature fusion, 10-fold cross-subject binary (HC vs. MCI+AD; MCI vs. AD) and ternary (HC, MCI, AD) classification tasks were performed subsequently.

Results

GCN significantly outperformed other models in all three cross-subject classification tasks. In binary classification tasks distinguishing HC from MCI and AD, GCN achieved accuracies of 90.17 ± 5.58 %, 86.73 ± 2.34 %, and 84.73 ± 4.28 % under Neutral, Happiness, and Sadness, respectively. Similarly, in ternary classification of HC, MCI, and AD, GCN reached the highest accuracy of 72.67 ± 1.89 % under Neutral stimulus.

Conclusions

Leveraging fused ERP and TCM features, deep learning models have demonstrated robust cross-subject efficacy in the early diagnosis of cognitive decline. Particularly in distinguishing HC from MCI and AD, the performance of GCN was comparable to that of hematological biomarkers. Our study, therefore, highlights a reliable and effective AI-driven methodology for the early diagnosis of cognitive impairment in clinical settings.