A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network

Ruofan Wang; Haodong Wang; Lianshuan Shi; Chunxiao Han; Qiguang He; Yanqiu Che; Li Luo

doi:10.3389/fnagi.2023.1160534

A novel framework of MOPSO-GDM in recognition of Alzheimer's EEG-based functional network

Front Aging Neurosci. 2023 Jun 29:15:1160534. doi: 10.3389/fnagi.2023.1160534. eCollection 2023.

Authors

Ruofan Wang^#¹, Haodong Wang^#¹, Lianshuan Shi¹, Chunxiao Han², Qiguang He¹, Yanqiu Che², Li Luo³

Affiliations

¹ School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, China.
² Tianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, China.
³ College of Agronomy, Sichuan Agricultural University, Chengdu, China.

^# Contributed equally.

Abstract

Background: Most patients with Alzheimer's disease (AD) have an insidious onset and frequently atypical clinical symptoms, which are considered a normal consequence of aging, making it difficult to diagnose AD medically. But then again, accurate diagnosis is critical to prevent degeneration and provide early treatment for AD patients.

Objective: This study aims to establish a novel EEG-based classification framework with deep learning methods for AD recognition.

Methods: First, considering the network interactions in different frequency bands (δ, θ, α, β, and γ), multiplex networks are reconstructed by the phase synchronization index (PSI) method, and fourteen topology features are extracted subsequently, forming a high-dimensional feature vector. However, in feature combination, not all features can provide effective information for recognition. Moreover, combining features by manual selection is time-consuming and laborious. Thus, a feature selection optimization algorithm called MOPSO-GDM was proposed by combining multi-objective particle swarm optimization (MOPSO) algorithm with Gaussian differential mutation (GDM) algorithm. In addition to considering the classification error rates of support vector machine, naive bayes, and discriminant analysis classifiers, our algorithm also considers distance measure as an optimization objective.

Results: Finally, this method proposed achieves an excellent classification error rate of 0.0531 (5.31%) with the feature vector size of 8, by a ten-fold cross-validation strategy.

Conclusion: These findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional interactions, and characterize brain functional abnormalities, which can improve the recognition efficiency of diseases. While improving the classification accuracy of application algorithms, we aim to expand our understanding of the brain function of patients with neurological disorders through the analysis of brain networks.

Keywords: Alzheimer's disease; EEG; complex network; feature selection; multi-objective optimization.

Grants and funding

This research was funded by the Natural Science Foundation of Tianjin (Grant No. 22JCYBJC00470), Tianjin Science and Popularization of Science and Research Development Project (Grant No. 22KPXMRC00170), Science and Technology Think Tank Youth Talent Program, China (Grant No. 20220615ZZ07110153), Tianjin Municipal Education Commission Scientific Research Program (Grant No. 2022KJ106), and the Tianjin Municipal Special Program of Talent Development for Excellent Youth Scholars (Grant No. TJTZJHQNBJRC-2-2).