MDEEG-IMPA: Integrating multidimensional EEG features with an improved MPA for high-precision diagnosis neurodegenerative disease.

Abstract

Current clinical differentiation of Alzheimer's disease (AD) and frontotemporal dementia (FTD) suffers from a misdiagnosis rate exceeding 40% due to overlapping symptomatology, and existing Electroencephalography (EEG)-based tools inadequately integrate multidimensional features or lack adaptive optimization. We aimed to develop a framework combining periodic and aperiodic EEG features with adaptive optimization for high-precision differential diagnosis. We proposed a dual-branch neural network integrating multi-dimensional electroencephalogram (MDEEG) features with an improved Marine Predator Algorithm (IMPA). The MDEEG-IMPA model combines Convolutional Neural Network (CNN)-based power spectral density (PSD) feature extraction with a fully connected branch for aperiodic parameters (1/f offset and exponent), enhanced by IMPA for adaptive feature weighting. The model was evaluated on resting-state EEG from 88 subjects (36 AD, 23 FTD, 29 healthy controls (HC)). MDEEG-IMPA achieved 99.20% accuracy in discriminating AD from FTD (Recall = 98.82%, F1-score = 99.02%), substantially outperforming comparative methods including STEADYNnet (84.59%) and SVM (93.5%). The MDEEG-IMPA model demonstrated robust performance in discriminating between AD and HC (Accuracy = 98.11%) and between FTD and HC (Accuracy = 98.95%). Ablation studies confirmed that 96.3% of the improvement in performance originated from the synergy between features and algorithms. The MDEEG-IMPA framework provides a reliable, high-precision computer-assisted diagnostic solution for neurodegenerative diseases with overlapping clinical presentations. By integrating periodic and aperiodic EEG features within an optimized multi-branch architecture, this work demonstrates that multidimensional electrophysiological characterization combined with adaptive optimization can substantially improve differential diagnostic accuracy, offering significant potential for clinical translation in early and accurate identification of AD and FTD.