Automatic detection of Alzheimer's disease from EEG signals using an improved AFS-GA hybrid algorithm

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by energy diffusion and partial disconnection in the brain, with its main feature being an insidious onset and subtle clinical symptoms. Electroencephalogram (EEG) as a primary tool for assessing and aiding in the diagnosis of brain diseases has been widely used in AD detection. Accurate diagnosis is crucial for preventing the transition from early cognitive impairment to AD and providing early treatment for AD patients. This study aims to establish a hybrid model based on the Improved Artificial Fish Swarm Algorithm (IAFS) and Genetic Algorithm (GA)-IAFS-GA, to determine the optimal channel combination for AD detection under multiple EEG signals. Geometric features and complexity features of AD EEG signals were extracted using Second Order Difference Plot (SODP) and entropy analysis across the full frequency band. Subsequently, Pearson correlation was used for feature ranking, selecting the six least correlated features for each channel. The Relief algorithm was then used to fuse these selected features, with one fused feature representing one channel. Based on this, a feature selection optimization algorithm, IAFS-GA, combining the improved artificial fish swarm algorithm and genetic algorithm, was proposed. Finally, the feature combination was input into a Naive Bayes classifier for the identification of AD patients and normal controls. The feature combination was input into a Naive Bayes classifier for the identification of AD patients and normal controls. Using a five-fold cross-validation strategy across the entire frequency band, the classification accuracy reached 93.53%, with a sensitivity of 98.74%, specificity of 98.25%, and an AUC area of 97.82%. This framework can quickly select appropriate brain channels to enhance the efficiency of detecting AD and other neurological diseases. Moreover, it is the first time that an improved artificial fish swarm genetic combination algorithm and SODP features has been used for channel selection in EEG, proving to be an effective method for AD detection. It is based on SODP analysis, entropy analysis, and intelligent algorithms, which can assist clinicians in rapidly diagnosing AD, reducing the misdiagnosis rate of false positives, and expanding our understanding of brain function in patients with neurological diseases.