Transparent EEG Analysis: Leveraging Autoencoders, Bi-LSTMs, and SHAP for Improved Neurodegenerative Diseases Detection

This study explores the use of deep learning techniques for classifying EEG signals in the context of Alzheimer’s disease (AD) and frontotemporal dementia (FTD). We propose a novel classification pipeline that combines autoencoders for feature extraction and bidirectional long short-term memory (Bi-LSTM) networks for temporal analysis of EEG data. Given the challenges of processing high dimensional EEG signals, we employed an Autoencoder to reduce the data’s dimensionality while preserving key features relevant for diagnosis. The Bi-LSTM model effectively captured the temporal dependencies in the EEG signals, essen- tial for detecting subtle neural changes indicative of AD and FTD. Furthermore, we leveraged SHapley Additive exPlanations (SHAP) to interpret the model’s predictions, providing transparency and ensuring that the contributions of individual features in the decision-making process are understood. The results demonstrate the efficacy of this approach, achieving an accuracy of 98%, while maintaining interpretability, making it a promising tool for clinical applications in the diagnosis of neurodegenerative diseases.