A Hybrid Pain Assessment Approach with Stacked Autoencoders and Attention-Based CP-LSTM

Pain assessment is an integral part of healthcare since it enables the optimal management of patient well-being and the prompt administration of therapies. The ability to precisely diagnose pain is essential for ensuring appropriate medical attention and treatment. This study presents a novel pain categorization approach based on EEG (Electroencephalography) signals. A hybrid deep learning-based model is utilized in the study, which combines a Stacked Autoencoder for automated feature extraction and Chebyshev polynomial Long Short-Term Memory (CP-LSTM) with an attention mechanism for classification. The primary objective is to distinguish between two distinct pain states: ‘No Pain’ and ‘Pain’. The proposed model can detect the ‘Pain’ and ‘No Pain’ states of an individual with 99.32% and 98.91% accuracy, respectively. Experimental observations indicate an increase in delta wave power in the frontal and central cortex, as well as an increase in alpha wave power in the frontal, temporal, and occipital lobes during pain.