Pain Assessment Using Multi-Kernel-FCN-LSTM and Haemoglobin Difference in fNIRS

This study investigates the effectiveness of various machine learning and deep learning models for automated pain detection using functional near-infrared spectroscopy (fNIRS) data from the AI4Pain Grand Challenge dataset. Four different near-infrared spectroscopy metrics – oxygenated haemoglobin (HbO2), deoxygenated haemoglobin (HHb), total haemoglobin (HT), and haemoglobin difference (HbDiff) – were investigated to determine their contributions to pain assessment and identify which metric offers the most reliable performance. Across all models, both traditional and deep learning, HbDiff consistently outperformed the other metrics in terms of classification accuracy. The multi-kernel fully convolutional network hybrid with long short-term memory (MK-FCN-LSTM) model, particularly when utilising the HbDiff metric, achieved superior performance with a binary classification accuracy of 64.73%. These findings suggest that haemoglobin difference may provide more sensitive and reliable features for pain assessment, highlighting its potential as a key biomarker in fNIRS-based pain detection systems.