Automated Detection And Quantification Of Pain Using Electroencephalography

Effective pain assessment and management strategies are needed to better manage pain. In addition to self-report, an objective pain assessment system to detect, quantify, and track the intensity of pain reduces the uncertainty of treatment outcome and provides a reliable benchmark for longitudinal evaluation of pain therapies. With electroencephalography (EEG) gaining traction as a reliable tool for characterizing brain regions active during pain, this work presents the development of robust and accurate machine learning algorithms on neuroimaging data using EEG to detect and quantify tonic thermal pain. To quantify pain, a random forest model was trained to using time-frequency wavelet representations of independent components obtained from EEG data. The mean classification accuracy for predicting pain on an independent test subject for a range of 1-10 is 89.45%, highest among existing state of the art quantification algorithms for EEG, demonstrating the potential of this tool to be used clinically to help improve chronic pain treatment. A temporally pain-specific biomarker using EEG was developed using EEG microstates to evaluate their specificity to pain compared to rest and two non-rest conditions evoking similar responses. Multifractal analyses on the microstate sequence showed that microstate interactions during pain were significantly more stable across time scales compared to non-painful conditions, but significantly more chaotic compared to resting state. A pain detection algorithm using deep learning techniques was constructed utilizing non-orthogonal temporal dependencies between microstates. Each branch of the deep learning network was trained to differentiate between pain and a non-painful condition to increase the specificity of the final algorithm to pain. The resulting algorithm improved on the state of the art by 14%, scoring 90.67% in terms of specificity to various levels of pain, compared to non-painful stimuli. Stacking this deep-learning pain detection algorithm on top of the pain quantification algorithm showed a 10% improvement in terms of F-score over the state of the art in pain quantification algorithms. This is an encouraging step forward in developing a clinically feasible tool that can detect, record, quantify and longitudinally compare the intensities of pain in patients to better aid the development of effective therapies to manage pain.

Author dc:creator

• Vijayakumar, Vishal

• electroencephalography
• machine learning
• pain

Language dc:language.iso

en

Handle dc:identifier.uri

http://hdl.handle.net/11299/215103

OAI identifier oai:identifier

oai:conservancy.umn.edu:11299/215103