Pain Detection with fNIRS-Measured Brain Signals: A Personalized Machine Learning Approach Using the Wavelet Transform and Bayesian Hierarchical Modeling with Dirichlet Process Priors
Name
1907.12830.pdf
Description
Submitted version
Size
2.68 MB
Format
Adobe PDF
Checksum (MD5)
97f545e75587ffb2de62ddc0c90d345b
Author(s)
Lopez-Martinez, Daniel
Peng, Ke
Lee, Arielle
Borsook, David
Picard, Rosalind W.
Date Issued
2019
Journal
2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2019
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Lopez-Martinez, Daniel, Peng, Ke, Lee, Arielle, Borsook, David and Picard, Rosalind. 2019. "Pain Detection with fNIRS-Measured Brain Signals: A Personalized Machine Learning Approach Using the Wavelet Transform and Bayesian Hierarchical Modeling with Dirichlet Process Priors." 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2019.
Version
Original manuscript
Abstract
© 2019 IEEE. Currently self-report pain ratings are the gold standard in clinical pain assessment. However, the development of objective automatic measures of pain could substantially aid pain diagnosis and therapy. Recent neuroimaging studies have shown the potential of functional near-infrared spectroscopy (fNIRS) for pain detection. This is a brain-imaging technique that provides non-invasive, long-term measurements of cortical hemoglobin concentration changes. In this study, we focused on fNIRS signals acquired exclusively from the prefrontal cortex, which can be accessed unobtrusively, and derived an algorithm for the detection of the presence of pain using Bayesian hierarchical modelling with wavelet features. This approach allows personalization of the inference process by accounting for inter-participant variability in pain responses. Our work highlights the importance of adopting a personalized approach and supports the use of fNIRS for pain assessment.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Media Laboratory
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1109/ACIIW.2019.8925076
