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Application of machine learning and functional data analysis in classification and clustering of functional near infrared spectroscopy signal in response to noxious stimuli
Dissertation   Open access

Application of machine learning and functional data analysis in classification and clustering of functional near infrared spectroscopy signal in response to noxious stimuli

Ahmad Pourshoghi
Doctor of Philosophy (Ph.D.), Drexel University
Sep 2015
DOI:
https://doi.org/10.17918/etd-7184
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Abstract

Near infrared spectroscopy Machine learning--Mathematical models Biomedical Engineering
The main objective of this PhD research has been to utilize machine learning techniques on near infrared spectroscopy (NIRS) signals, for the development of highly accurate and clinically practical biomarkers for the objective assessment of pain perception. While advances in medical imaging technology have significantly improved the scientific knowledge in regards to the brain's response to noxious stimuli, there remains an unmet clinical need for a practical, inexpensive tool for the reliable and objective assessment of pain perception. Even though functional imaging modalities such as fMRI and PET scans deliver superior spatial information, they are not readily accessible for routine clinical use. On the other hand NIRS is non-invasive, safe, portable and affordable with a short setup time. These features make NIRS ideal for clinical applications. In this thesis we used the cold pressor test to induce different levels of pain in healthy subjects while the NIRS signal was recorded from the frontal regions of the brain. We extracted 54 features from each dataset and used machine learning techniques, logistic regression and support vector machine, to classify the signals based on the self-reported pain scores. To select the model for machine learning, we developed our feature selection algorithm based on a RFE-SVM (recursive feature elimination - support vector machine) method to find subsets of feature space with the highest classification capability. Through this process we identified a subset of 10 features which could distinguish high-pain from low-pain stimuli with an accuracy of 85% (Leave-one-out cross validation). Moreover we applied functional data analysis on the collected NIRS data and converted discrete samples to continuous curves. This time we used the same RFE-SVM method on the coefficients of fDA bases (as opposed to extracted features) and we achieved 94% of accuracy to classify low-pain high-pain signals. Then using machine learning techniques (k-means and hierarchical clustering) we found clusters in the data which covered low pain and high pain groups with an accuracy of 91.2%. The center of these clusters can represent the prototype NIRS response of that pain level. Our approaches provided trial-by-trial predictions of pain level from NIRS measurement for each individual (as opposed to methods based on responses averaged across many trials and subjects), and thus, represent a step towards the goal of establishing an objective clinical bio marker of pain perception. Further refinement of proposed methods, including incorporating more datasets and employing other noxious stimuli, is required to make the NIRS technique a powerful clinical tool for pain assessment.

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