A Pilot Study Using Machine Learning for Classification of Pain-Related Versus Non-Pain-Related Electroencephalographic Activity in Preterm Infants

Simple item page
dc.contributor.advisorPillai Riddell, Rebecca
dc.contributor.authorHamwi, Lojain
dc.date.accessioned2025-04-10T10:38:18Z
dc.date.available2025-04-10T10:38:18Z
dc.date.copyright2024-08-16
dc.date.issued2025-04-10
dc.date.updated2025-04-10T10:38:18Z
dc.degree.disciplinePsychology(Functional Area: Clinical-Developmental)
dc.degree.levelMaster's
dc.degree.nameMA - Master of Arts
dc.description.abstractEffective pain assessment and management are crucial to mitigate both immediate and long-term consequences of prolonged NICU stays. Accurately assessing pain in premature infants is challenging due to their inability to verbally communicate their pain, the potential judgement bias by caregivers, the lack of specificity in current pain assessment tools and time constraints in a busy hospital environment. This pilot study explores a machine learning approach to support pain assessment in neonatal care using cortical activity. The current study aims to test machine learning models that autonomously distinguishes non-pain related from pain-related cortical activity. The present dataset includes 72 preterm infants (27 females), born between 24- and 36-weeks gestational age, from two NICUs: Mount Sinai Hospital (Toronto, Canada) and University College London Hospital (London, UK). The primary outcome was to assess the accuracy of various machine learning models (XGBoost, Support Vector Machines, Random Forest, Logistic Regression, Convolutional Neural Networks) in distinguishing EEG features within a one-second pre-lance epoch (non-pain related) from a one-second post-lance epoch (pain-related). Performance metrics varied across post-menstrual age groups, reflecting developmental differences in EEG patterns. Machine learning algorithms can autonomously distinguish the one-second epoch immediately following a heel lance from the one-second epoch immediately preceding the procedure in preterm infants. Moreover, the performance of these algorithms improves with increasing postmenstrual age, demonstrating greater accuracy and reliability in older infants. This study provides a foundation for developing an autonomous and accurate tool for pain assessment in neonatal patients that can improve pain management practices in NICUs.
dc.identifier.urihttps://hdl.handle.net/10315/42717
dc.languageen
dc.rightsAuthor owns copyright, except where explicitly noted. Please contact the author directly with licensing requests.
dc.subject.keywordsInfant pain
dc.subject.keywordsPreterm infants
dc.subject.keywordsNICU
dc.subject.keywordsMachine learning
dc.subject.keywordsBrain activity
dc.subject.keywordsEEG
dc.titleA Pilot Study Using Machine Learning for Classification of Pain-Related Versus Non-Pain-Related Electroencephalographic Activity in Preterm Infants
dc.typeElectronic Thesis or Dissertation

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Lojain_Hamwi_2024_MA.pdf
Size:
952.72 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 2 of 2
No Thumbnail Available
Name:
license.txt
Size:
1.87 KB
Format:
Plain Text
Description:
Download
No Thumbnail Available
Name:
YorkU_ETDlicense.txt
Size:
3.39 KB
Format:
Plain Text
Description:
Download

Collections

Psychology (Functional Area: Clinical-Developmental)

All items in the YorkSpace institutional repository are protected by copyright, with all rights reserved except where explicitly noted.