Development and comparison of EEG analysis pipelines for diagnosis of mild traumatic brain injuries
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Perustieteiden korkeakoulu |
Master's thesis
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Authors
Date
2023-05-15
Department
Major/Subject
Biomedical Engineering
Mcode
SCI3059
Degree programme
Master’s Programme in Life Science Technologies
Language
en
Pages
48 + 19
Series
Abstract
Mild Traumatic Brain Injuries (mTBI) are very common and can have a significant impact on an individual’s life. The symptoms of mTBI, which include headache, fatigue, sleep alterations, dizziness and difficulty in concentrating, may be more severe than the injury’s "mild" label suggests. Although most patients recover promptly, one in eight will suffer prolonged symptoms for weeks, months or possibly, throughout their whole lifetime. Understanding the mechanisms of mTBI is important for promoting individual health and well-being, but diagnosing remains challenging due to the large individual variability in symptoms, and the limitations of brain imaging techniques. Identifying objective biomarkers could increase diagnostic accuracy and improve clinical outcomes. Studies indicate that computational methods combined with neuroimaging could increase the diagnostic accuracy, by recognising subtle abnormalities in neurophysiological activity thus differentiating mTBI patients from healthy subjects. Machine learning techniques are frequently used in computational pipelines, and as these pipelines become more complex, they start to resemble software packages. To ensure that the results are reliable and the workflows are reproducible, it is important to follow computational best practices. In this Master’s Thesis, a well-documented software package for classifying mTBI patients was designed and developed, following best standards from computational research. A pipeline was constructed to process, analyse, and classify electroencephalographic (EEG) data applying a reproducible approach, based on different machine learning models. The software has been made publicly available at GitHub as a Python package: https://github.com/BioMag/mtbi_meeg. The Thesis describes the software development process of the pipeline, along with the performance metrics from different classifiers when utilising EEG data of 35 mTBI patients and 36 healthy controls, measured in BioMag Laboratory, Helsinki University Hospital. The pipeline can be further expanded to incorporate ther imaging modalities for more comprehensive analyses.Description
Supervisor
Renvall, HannaThesis advisor
Liljeström, MiaHeikkinen, Verna
Keywords
electroencephalography, mTBI, neuroimaging, software, Python