Artifact detection in neonatal EEG using unsupervised machine learning
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Perustieteiden korkeakoulu |
Master's thesis
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Authors
Date
2017-04-03
Department
Major/Subject
Biomedical Engineering
Mcode
SCI3059
Degree programme
Master’s Programme in Life Science Technologies
Language
en
Pages
53+8
Series
Abstract
The neonatal electroencephalogram (EEG) is an important tool for assessing cortical activity in critically ill neonates. However, the EEG is often contaminated by artifacts such movement artifacts, electromyographic (EMG) artifacts from muscle activity and electrocardiographic (ECG) artifacts. These artifacts make visual inspection difficult and negatively influence the results of automated analysis. Even though several methods for automated neonatal EEG analysis have been developed, there is a significant lack of comprehensive artifact detection systems for the neonatal EEG. In this thesis, an automated artifact detection system based on a semi-supervised Gaussian mixture model (GMM) is presented. We examined the effects of feature set size, mixture number and the use of principal component analysis (PCA) as a pre-processor. Performance was assessed using the area under the receiver operating characteristic (AUC) and estimated using leave-one-patient-out cross-validation. The best-performing system was obtained with 23 features, 30 mixtures and no PCA (median AUC = 0.95, IQR: 0.83--0.99). EMG and movement artifacts were detected with the highest accuracy.Description
Supervisor
Parkkonen, LauriThesis advisor
Stevenson, NathanKeywords
EEG, artifact, Gaussian mixture model, classification, neonatal