Automated detection of artefacts in neonatal EEG with residual neural networks. (September 2021)

• Record Type: Journal Article
• Authors: Webb, Lachlan; Kauppila, Minna; Roberts, James A.; Vanhatalo, Sampsa; Stevenson, Nathan J.
• Is Part Of: Computer methods and programs in biomedicine. Volume 208(2021)
• Publication Date: 2021-09
• Subjects: Artefact detection -- Electroencephalogram -- Residual Neural Network -- Brain monitoring -- Neonatal intensive care unit; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106194
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We applied a Residual Deep Neural Network as part of an artefact detection algorithm in neonatal electroencephalograms. The algorithm shows high accuracy in identifying artefactual data in general and for specific artefact types. EEG channels near the top of the head are less prone to artefact. Abstract: Background and objective: To develop a computational algorithm that detects and identifies different artefact types in neonatal electroencephalography (EEG) signals. Methods: As part of a larger algorithm, we trained a Residual Deep Neural Network on expert human annotations of EEG recordings from 79 term infants recorded in a neonatal intensive care unit (112 h of 18-channel recording). The network was trained using 10 fold cross validation in Matlab. Artefact types included: device interference, EMG, movement, electrode pop, and non-cortical biological rhythms. Performance was assessed by prediction statistics and further validated on a separate independent dataset of 13 term infants (143 h of 3-channel recording). EEG pre-processing steps, and other post-processing steps such as averaging probability over a temporal window, were also included in the algorithm. Results: The Residual Deep Neural Network showed high accuracy (95%) when distinguishing periods of clean, artefact-free EEG from any kind of artefact, with a median accuracy for individual patient of 91% (IQR: 81%-96%). The accuracy in identifying the five different types of artefacts ranged from 57%-92%, with electrode pop being the hardest to detect and EMG being the easiest. This reflected the proportion of artefact available in the training dataset. Misclassification as clean was low for each artefact type, ranging from 1%-11%. The detection accuracy was lower on the validation set (87%). We used the algorithm to show that EEG channels located near the vertex were the least susceptible to artefact. Conclusion: Artefacts can be accurately and reliably identified in the neonatal EEG using a deep learning algorithm. Artefact detection algorithms can provide continuous bedside quality assessment and support EEG review by clinicians or analysis algorithms.