Development of an EEG artefact detection algorithm and its application in grading neonatal hypoxic-ischemic encephalopathy

Objective: The primary aim of this study is to develop and evaluate algorithms for neonatal EEG artefact detection. The secondary aim is to subsequently assess its application as a post-processing routine for automated EEG grading of background abnormalities in neonatal hypoxic-ischemic encephalopathy (HIE). Methods: A database of neonatal EEG with expertly annotated artefacts was used to train and validate machine learning models to automatically identify EEG epochs containing artefacts. Three approaches were developed and compared, specifically, a simple threshold-based digital signal processing (DSP) method, a machine learning method, and a deep learning method. The artefact detection classifier was subsequently assessed as a post-processing tool to assist in the application of automated EEG grading of HIE. A new deep learning model for grading the EEG was developed by training an existing network on a large, multi-centre dataset. The artefact detection algorithm was integrated into the grading algorithm through a post-processing routine. Results: Using a database containing 19 h of EEG from 51 patients with per-channel and per-second annotations of artefacts, a deep learning convolutional neural network solution achieved best performance for artefact detection with an area under the operating characteristic curve (AUC) of 0.84, compared to an AUC of 0.68 and 0.82 for a DSP method and a random-kernel ridge-classifier model, respectively. The automated EEG grading algorithm was trained and tested on 653 h of EEG from 181 patients, which achieved an accuracy of 82.8 % (95 % CI: 80.5 % to 85.2 %). The percentage of detected artefacts in the misclassified epochs was not statistically different (p = 0.568) compared to that of correctly classified epochs. Using artefact detection, a small number of epochs were removed from grading, resulting in a minor increase in accuracy for the EEG grading algorithm from 82.6 % to 83.6 %. Conclusion: Deep learning methods achieved highest classification performance for neonatal EEG artefact detection, although a ridge classifier using random kernels achieved comparable performance without significant parameter tuning or training time. The inclusion of artefact detection in automated EEG grading does not significantly improve accuracy in our curated dataset, but does allow for a quality measure to be presented alongside the automated EEG grades which may increase user confidence in its real-world application