A machine learning artefact detection method for single-channel infant event-related potential studies

Simon Marchant ¹, Marianne van der Vaart ¹, Kirubin Pillay ¹

⁰Department of Paediatrics, University of Oxford, Oxford, United Kingdom.

Journal of Neural Engineering +

|

June 26, 2024

View abstract on PubMed

Summary

This study developed an automated method to detect artefacts in single-channel electroencephalographic (EEG) data from neonates. The new approach matches manual review accuracy, improving infant EEG analysis for research and clinical use.

Area Of Science

Neuroscience
Biomedical Engineering
Clinical Research

Background

Automated artefact detection in neonatal electroencephalography (EEG) is crucial for faster, more reproducible clinical research.
Existing methods are unsuitable for short, single-channel infant EEG epochs due to data limitations and infant vulnerability.
Developing a specialized automated method is necessary for this specific application.

Purpose Of The Study

To create and evaluate an automated method for detecting artefacts in 1500 ms, single-channel infant EEG epochs.
To address the limitations of current artefact detection techniques in neonatal EEG analysis.
To enhance the utility of EEG in neonatal clinical and research settings.

Main Methods

A dataset of 410 EEG epochs from 160 infants (28-43 weeks postmenstrual age) was utilized.
Epochs included background activity and responses to various stimuli, labeled by seven independent raters.
A random forest model was trained on 340 epochs and tested on 70 epochs for artefact detection.

Main Results

The random forest model achieved a balanced accuracy of 0.81, comparable to manual review.
Accuracy was consistent across different infant ages and stimulus types.
The model effectively identified artefacts in short, single-channel neonatal EEG data.

Conclusions

The developed automated method provides an objective tool for artefact rejection in neonatal EEG.
This technique can significantly improve the efficiency and reliability of analyzing short-epoch, single-channel infant EEG data.
The automated tool has the potential to increase the application and value of EEG in neonatal care and research.

Keywords:

artifacts electroencephalography evoked potentials infant machine learning random forest