David Western David.Western@uwe.ac.uk
Wallscourt Fellow in Health Technology
David Western David.Western@uwe.ac.uk
Wallscourt Fellow in Health Technology
T. Weber
R. Kandasamy
F. May
S. Taylor
Yanhui Zhu Yanhui.Zhu@uwe.ac.uk
Senior Lecturer in Economics
L. Canham
Machine learning classifiers for detection of abnormal clinical electroencephalography (EEG) signals have advanced signficantly in recent years, largely supported by the carefully curated Temple University Hospital Abnormal EEG Corpus (TUAB). Further progress towards clinically useful tools is likely to require larger volumes of data. In this study, we explore the viability and benefits of fully automated labelling of clinical EEG recordings based on the text in the clinical report, to efficiently exploit larger existing databases. We apply a machine learning classifier to the text reports in the Temple University Hospital EEG Corpus (TUEG) in order to label individual recordings. We show that training a deep convolutional neural network against the resulting dataset yields advantages in the resulting classification performance, namely increased area under the receiver operating characteristic curve and state-of-the-art specificity, albeit with a notable reduction in sensitivity. By demonstrating the viability of automatic report-based labelling, this paper opens the prospect of efficiently utilising the huge amount of historical EEG data in global medical archives to enhance the training of machine learning classifiers, either for enhanced general performance or bespoke training/evaluation for local populations.
Western, D., Weber, T., Kandasamy, R., May, F., Taylor, S., Zhu, Y., & Canham, L. (2022). Automatic report-based labelling of clinical EEGs for classifier training. In 2021 IEEE Signal Processing in Medicine and Biology Symposium (SPMB). https://doi.org/10.1109/SPMB52430.2021.9672295
Conference Name | 2021 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) |
---|---|
Conference Location | Philadelphia, USA |
Start Date | Dec 4, 2021 |
End Date | Dec 4, 2021 |
Acceptance Date | Nov 4, 2021 |
Online Publication Date | Jan 20, 2022 |
Publication Date | Jan 20, 2022 |
Deposit Date | Feb 24, 2022 |
Publicly Available Date | Feb 25, 2022 |
Publisher | Institute of Electrical and Electronics Engineers (IEEE) |
Book Title | 2021 IEEE Signal Processing in Medicine and Biology Symposium (SPMB) |
ISBN | 9781665428989 |
DOI | https://doi.org/10.1109/SPMB52430.2021.9672295 |
Keywords | Deep learning, natural language processing, signal processing, AI, EEG, neural networks, data science, machine learning |
Public URL | https://uwe-repository.worktribe.com/output/8677539 |
Automatic report-based labelling of clinical EEGs for classifier training
(139 Kb)
PDF
Licence
http://www.rioxx.net/licenses/all-rights-reserved
Publisher Licence URL
http://www.rioxx.net/licenses/all-rights-reserved
Copyright Statement
The impact of monetary policy on gold price dynamics
(2017)
Journal Article
Hedging effectiveness in the index futures market
(2010)
Book Chapter
Credit risk premium in a disaster-prone world
(2008)
Presentation / Conference
Rare disasters and the equity risk premium in a two-country world
(2008)
Presentation / Conference
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
Apache License Version 2.0 (http://www.apache.org/licenses/)
Apache License Version 2.0 (http://www.apache.org/licenses/)
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Advanced Search