Utilization of temporal autoencoder for semi-supervised intracranial EEG clustering and classification

Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that is often associated with human bias and requires trained electrophysiology experts with specific domain knowledge. This challenge is now compounded by development of measurement technolo...

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Vydáno v:Scientific reports Ročník 13; číslo 1; s. 744 - 13
Hlavní autoři: Nejedly, Petr, Kremen, Vaclav, Lepkova, Kamila, Mivalt, Filip, Sladky, Vladimir, Pridalova, Tereza, Plesinger, Filip, Jurak, Pavel, Pail, Martin, Brazdil, Milan, Klimes, Petr, Worrell, Gregory
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 13.01.2023
Nature Publishing Group
Nature Portfolio
Témata:
631/114/116
631/114/1305
692/617/375/178
Brain - physiology
Classification
Deep learning
EEG
Electrocorticography
Electroencephalography
Electroencephalography - methods
Electrophysiology
Epilepsy
Firing pattern
Gold
Humanities and Social Sciences
Humans
Machine learning
multidisciplinary
Patients
Prospective Studies
ROC Curve
Science
Science (multidisciplinary)
ISSN:2045-2322, 2045-2322
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Popis
Shrnutí:Manual visual review, annotation and categorization of electroencephalography (EEG) is a time-consuming task that is often associated with human bias and requires trained electrophysiology experts with specific domain knowledge. This challenge is now compounded by development of measurement technologies and devices allowing large-scale heterogeneous, multi-channel recordings spanning multiple brain regions over days, weeks. Currently, supervised deep-learning techniques were shown to be an effective tool for analyzing big data sets, including EEG. However, the most significant caveat in training the supervised deep-learning models in a clinical research setting is the lack of adequate gold-standard annotations created by electrophysiology experts. Here, we propose a semi-supervised machine learning technique that utilizes deep-learning methods with a minimal amount of gold-standard labels. The method utilizes a temporal autoencoder for dimensionality reduction and a small number of the expert-provided gold-standard labels used for kernel density estimating (KDE) maps. We used data from electrophysiological intracranial EEG (iEEG) recordings acquired in two hospitals with different recording systems across 39 patients to validate the method. The method achieved iEEG classification (Pathologic vs. Normal vs. Artifacts) results with an area under the receiver operating characteristic (AUROC) scores of 0.862 ± 0.037, 0.879 ± 0.042, and area under the precision-recall curve (AUPRC) scores of 0.740 ± 0.740, 0.714 ± 0.042. This demonstrates that semi-supervised methods can provide acceptable results while requiring only 100 gold-standard data samples in each classification category. Subsequently, we deployed the technique to 12 novel patients in a pseudo-prospective framework for detecting Interictal epileptiform discharges (IEDs). We show that the proposed temporal autoencoder was able to generalize to novel patients while achieving AUROC of 0.877 ± 0.067 and AUPRC of 0.705 ± 0.154.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-27978-6