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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F23%3A00571948" target="_blank" >RIV/68081731:_____/23:00571948 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21460/23:00362705 RIV/68407700:21730/23:00362705 RIV/00216305:26220/23:PU148186 RIV/00159816:_____/23:00079606 RIV/00216224:14110/23:00134673

Result on the web

<a href="https://www.nature.com/articles/s41598-023-27978-6" target="_blank" >https://www.nature.com/articles/s41598-023-27978-6</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41598-023-27978-6" target="_blank" >10.1038/s41598-023-27978-6</a>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30103 - Neurosciences (including psychophysiology)

Project

Result was created during the realization of more than one project. More information in the Projects tab.

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Scientific Reports

ISSN

2045-2322

e-ISSN

2045-2322

Volume of the periodical

13

Issue of the periodical within the volume

1

Country of publishing house

US - UNITED STATES

Number of pages

13

Pages from-to

744

UT code for WoS article

000968670400005

EID of the result in the Scopus database

2-s2.0-85146295165