Automated unsupervised behavioral state classification using intracranial electrophysiology

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F19%3A00328195" target="_blank" >RIV/68407700:21730/19:00328195 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1088/1741-2552/aae5ab" target="_blank" >https://doi.org/10.1088/1741-2552/aae5ab</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1741-2552/aae5ab" target="_blank" >10.1088/1741-2552/aae5ab</a>

Result language

angličtina

Original language name

Automated unsupervised behavioral state classification using intracranial electrophysiology

Original language description

Objective. Automated behavioral state classification in intracranial EEG (iEEG) recordings may be beneficial for iEEG interpretation and quantifying sleep patterns to enable behavioral state dependent neuromodulation therapy in next generation implantable brain stimulation devices. Here, we introduce a fully automated unsupervised framework to differentiate between awake (AW), sleep (N2), and slow wave sleep (N3) using intracranial EEG (iEEG) only and validated with expert scored polysomnography. Approach. Data from eight patients undergoing evaluation for epilepsy surgery (age 40 +/- 11, three female) with intracranial depth electrodes for iEEG monitoring were included. Spectral power features (0.1-235 Hz) spanning several frequency bands from a single electrode were used to classify behavioral states of patients into AW, N2, and N3. Main results. Overall, classification accuracy of 94%, with 94% sensitivity and 93% specificity across eight subjects using multiple spectral power features from a single electrode was achieved. Classification performance of N3 sleep was significantly better (95%, sensitivity 95%, specificity 93%) than that of the N2 sleep phase (87%, sensitivity 78%, specificity 96%). Significance. Automated, unsupervised, and robust classification of behavioral states based on iEEG data is possible, and it is feasible to incorporate these algorithms into future implantable devices with limited computational power, memory, and number of electrodes for brain monitoring and stimulation.

Czech name

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Czech description

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Type

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

CEP classification

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OECD FORD branch

20601 - Medical engineering

Project

<a href="/en/project/GA17-20480S" target="_blank" >GA17-20480S: Temporal context in analysis of long-term non-stationary multidimensional signal</a><br>

Continuities

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

Publication year

2019

Confidentiality

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

Name of the periodical

Journal of Neural Engineering

ISSN

1741-2560

e-ISSN

1741-2552

Volume of the periodical

16

Issue of the periodical within the volume

2

Country of publishing house

GB - UNITED KINGDOM

Number of pages

8

Pages from-to

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UT code for WoS article

000456333500004

EID of the result in the Scopus database

2-s2.0-85062865646