Behavioral state classification in epileptic brain using intracranial electrophysiology

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21730%2F17%3A00309724" target="_blank" >RIV/68407700:21730/17:00309724 - isvavai.cz</a>

Result on the web

<a href="http://80.iopscience.iop.org.dialog.cvut.cz/article/10.1088/1741-2552/aa5688/pdf" target="_blank" >http://80.iopscience.iop.org.dialog.cvut.cz/article/10.1088/1741-2552/aa5688/pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1088/1741-2552/aa5688" target="_blank" >10.1088/1741-2552/aa5688</a>

Result language

angličtina

Original language name

Behavioral state classification in epileptic brain using intracranial electrophysiology

Original language description

Objective. Automated behavioral state classification can benefit next generation implantable epilepsy devices. In this study we explored the feasibility of automated awake (AW) and slow wave sleep (SWS) classification using wide bandwidth intracranial EEG (iEEG) in patients undergoing evaluation for epilepsy surgery. Approach. Data from seven patients (age $34pm 12$ , 4 women) who underwent intracranial depth electrode implantation for iEEG monitoring were included. Spectral power features (0.1–600 Hz) spanning several frequency bands from a single electrode were used to train and test a support vector machine classifier. Main results. Classification accuracy of 97.8  ±  0.3% (normal tissue) and 89.4  ±  0.8% (epileptic tissue) across seven subjects using multiple spectral power features from a single electrode was achieved. Spectral power features from electrodes placed in normal temporal neocortex were found to be more useful (accuracy 90.8  ±  0.8%) for sleep-wake state classification than electrodes located in normal hippocampus (87.1  ±  1.6%). Spectral power in high frequency band features (Ripple (80–250 Hz), Fast Ripple (250–600 Hz)) showed comparable performance for AW and SWS classification as the best performing Berger bands (Alpha, Beta, low Gamma) with accuracy  >=90% using a single electrode contact and single spectral feature. Significance. Automated classification of wake and SWS should prove useful for future implantable epilepsy devices with limited computational power, memory, and number of electrodes. Applications include quantifying patient sleep patterns and behavioral state dependent detection, prediction, and electrical stimulation therapies.

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

20205 - Automation and control systems

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

2017

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

14

Issue of the periodical within the volume

2

Country of publishing house

GB - UNITED KINGDOM

Number of pages

9

Pages from-to

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UT code for WoS article

000395425600001

EID of the result in the Scopus database

2-s2.0-85015716549