Automatic identification of artifacts and unwanted physiologic signals in EEG and EOG during wakefulness

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F17%3A00303256" target="_blank" >RIV/68407700:21230/17:00303256 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21730/17:00303256

Výsledek na webu

<a href="http://www.sciencedirect.com/science/article/pii/S174680941630132X" target="_blank" >http://www.sciencedirect.com/science/article/pii/S174680941630132X</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.bspc.2016.09.006" target="_blank" >10.1016/j.bspc.2016.09.006</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automatic identification of artifacts and unwanted physiologic signals in EEG and EOG during wakefulness

Popis výsledku v původním jazyce

A set of computationally inexpensive methods for reliable and robust detection of undesired signals in the EEG and EOG was designed, implemented, and tested. This strategy includes detection of eye blinking, eye movements, muscle activity, and flat lines in multichannel EEG and EOG data. The proposed methodology was verified on real awake data acquired in controlled conditions (44 recordings of total length 26.38 h) during Maintenance of Wakefulness Tests (MWT). The algorithms worked reliably (average precision was 0.992 ± 0.006, accuracy 0.988 ± 0.006, sensitivity 0.985 ± 0.009, and F1 score 0.988 ± 0.006) and fast (1 h of recording processed in 46.2 ± 5.3 s). We suggest testing this versatile and fast methodology on other type of EEG recordings with modification of threshold parameters if needed. This article reports data from a clinical trials no. NCT01433315 and NCT01580761.

Název v anglickém jazyce

Automatic identification of artifacts and unwanted physiologic signals in EEG and EOG during wakefulness

Popis výsledku anglicky

A set of computationally inexpensive methods for reliable and robust detection of undesired signals in the EEG and EOG was designed, implemented, and tested. This strategy includes detection of eye blinking, eye movements, muscle activity, and flat lines in multichannel EEG and EOG data. The proposed methodology was verified on real awake data acquired in controlled conditions (44 recordings of total length 26.38 h) during Maintenance of Wakefulness Tests (MWT). The algorithms worked reliably (average precision was 0.992 ± 0.006, accuracy 0.988 ± 0.006, sensitivity 0.985 ± 0.009, and F1 score 0.988 ± 0.006) and fast (1 h of recording processed in 46.2 ± 5.3 s). We suggest testing this versatile and fast methodology on other type of EEG recordings with modification of threshold parameters if needed. This article reports data from a clinical trials no. NCT01433315 and NCT01580761.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20301 - Mechanical engineering

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název periodika

Biomedical Signal Processing and Control

ISSN

1746-8094

e-ISSN

1746-8108

Svazek periodika

31

Číslo periodika v rámci svazku

January

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

381-390

Kód UT WoS článku

000386984300042

EID výsledku v databázi Scopus

2-s2.0-84987903150