Sleep spindle detection and prediction using a mixture of time series and chaotic features
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10238292" target="_blank" >RIV/61989100:27240/17:10238292 - isvavai.cz</a>
Výsledek na webu
<a href="http://advances.vsb.cz/index.php/AEEE/article/view/2174" target="_blank" >http://advances.vsb.cz/index.php/AEEE/article/view/2174</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.15598/aeee.v15i3.2174" target="_blank" >10.15598/aeee.v15i3.2174</a>
Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Sleep spindle detection and prediction using a mixture of time series and chaotic features
Popis výsledku v původním jazyce
It is well established that sleep spindles (bursts of oscillatory brain electrical activity) are significant indicators of learning, memory and some disease states. Therefore, many attempts have been made to detect these hallmark patterns automatically. In this pilot investigation, we paid special attention to nonlinear chaotic features of EEG signals (in combination with linear features) to investigate the detection and prediction of sleep spindles. These nonlinear features included: Higuchi’s, Katz’s and Sevcik’s Fractal Dimensions, as well as the Largest Lyapunov Exponent and Kolmogorov’s Entropy. It was shown that the intensity map of various nonlinear features derived from the constructive interference of spindle signals could improve the detection of the sleep spindles. It was also observed that the prediction of sleep spindles could be facilitated by means of the analysis of these maps. Two well-known classifiers, namely the Multi-Layer Percep-tron (MLP) and the K-Nearest Neighbor (KNN) were used to distinguish between spindle and non-spindle patterns. The MLP classifier produced a high discriminative capacity (accuracy = 94.93 %, sensitivity = 94.31 % and specificity = 95.28 %) with significant robustness (accuracy ranging from 91.33 % to 94.93 %, sensitivity varying from 91.20 % to 94.31 %, and specificity extending from 89.79 % to 95.28 %) in separating spindles from non-spindles. This classifier also generated the best results in predicting sleep spindles based on chaotic features. In addition, the MLP was used to find out the best time window for predicting the sleep spindles, with the experimental results reaching 97.96 % accuracy. © 2017 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING.
Název v anglickém jazyce
Sleep spindle detection and prediction using a mixture of time series and chaotic features
Popis výsledku anglicky
It is well established that sleep spindles (bursts of oscillatory brain electrical activity) are significant indicators of learning, memory and some disease states. Therefore, many attempts have been made to detect these hallmark patterns automatically. In this pilot investigation, we paid special attention to nonlinear chaotic features of EEG signals (in combination with linear features) to investigate the detection and prediction of sleep spindles. These nonlinear features included: Higuchi’s, Katz’s and Sevcik’s Fractal Dimensions, as well as the Largest Lyapunov Exponent and Kolmogorov’s Entropy. It was shown that the intensity map of various nonlinear features derived from the constructive interference of spindle signals could improve the detection of the sleep spindles. It was also observed that the prediction of sleep spindles could be facilitated by means of the analysis of these maps. Two well-known classifiers, namely the Multi-Layer Percep-tron (MLP) and the K-Nearest Neighbor (KNN) were used to distinguish between spindle and non-spindle patterns. The MLP classifier produced a high discriminative capacity (accuracy = 94.93 %, sensitivity = 94.31 % and specificity = 95.28 %) with significant robustness (accuracy ranging from 91.33 % to 94.93 %, sensitivity varying from 91.20 % to 94.31 %, and specificity extending from 89.79 % to 95.28 %) in separating spindles from non-spindles. This classifier also generated the best results in predicting sleep spindles based on chaotic features. In addition, the MLP was used to find out the best time window for predicting the sleep spindles, with the experimental results reaching 97.96 % accuracy. © 2017 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING.
Klasifikace
Druh
J<sub>SC</sub> - Článek v periodiku v databázi SCOPUS
CEP obor
—
OECD FORD obor
20201 - Electrical and electronic engineering
Návaznosti výsledku
Projekt
—
Návaznosti
S - Specificky vyzkum na vysokych skolach
Ostatní
Rok uplatnění
2017
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Údaje specifické pro druh výsledku
Název periodika
Advances in Electrical and Electronic Engineering
ISSN
1336-1376
e-ISSN
—
Svazek periodika
15
Číslo periodika v rámci svazku
3
Stát vydavatele periodika
CZ - Česká republika
Počet stran výsledku
13
Strana od-do
435-447
Kód UT WoS článku
—
EID výsledku v databázi Scopus
2-s2.0-85030534382