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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