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Comparative analysis of the discriminative capacity of EEG, two ECG-derived and respiratory signals in automatic sleep staging

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F17%3A10238281" target="_blank" >RIV/61989100:27240/17:10238281 - isvavai.cz</a>

  • Výsledek na webu

    <a href="http://advances.vsb.cz/index.php/AEEE/article/view/2182" target="_blank" >http://advances.vsb.cz/index.php/AEEE/article/view/2182</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.15598/aeee.v15i3.2182" target="_blank" >10.15598/aeee.v15i3.2182</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Comparative analysis of the discriminative capacity of EEG, two ECG-derived and respiratory signals in automatic sleep staging

  • Popis výsledku v původním jazyce

    Highly accurate classification of sleep stages is possible based on EEG signals alone. However, reliable and high quality acquisition of these signals in the home environment is difficult. Instead, electrocardiogram (ECG) and Respiratory (Res) signals are easier to record and may offer a practical alternative for home monitoring of sleep. Therefore, automatic sleep staging was performed using ECG, Res (thoracic excur-sion) and EEG signals from 31 nocturnal recordings of the Sleep Heart Health Study (SHHS) polysomnography Database. Feature vectors were extracted from 0.5 min (standard) epochs of sleep data by time-domain, frequency domain, time-frequency and nonlinear methods and optimized by using the Support Vector Machine -Recursive Feature Elimination (SVM-RFE) method. These features were then classified by using a SVM. Classification based upon EEG features produced a Correct Classification Ratio CCR = 0.92. In comparison, features derived from ECG signals alone, that is the combination of Heart Rate Variability (HRV), and ECG-Derived Respiration (EDR) signals produced a CCR = 0.54, while those features based on the combination of HRV and (thoracic) Res signals resulted in a CCR = 0.57. Overall comparison of the results based on standard epochs of EEG signals with those obtained from 5-minute (long) epochs of cardiorespiratory signals, revealed that acceptable CCR = 0.81 and discriminative capacity (Accuracy = 89.32 %, Specificity = 92.88 % and Sensitivity = 78.64 %) were also achievable when using optimal feature sets derived from long epochs of the latter signals in sleep staging. In addition, it was observed that the presence of some artifacts (like bigeminy) in the cardiorespiratory signals reduced the accuracy of automatic sleep staging more than the artifacts that contaminated the EEG signals. © 2017 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERING.

  • Název v anglickém jazyce

    Comparative analysis of the discriminative capacity of EEG, two ECG-derived and respiratory signals in automatic sleep staging

  • Popis výsledku anglicky

    Highly accurate classification of sleep stages is possible based on EEG signals alone. However, reliable and high quality acquisition of these signals in the home environment is difficult. Instead, electrocardiogram (ECG) and Respiratory (Res) signals are easier to record and may offer a practical alternative for home monitoring of sleep. Therefore, automatic sleep staging was performed using ECG, Res (thoracic excur-sion) and EEG signals from 31 nocturnal recordings of the Sleep Heart Health Study (SHHS) polysomnography Database. Feature vectors were extracted from 0.5 min (standard) epochs of sleep data by time-domain, frequency domain, time-frequency and nonlinear methods and optimized by using the Support Vector Machine -Recursive Feature Elimination (SVM-RFE) method. These features were then classified by using a SVM. Classification based upon EEG features produced a Correct Classification Ratio CCR = 0.92. In comparison, features derived from ECG signals alone, that is the combination of Heart Rate Variability (HRV), and ECG-Derived Respiration (EDR) signals produced a CCR = 0.54, while those features based on the combination of HRV and (thoracic) Res signals resulted in a CCR = 0.57. Overall comparison of the results based on standard epochs of EEG signals with those obtained from 5-minute (long) epochs of cardiorespiratory signals, revealed that acceptable CCR = 0.81 and discriminative capacity (Accuracy = 89.32 %, Specificity = 92.88 % and Sensitivity = 78.64 %) were also achievable when using optimal feature sets derived from long epochs of the latter signals in sleep staging. In addition, it was observed that the presence of some artifacts (like bigeminy) in the cardiorespiratory signals reduced the accuracy of automatic sleep staging more than the artifacts that contaminated the EEG signals. © 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

    17

  • Strana od-do

    459-475

  • Kód UT WoS článku

    000424330700011

  • EID výsledku v databázi Scopus

    2-s2.0-85030546920