Automated Sleep Arousal Detection Based on EEG Envelograms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F18%3A00509015" target="_blank" >RIV/68081731:_____/18:00509015 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.22489/CinC.2018.040" target="_blank" >http://dx.doi.org/10.22489/CinC.2018.040</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.22489/CinC.2018.040" target="_blank" >10.22489/CinC.2018.040</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automated Sleep Arousal Detection Based on EEG Envelograms

Popis výsledku v původním jazyce

Background: Sleep arousal is basically described as a shift in EEG activity in frequencies > 16 Hz for a duration of > 3 sec (by the American Sleep Disorders Association - ASDA). The number of these arousals during sleep is a reflection of sleep quality. In accordance with the PhysioNet/CinC Challenge 2018, we present a method for automatic detection of arousals in polysomnographic recordings. nnMethod: Each file in the training dataset (N=994) has defined ´Target Arousal Regions´ (TAR, median length 33 seconds), however, arousals were usually located in the right half of these TARs. We built a method detecting EEG frequency shift to locate arousals inside ARs: envelograms (14-20, 16-25 and 20-40 Hz) were inspected in a 3-sec floating window for an increase against a 10-sec background. We then extracted 133,573 blocks with such a shift from TARs (N=38,628) as well as outside TARs (N=94,945). We extracted 23 features from these blocks (how many EEG channels/frequency bands EEG frequency shift, heart rate before/during arousal, airflow and EMG changes) and trained a bagged tree ensemble model (70/30 % hold-out). nnResults: The method showed AUPRC 0.27 on a training set and AUPRC 0.20 on a testing set (N=989).

Název v anglickém jazyce

Automated Sleep Arousal Detection Based on EEG Envelograms

Popis výsledku anglicky

Background: Sleep arousal is basically described as a shift in EEG activity in frequencies > 16 Hz for a duration of > 3 sec (by the American Sleep Disorders Association - ASDA). The number of these arousals during sleep is a reflection of sleep quality. In accordance with the PhysioNet/CinC Challenge 2018, we present a method for automatic detection of arousals in polysomnographic recordings. nnMethod: Each file in the training dataset (N=994) has defined ´Target Arousal Regions´ (TAR, median length 33 seconds), however, arousals were usually located in the right half of these TARs. We built a method detecting EEG frequency shift to locate arousals inside ARs: envelograms (14-20, 16-25 and 20-40 Hz) were inspected in a 3-sec floating window for an increase against a 10-sec background. We then extracted 133,573 blocks with such a shift from TARs (N=38,628) as well as outside TARs (N=94,945). We extracted 23 features from these blocks (how many EEG channels/frequency bands EEG frequency shift, heart rate before/during arousal, airflow and EMG changes) and trained a bagged tree ensemble model (70/30 % hold-out). nnResults: The method showed AUPRC 0.27 on a training set and AUPRC 0.20 on a testing set (N=989).

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20601 - Medical engineering

Projekt

<a href="/cs/project/LO1212" target="_blank" >LO1212: ALISI - Centrum pokročilých diagnostických metod a technologií</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2018

Kód důvěrnosti údajů

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

Název statě ve sborníku

Computing in Cardiology 2018

ISBN

—

ISSN

2325-887X

e-ISSN

—

Počet stran výsledku

4

Strana od-do

8744043

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Maastricht

Datum konání akce

23. 9. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000482598700258