0509015 - ÚPT 2020 RIV US eng C - Konferenční příspěvek (zahraniční konf.)
Plešinger, Filip - Viščor, Ivo - Nejedlý, Petr - Andrla, Petr - Halámek, Josef - Jurák, Pavel
Automated Sleep Arousal Detection Based on EEG Envelograms.
Computing in Cardiology 2018. Vol. 45. New York: IEEE, 2018, č. článku 8744043. E-ISSN 2325-887X.
[Computing in Cardiology Conference (CinC) /45./. Maastricht (NL), 23.09.2018-26.09.2018]
Grant CEP: GA MÅ k(CZ) LO1212
Grant ostatní: AV ČR(CZ) MSM100651602
Program: Program na podporu mezinárodní spolupráce začínajících výzkumných pracovníků
Institucionální podpora: RVO:68081731
Klíčová slova: automated detection * EEG activity * frequency shift
Obor OECD: Medical engineering
DOI: https://doi.org/10.22489/CinC.2018.040

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.

Method: 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).

Results: The method showed AUPRC 0.27 on a training set and AUPRC 0.20 on a testing set (N=989).
Trvalý link: http://hdl.handle.net/11104/0299829