Automated Sleep Arousal Detection Based on EEG Envelograms
| Autor: | Ivo Viscor, Petr Andrla, Filip Plesinger, Pavel Jurák, Petr Nejedly, Josef Halamek |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
medicine.medical_specialty
Training set medicine.diagnostic_test Sleep quality Computer science 0206 medical engineering 02 engineering and technology Electroencephalography Audiology Sleep arousal 020601 biomedical engineering Arousal 03 medical and health sciences 0302 clinical medicine Duration (music) Heart rate medicine Sleep (system call) 030217 neurology & neurosurgery |
| Zdroj: | CinC |
| ISSN: | 2325-887X |
| Popis: | 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). |
| Databáze: | OpenAIRE |
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