Automatic Sleep Stage Detection using a Single Channel Frontal EEG
| Autor: | Bogdan Ionescu, Ruben de Francisco, Alessandro C. Rossi, Alexandra-Maria Tautan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Sleep disorder
Sleep Stages Channel (digital image) medicine.diagnostic_test Computer science business.industry Pattern recognition Electroencephalography medicine.disease 030218 nuclear medicine & medical imaging Random forest 03 medical and health sciences 0302 clinical medicine Frequency domain medicine Sleep (system call) Artificial intelligence Precision and recall business 030217 neurology & neurosurgery |
| Zdroj: | 2019 E-Health and Bioengineering Conference (EHB). |
| DOI: | 10.1109/ehb47216.2019.8969973 |
| Popis: | Sleep stage detection algorithms can significantly reduce the workload of manual sleep staging and in improving sleep disorder diagnostics. In this paper, we focus on the automatic detection of sleep stages from a frontal channel EEG using expert defined features in both time and frequency domain, fed to a random forest classifier. The proposed approach shows that using a single frontal channel EEG signal as input to automated sleep scoring algorithms is as effective as using EEGs recorded from the central and occipital regions. Mean overall accuracy, precision and recall were respectively of 72.98%, 79.75% and 71.83%, when validating our method on the MGH (Massachusetts General Hospital), You snooze, you win dataset. |
| Databáze: | OpenAIRE |
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