1 research outputs found
Machineālearningāderived sleepāwake staging from aroundātheāear electroencephalogram outperforms manual scoring and actigraphy
Quantification of sleep is important for the diagnosis of sleep disorders and sleep research. However, the only widely accepted method to obtain sleep staging is by visual analysis of polysomnography (PSG), which is expensive and time consuming. Here, we investigate automated sleep scoring based on a lowācost, mobile electroencephalogram (EEG) platform consisting of a lightweight EEG amplifier combined with flexāprinted cEEGrid electrodes placed around the ear, which can be implemented as a fully selfāapplicable sleep system. However, cEEGrid signals have different amplitude characteristics to normal scalp PSG signals, which might be challenging for visual scoring. Therefore, this study evaluates the potential of automatic scoring of cEEGrid signals using a machine learning classifier (ārandom forestsā) and compares its performance with manual scoring of standard PSG. In addition, the automatic scoring of cEEGrid signals is compared with manual annotation of the cEEGrid recording and with simultaneous actigraphy. Acceptable recordings were obtained in 15 healthy volunteers (aged 35 Ā± 14.3 years) during an extended nocturnal sleep opportunity, which induced disrupted sleep with a large interāindividual variation in sleep parameters. The results demonstrate that machineālearningābased scoring of aroundātheāear EEG outperforms actigraphy with respect to sleep onset and total sleep time assessments. The automated scoring outperforms human scoring of cEEGrid by standard criteria. The accuracy of machineālearningābased automated scoring of cEEGrid sleep recordings compared with manual scoring of standard PSG was satisfactory. The findings show that cEEGrid recordings combined with machineālearningābased scoring holds promise for largeāscale sleep studies