Ciro della Monica

Dr Ciro della Monica


My publications

Publications

Sterr Annette, Ebajemito James, Mikkelsen Kaare B., Bonmati-Carrion Maria, Santhi Nayantara, della Monica Ciro, Grainger Lucinda, Atzori Giuseppe, Revell Victoria, Debener Stefan, Dijk Derk-Jan, DeVos Maarten (2018) Sleep EEG Derived From Behind-the-Ear Electrodes (cEEGrid) Compared to Standard Polysomnography: A Proof of Concept Study, Frontiers in Human Neuroscience 12 452 Frontiers Research Foundation
Electroencephalography (EEG) recordings represent a vital component of the
assessment of sleep physiology, but the methodology presently used is costly, intrusive
to participants, and laborious in application. There is a recognized need to develop more
easily applicable yet reliable EEG systems that allow unobtrusive long-term recording
of sleep-wake EEG ideally away from the laboratory setting. cEEGrid is a recently
developed flex-printed around-the-ear electrode array, which holds great potential for
sleep-wake monitoring research. It is comfortable to wear, simple to apply, and minimally
intrusive during sleep. Moreover, it can be combined with a smartphone-controlled
miniaturized amplifier and is fully portable. Evaluation of cEEGrid as a motion-tolerant
device is ongoing, but initial findings clearly indicate that it is very well suited for cognitive
research. The present study aimed to explore the suitability of cEEGrid for sleep
research, by testing whether cEEGrid data affords the signal quality and characteristics
necessary for sleep stage scoring. In an accredited sleep laboratory, sleep data from
cEEGrid and a standard PSG system were acquired simultaneously. Twenty participants
were recorded for one extended nocturnal sleep opportunity. Fifteen data sets were
scored manually. Sleep parameters relating to sleep maintenance and sleep architecture
were then extracted and statistically assessed for signal quality and concordance. The
findings suggest that the cEEGrid system is a viable and robust recording tool to capture
sleep and wake EEG. Further research is needed to fully determine the suitability of
cEEGrid for basic and applied research as well as sleep medicine.
Mikkelsen Kaare, Ebajemito James K., Bonmati?Carrion Mari, Santhi Nayantara, Revell Victoria, Atzori Giuseppe, della Monica Ciro, Debener Stefan, Dijk Derk-Jan, Sterr Annette, Vos Maarte (2019) Machine?learning?derived sleep?wake staging from around?the?ear electroencephalogram outperforms manual scoring and actigraphy, Journal of Sleep Research 28 (2) e12786 Wiley-Blackwell Publishing
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 low?cost, mobile electroencephalogram (EEG) platform consisting of a lightweight EEG amplifier combined with flex?printed cEEGrid electrodes placed around the ear, which can be implemented as a fully self?applicable 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 (?random forests?) 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 ± 14.3 years) during an extended nocturnal sleep opportunity, which induced disrupted sleep with a large inter?individual variation in sleep parameters. The results demonstrate that machine?learning?based scoring of around?the?ear 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 machine?learning?based automated scoring of cEEGrid sleep recordings compared with manual scoring of standard PSG was satisfactory. The findings show that cEEGrid recordings combined with machine?learning?based scoring holds promise for large?scale sleep studies.

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