Ciro della Monica

Dr Ciro della Monica

Research Fellow
+44 (0)1483 684208
13 MA 01

Academic and research departments

Faculty of Health and Medical Sciences, School of Biosciences and Medicine, Surrey Sleep Research Centre (SSRC).

My publications

Publications

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, Sterr Annette (2018)Sleep EEG Derived From Behind-the-Ear Electrodes (cEEGrid) Compared to Standard Polysomnography: A Proof of Concept Study, In: Frontiers in Human Neuroscience12452 Frontiers Research Foundation
DOI: 10.3389/fnhum.2018.00452
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, In: Journal of Sleep Research28(2)e12786 Wiley-Blackwell Publishing
DOI: 10.1111/jsr.12786
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.
della Monica C, Johnsen Sigurd, Atzori Giuseppe, Groeger J, Dijk Derk-Jan (2018)Rapid-Eye-Movement Sleep, Sleep Continuity and Slow Wave Sleep as Predictors of Cognition, Mood and Subjective Sleep Quality in Healthy Men and Women, Aged 20-84 Years, In: Frontiers in Psychiatry9255 Frontiers Media
DOI: 10.3389/fpsyt.2018.00255
Sleep and its sub-states are assumed to be important for brain function across the lifespan but which aspects of sleep associate with various aspects of cognition, mood and self-reported sleep quality has not yet been established in detail. Sleep was quantified by polysomnography, quantitative Electroencephalogram (EEG) analysis and self-report in 206 healthy men and women, aged 20–84 years, without sleep complaints. Waking brain function was quantified by five assessments scheduled across the day covering objectively assessed performance across cognitive domains including sustained attention and arousal, decision and response time, motor and sequence control, working memory, and executive function as well as self-reports of alertness, mood and affect. Controlled for age and sex, self-reported sleep quality was negatively associated with number of awakenings and positively associated with the duration of Rapid Eye Movement (REM) sleep, but no significant associations with Slow Wave Sleep (SWS) measures were observed. Controlling only for age showed that associations between objective and subjective sleep quality were much stronger in women than in men. Analysis of 51 performance measures demonstrated that, after controlling for age and sex, fewer awakenings and more REM sleep were associated significantly with better performance on the Goal Neglect task, which is a test of executive function. Factor analysis of the individual performance measures identified four latent variables labeled Mood/Arousal, Response Time, Accuracy, and Visual Perceptual Sensitivity. Whereas Mood/Arousal improved with age, Response Times became slower, while Accuracy and Visual perceptual sensitivity showed little change with age. After controlling for sex and age, nominally significant association between sleep and factor scores were observed such that Response Times were faster with more SWS, and Accuracy was reduced where individuals woke more often or had less REM sleep. These data identify a positive contribution of SWS to processing speed and in particular highlight the importance of sleep continuity and REM sleep for subjective sleep quality and performance accuracy across the adult lifespan. These findings warrant further investigation of the contribution of sleep continuity and REM sleep to brain function.

Additional publications