Imperial College London

DrNicolettaNicolaou

Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Honorary Research Fellow
 
 
 
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Contact

 

+44 (0)20 7594 0875n.nicolaou Website

 
 
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Location

 

B422Bessemer BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

51 results found

LOIZOU S, Nicolaou N, Pincus BA, Papageorgiou A, McCrorie Pet al., 2022, Concept maps as a novel assessment tool in medical education, MedEdPublish, Vol: 12, Pages: 21-21

<ns4:p><ns4:bold>Background:</ns4:bold> Concept Maps (CMs) have been used in a Problem-Based Learning (PBL) setting as  complementary tools to current educational techniques for enhancing medical student knowledge and critical thinking. We conducted a pilot study that used CMs in a PBL-setting to introduce a measure from the field of graph theory and investigate its usefulness as a means of CM quantitative quality assessment.</ns4:p><ns4:p> <ns4:bold>Methods:</ns4:bold> Participants were first-year medical students with no or minor prior CM experience. All participants completed questionnaires (demographic information and assessment of learning style) to establish a baseline measure against which the change in clinical and critical thinking was assessed. They were asked to prepare CMs for three PBL cases, and following the submission of the CMs they completed semi-structured critical and clinical thinking questionnaires. A clinical expert also created corresponding “benchmark” CMs for comparison. Qualitative (Wordclouds) and quantitative (graph theory) analysis provided a summary of the key concepts and quantified the CM quality respectively, compared to the “benchmark” CMs.</ns4:p><ns4:p> <ns4:bold>Results: </ns4:bold>It was  found that graph-theoretical measures (graph density, modularity) were suitable for distinguishing between CMs that captured more in-depth knowledge, compared to CMs that contained simpler associations. Questionnaires also revealed that CMs helped students recall information, organize material in a concise manner, prepare better for their PBL session and provided a good revision tool.</ns4:p><ns4:p> <ns4:bold>Conclusions: </ns4:bold>We have shown that a graph-theoretical approach to quantitative CM assessment is feasible using measures such as graph density and modularity.</ns4:p>

Journal article

LOIZOU S, Nicolaou N, Pincus BA, Papageorgiou A, McCrorie Pet al., 2022, Concept maps as a novel assessment tool in medical education, MedEdPublish, Vol: 12, Pages: 21-21

<ns4:p><ns4:bold>Background:</ns4:bold> We conducted a pilot study to investigate the use of Concept Maps (CMs) in a Problem-Based Learning (PBL) setting as a complementary tool to current educational techniques for enhancing medical student knowledge and critical thinking. The main focus of the pilot was to introduce a measure from the field of graph theory and investigate its usefulness as a means of CM quantitative quality assessment.</ns4:p><ns4:p> <ns4:bold>Methods:</ns4:bold> Participants were first-year medical students with no or minor prior CM experience. All participants completed questionnaires (demographic information and assessment of learning style) to establish a baseline measure against which the change in clinical and critical thinking was assessed. They were asked to prepare CMs for three PBL cases, and following the submission of the CMs they completed semi-structured critical and clinical thinking questionnaires. A clinical expert also created corresponding “benchmark” CMs for comparison. Qualitative (Wordclouds) and quantitative (graph theory) analysis provided a summary of the key concepts and quantified the CM quality respectively, compared to the “benchmark” CMs.</ns4:p><ns4:p> <ns4:bold>Results: </ns4:bold>Questionnaires revealed that CMs helped students recall information, organize material in a concise manner, prepare better for their PBL session and provided a good revision tool. It was also found that graph-theoretical measures (graph density, modularity) were suitable for distinguishing between CMs that captured more in-depth knowledge, compared to CMs that contained simpler associations.</ns4:p><ns4:p> <ns4:bold>Conclusions: </ns4:bold>We have shown that it is possible to quantify CM quality using graph-theoretical measures, such as graph density and modularity.</ns4:p>

Journal article

LOIZOU S, Nicolaou N, Pincus BA, Papageorgiou A, McCrorie Pet al., 2022, Concept maps as a novel assessment tool in medical education, MedEdPublish, Vol: 12, Pages: 21-21

<ns4:p><ns4:bold>Background:</ns4:bold> We conducted a pilot study to investigate the use of Concept Maps (CMs) in a Problem-Based Learning (PBL) setting as a complementary tool to current educational techniques for enhancing medical student knowledge and critical thinking. We also introduced a measure from the field of graph theory as an objective means of CM quality assessment.</ns4:p><ns4:p> <ns4:bold>Methods:</ns4:bold> Participants were first-year medical students with no or minor prior CM experience. All participants completed questionnaires (demographic information and assessment of learning style) to establish a baseline measure against which the change in clinical and critical thinking was assessed. They were asked to prepare CMs for three PBL cases, and following the submission of the CMs they completed semi-structured critical and clinical thinking questionnaires. A clinical expert also created corresponding “benchmark” CMs for comparison. Qualitative (Wordclouds) and quantitative (graph theory) analysis provided a summary of the key concepts and quantified the CM quality respectively, compared to the “benchmark” CMs.</ns4:p><ns4:p> <ns4:bold>Results: </ns4:bold>Questionnaires revealed that CMs helped students recall information, organize material in a concise manner, prepare better for their PBL session and provided a good revision tool. It was also found that graph-theoretical measures (graph density, modularity) were suitable for objectively distinguishing between CMs that captured more in-depth knowledge, compared to CMs that contained simpler associations.</ns4:p><ns4:p> <ns4:bold>Conclusions: </ns4:bold>We have shown that it is possible to quantify CM quality using graph-theoretical measures, such as graph density and modularity.</ns4:p>

Journal article

Ugarte MP, Achilleos S, Quattrocchi A, Gabel J, Kolokotroni O, Constantinou C, Nicolaou N, Rodriguez-Llanes JM, Huang Q, Verstiuk O, Pidmurniak N, Tao JW, Burstrom B, Klepac P, Erzen I, Chong M, Barron M, Hagen TP, Kalmatayeva Z, Davletov K, Zucker I, Kaufman Z, Kereselidze M, Kandelaki L, Le Meur N, Goldsmith L, Critchley JA, Pinilla MA, Jaramillo GI, Teixeira D, Gomez LF, Lobato J, Araujo C, Cuthbertson J, Bennett CM, Polemitis A, Charalambous A, Demetriou CAet al., 2022, Premature mortality attributable to COVID-19: potential years of life lost in 17 countries around the world, January-August 2020, BMC PUBLIC HEALTH, Vol: 22

Journal article

Adama S, Wu S-J, Nicolaou N, Bogdan Met al., 2022, Extendable Hybrid Approach to Detect Conscious States in a CLIS Patient Using Machine Learning, SNE Simulation Notes Europe, Vol: 32, Pages: 37-45, ISSN: 2305-9974

Journal article

Achilleos S, Quattrocchi A, Gabel J, Heraclides A, Kolokotroni O, Constantinou C, Ugarte MP, Nicolaou N, Rodriguez-Llanes JM, Bennett CM, Bogatyreva E, Schernhammer E, Zimmermann C, Leal Costa AJ, Pinto Lobato JC, Fernandes NM, Semedo-Aguiar AP, Jaramillo Ramirez GI, Martin Garzon OD, Mortensen LH, Critchley JA, Goldsmith LP, Denissov G, Ruutel K, Le Meur N, Kandelaki L, Tsiklauri S, O'Donnell J, Oza A, Kaufman Z, Zucker I, Ambrosio G, Stracci F, Hagen TP, Erzen I, Klepac P, Arcos Gonzalez P, Camporro AF, Burstrom B, Pidmurniak N, Verstiuk O, Huang Q, Mehta NK, Polemitis A, Charalambous A, Demetriou CAet al., 2021, Excess all-cause mortality and COVID-19-related mortality: a temporal analysis in 22 countries, from January until August 2020, INTERNATIONAL JOURNAL OF EPIDEMIOLOGY, Vol: 51, Pages: 35-53, ISSN: 0300-5771

Journal article

Stefani S, Kousiappa I, Nicolaou N, Papathanasiou ES, Oulas A, Fanis P, Neocleous V, Phylactou LA, Spyrou GM, Papacostas SSet al., 2020, Neurophysiological and Genetic Findings in Patients With Juvenile Myoclonic Epilepsy, FRONTIERS IN INTEGRATIVE NEUROSCIENCE, Vol: 14, ISSN: 1662-5145

Journal article

Daly I, Nicolaou N, Williams D, Hwang F, Kirke A, Miranda E, Nasuto SJet al., 2020, Neural and physiological data from participants listening to affective music, SCIENTIFIC DATA, Vol: 7

Journal article

Kong E, Nicolaou N, Vizcaychipi MP, 2020, Hemodynamic stability of closed-loop anesthesia systems: a systematic review, MINERVA ANESTESIOLOGICA, Vol: 86, Pages: 76-87, ISSN: 0375-9393

Journal article

Ardissino M, Nicolaou N, Vizcaychipi M, 2019, Non-invasive real-time autonomic function characterization during surgery via continuous Poincare quantification of heart rate variability, International Journal of Clinical Monitoring and Computing, Vol: 33, Pages: 627-635, ISSN: 0167-9945

Heart rate variability (HRV) provides an excellent proxy for monitoring of autonomic function, but the clinical utility of such characterization has not been investigated. In a clinical setting, the baseline autonomic function can reflect ability to adapt to stressors such as anesthesia. No monitoring tool has yet been developed that is able to track changes in HRV in real time. This study is a proof-of-concept for a non-invasive, real-time monitoring model for autonomic function via continuous Poincaré quantification of HRV dynamics. Anonymized heart rate data of 18 healthy individuals (18–45 years) undergoing minor procedures and 18 healthy controls (21–35 years) were analyzed. Patients underwent propofol and fentanyl anesthesia, and controls were at rest. Continuous heart rate monitoring was carried out from before aesthetic induction to the end of the surgical procedure. HRV components (sympathetic and parasympathetic) were extracted and analyzed using Poincaré quantification, and a real-time assessment tool was developed. In the patient group, a significant decrease in the sympathetic and parasympathetic components of HRV was observed following anesthesia (SD1: p = 0.019; SD2: p = 0.00027). No corresponding change in HRV was observed in controls. HRV parameters were modelled into a real-time graph. Using the monitoring technique developed, autonomic changes could be successfully visualized in real-time. This could provide the basis for a novel, fast and non-invasive method of autonomic assessment that can be delivered at the point of care.

Journal article

Kong E, Nicolaou N, Vizcaychipi M, 2019, Haemodynamic stability of closed-loop anaesthesia systems: a systematic review and meta-analysis, Winter Scientific Meeting (WSM) of the Association-of-Anaesthetists, Publisher: WILEY, Pages: 63-63, ISSN: 0003-2409

Conference paper

Nicolaou N, Malik A, Daly I, Weaver J, Hwang F, Kirke A, Roesch EB, Williams D, Miranda ER, Nasuto SJet al., 2017, Directed Motor-Auditory EEG Connectivity Is Modulated by Music Tempo, FRONTIERS IN HUMAN NEUROSCIENCE, Vol: 11, ISSN: 1662-5161

Journal article

Lauteslager T, Nicolaou N, Lande TS, Constandinou TGet al., 2016, Functional neuroimaging Using UWB Impulse Radar: a Feasibility Study, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Publisher: IEEE, Pages: 406-409

Microwave imaging is a promising new modalityfor studying brain function. In the current paper we assess thefeasibility of using a single chip implementation of an ultra-wideband impulse radar for developing a portable and low-costfunctional neuroimaging device. A numerical model is used topredict the level of attenuation that will occur when detectinga volume of blood in the cerebral cortex. A phantom liquid ismade, to study the radar’s performance at different attenuationlevels. Although the radar is currently capable of detecting apoint reflector in a phantom liquid with submillimeter accuracyand high temporal resolution, object detection at the desired levelof attenuation remains a challenge.

Conference paper

Nicolaou N, Constandinou TG, 2016, A nonlinear causality estimator based on Non-Parametric Multiplicative Regression, Frontiers in Neuroinformatics, Vol: 10, ISSN: 1662-5196

Causal prediction has become a popular tool for neuroscience applications, as it allows the study of relationships between different brain areas during rest, cognitive tasks or brain disorders. We propose a nonparametric approach for the estimation of nonlinear causal prediction for multivariate time series. In the proposed estimator, C-NPMR, Autoregressive modelling is replaced by Nonparametric Multiplicative Regression (NPMR). NPMR quantifies interactions between a response variable (effect) and a set of predictor variables (cause); here, we modified NPMR for model prediction. We also demonstrate how a particular measure, the sensitivity Q, could be used to reveal the structure of the underlying causal relationships. We apply C-NPMR on artificial data with known ground truth (5 datasets), as well as physiological data (2 datasets). C-NPMR correctly identifies both linear and nonlinear causal connections that are present in the artificial data, as well as physiologically relevant connectivity in the real data, and does not seem to be affected by filtering. The Sensitivity measure also provides useful information about the latent connectivity.The proposed estimator addresses many of the limitations of linear Granger causality and other nonlinear causality estimators. C-NPMR is compared with pairwise and conditional Granger causality (linear) and Kernel-Granger causality (nonlinear). The proposed estimator can be applied to pairwise or multivariate estimations without any modifications to the main method. Its nonpametric nature, its ability to capture nonlinear relationships and its robustness to filtering make it appealing for a number of applications.

Journal article

Demarchou E, Georgiou J, Nicolaou N, Constandinou TGet al., 2014, Anesthetic-induced changes in EEG activity: a graph theoretical approach, IEEE Biomedical Circuits and Systems (BioCAS) Conference, Pages: 45-48

The dynamic brain networks forming during wakefulness and anesthetic-induced unconsciousness are investigated using time-delayed correlation and graph theoretical measures. Electrical brain activity (EEG) from 10 patients under propofol anesthesia during routine surgery is characterized using the shortest path length, λ, and clustering, c, extracted from time delayed correlation. An increase in λ and c during anesthesiareveals disruption of long-range connections and emergence of more localized neighborhoods. These changes were not a result of volume conduction, as were based on time-delayed correlation. Our observations are in line with theories of anesthetic action and support the use of graph theoretic measures to study emerging brain networks during wakefulness and anesthesia.

Conference paper

Nicolaou N, Georgiou J, 2014, The Study of EEG Dynamics During Anesthesia with Cross-Recurrence Rate, Cureus, Vol: 6

Journal article

Nicolaou N, Georgiou J, 2014, Neural Network-Based Classification of Anesthesia/Awareness Using Granger Causality Features, CLINICAL EEG AND NEUROSCIENCE, Vol: 45, Pages: 77-88, ISSN: 1550-0594

Journal article

Nicolaou N, Georgiou J, 2014, Global field synchrony during general anaesthesia, BRITISH JOURNAL OF ANAESTHESIA, Vol: 112, Pages: 529-539, ISSN: 0007-0912

Journal article

Nicolaou N, Georgiou J, 2014, Spatial Analytic Phase Difference of EEG activity during anesthetic-induced unconsciousness, Clinical Neurophysiology

Journal article

Nicolaou N, Georgiou J, 2013, Towards automatic sleep staging via Cross-Recurrence Rate of EEG and ECG activity, IEEE BioCAS, Pages: 198-201

Conference paper

Daly I, Nicolaou N, Nasuto SJ, Warwick Ket al., 2013, Automated Artifact Removal From the Electroencephalogram: A Comparative Study, CLINICAL EEG AND NEUROSCIENCE, Vol: 44, Pages: 291-306, ISSN: 1550-0594

Journal article

Nicolaou N, Georgiou J, 2013, Monitoring depth of hypnosis under propofol general anaesthesia: Granger Causality and Hidden Markov Models, Neurotechnix (special session: BrainRehab)

Conference paper

Garreau G, Nicolaou N, Georgiou J, 2012, Individual classification through autoregressive modelling of micro-Doppler signatures, IEEE BioCAS, Pages: 312-315

Conference paper

Nicolaou N, Dionysiou A, Georgiou J, 2012, Temporal dynamics of EEG during anesthesia, 12th IEEE BIBE, Pages: 288-291

Conference paper

Nicolaou N, Hourris S, Alexandrou P, Georgiou Jet al., 2012, EEG-Based Automatic Classification of 'Awake' versus 'Anesthetized' State in General Anesthesia Using Granger Causality, PLOS ONE, Vol: 7, ISSN: 1932-6203

Journal article

Nicolaou N, Hourris S, Alexandrou P, Georgiou Jet al., 2012, Permutation entropy: A reliable measure for automatic monitoring of anesthetic depth during surgery?, Engineering Intelligent Systems, Vol: 20, Pages: 9-18, ISSN: 1472-8915

Permutation Entropy (PE) has recently been applied to characterize anesthetic-induced changes in the frontal electrical brain activity (EEG) during anesthesia. In this work we investigate the stability of PE as a means of identifying between the awake and anesthetized EEG over the entire duration of surgery under different anesthetic regimes and using a full set of EEG sensors. Average classification rates from 22 patients range between 98-99% (specificity, sensitivity and accuracy), when using information from whole-head EEG. The findings support the robustness of PE for discriminating 'awake' and 'anesthesia' throughout the entire surgery, independently of the anesthetic regime followed. ©2012 CRL Publishing Ltd.

Journal article

Nicolaou N, Georgiou J, 2012, Detection of epileptic electroencephalogram based on Permutation Entropy and Support Vector Machines, EXPERT SYSTEMS WITH APPLICATIONS, Vol: 39, Pages: 202-209, ISSN: 0957-4174

Journal article

Nicolaou N, Houris S, Alexandrou P, Georgiou Jet al., 2012, Permutation Entropy: a reliable measure for automatic monitoring of anesthetic depth during surgery?, Engineering Intelligent Systems Journal (Special Issue: Timely developments in Artificial Intelligence Applications), Vol: 20, Pages: 1-10

Journal article

Nicolaou N, Houris S, Alexandrou P, Georgiou Jet al., 2011, Cross-Recurrence Rate for discriminating 'conscious' and 'unconscious' state in propofol general anesthesia, IEEE BioCAS, Pages: 416-419

Conference paper

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