Disorders of consciousness (DoC) are characterized by alteration in arousal and/or awareness commonly caused by severe brain injury. There exists a consensus on adopting advanced neuroimaging and electrophysiological procedures to improve diagnosis/prognosis of DoC patients. Currently, these procedures are prevalently applied in a research-oriented context and their translation into clinical practice is yet to come. The aim of the study consisted in the identification of measures derived from routinary electroencephalography (EEG) able to support clinicians in the prediction of DoC patients' outcome. In the present study, a routine EEG was recorded during rest from a sample of 58 DoC patients clinically diagnosed as Unresponsive Wakefulness State (UWS) and Minimally Conscious State (MCS) and followed- up for 3 months. EEG-based features characterizing brain activity in terms of spectral content and resting state networks organization were used in a predictive machine learning model to i) identify which were the most promising features in predicting patients' exit from the DoC, regardless of the clinical diagnosis and ii) verify whether such features would have been the same best discriminating UWS from MCS or specific of the outcome prediction. A predictive machine learning model was built on EEG features related to spectral content and resting state networks which returned up to 85% of performance accuracy in outcome prediction and 76% in DoC state recognition (UWS vs MCS). We provided preliminary evidence for the exploitation of a routine EEG to improve the clinical management of non- communicative patients to be confirmed in a larger DoC population.

EEG-derived markers to improve prognostic evaluation of Disorders of Consciousness / Toppi, J.; Quattrociocchi, I.; Riccio, A.; Dippolito, M.; Aloisi, M.; Colamarino, E.; Pichiorri, F.; Cincotti, F.; Formisano, R.; Mattia, D.. - In: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS. - ISSN 2168-2194. - PP:(2024), pp. 1-13. [10.1109/JBHI.2024.3445118]

EEG-derived markers to improve prognostic evaluation of Disorders of Consciousness

Toppi J.;Quattrociocchi I.;Aloisi M.;Colamarino E.;Pichiorri F.;Cincotti F.;
2024

Abstract

Disorders of consciousness (DoC) are characterized by alteration in arousal and/or awareness commonly caused by severe brain injury. There exists a consensus on adopting advanced neuroimaging and electrophysiological procedures to improve diagnosis/prognosis of DoC patients. Currently, these procedures are prevalently applied in a research-oriented context and their translation into clinical practice is yet to come. The aim of the study consisted in the identification of measures derived from routinary electroencephalography (EEG) able to support clinicians in the prediction of DoC patients' outcome. In the present study, a routine EEG was recorded during rest from a sample of 58 DoC patients clinically diagnosed as Unresponsive Wakefulness State (UWS) and Minimally Conscious State (MCS) and followed- up for 3 months. EEG-based features characterizing brain activity in terms of spectral content and resting state networks organization were used in a predictive machine learning model to i) identify which were the most promising features in predicting patients' exit from the DoC, regardless of the clinical diagnosis and ii) verify whether such features would have been the same best discriminating UWS from MCS or specific of the outcome prediction. A predictive machine learning model was built on EEG features related to spectral content and resting state networks which returned up to 85% of performance accuracy in outcome prediction and 76% in DoC state recognition (UWS vs MCS). We provided preliminary evidence for the exploitation of a routine EEG to improve the clinical management of non- communicative patients to be confirmed in a larger DoC population.
2024
Brain injuries; Brain modeling; Connectivity estimation; diagnosis; Disorders of Consciousness; EEG; Electroencephalography; Feature extraction; graph theory; machine learning; Predictive models; prognostic factors; Recording; Spectral analysis
01 Pubblicazione su rivista::01a Articolo in rivista
EEG-derived markers to improve prognostic evaluation of Disorders of Consciousness / Toppi, J.; Quattrociocchi, I.; Riccio, A.; Dippolito, M.; Aloisi, M.; Colamarino, E.; Pichiorri, F.; Cincotti, F.; Formisano, R.; Mattia, D.. - In: IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS. - ISSN 2168-2194. - PP:(2024), pp. 1-13. [10.1109/JBHI.2024.3445118]
File allegati a questo prodotto
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1721955
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? 0
  • Scopus 0
  • ???jsp.display-item.citation.isi??? ND
social impact