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Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach

Authors :
Francesco Di Gregorio
Fabio La Porta
Valeria Petrone
Simone Battaglia
Silvia Orlandi
Giuseppe Ippolito
Vincenzo Romei
Roberto Piperno
Giada Lullini
Source :
Biomedicines, Vol 10, Iss 8, p 1897 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients’ etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.

Details

Language :
English
ISSN :
22279059
Volume :
10
Issue :
8
Database :
Directory of Open Access Journals
Journal :
Biomedicines
Publication Type :
Academic Journal
Accession number :
edsdoj.f8b90c88ea9945b7b0e3dbce733d4a4d
Document Type :
article
Full Text :
https://doi.org/10.3390/biomedicines10081897