Patients who survive brain injuries may develop Disorders of Consciousness (DOC) such as Coma, Vegetative State (VS) or Minimally Conscious State (MCS). Unfortunately, the rate of misdiagnosis between VS and MCS due to clinical judgment is high. Therefore, diagnostic decision support systems aiming to correct any differentiation between VS and MCS are essential for the characterization of an adequate treatment and an effective prognosis. In recent decades, there has been a growing interest in the new EEG computational techniques. We have reviewed how resting-state EEG is computationally analyzed to support differential diagnosis between VS and MCS in view of applicability of these methods in clinical practice. The studies available so far have used different techniques and analyses; it is therefore hard to draw general conclusions. Studies using a discriminant analysis with a combination of various factors and reporting a cut-off are among the most interesting ones for a future clinical application.

Computational methods for resting-state EEG of patients with disorders of consciousness

Corchs, Silvia;Mauri, Giancarlo;
2019-01-01

Abstract

Patients who survive brain injuries may develop Disorders of Consciousness (DOC) such as Coma, Vegetative State (VS) or Minimally Conscious State (MCS). Unfortunately, the rate of misdiagnosis between VS and MCS due to clinical judgment is high. Therefore, diagnostic decision support systems aiming to correct any differentiation between VS and MCS are essential for the characterization of an adequate treatment and an effective prognosis. In recent decades, there has been a growing interest in the new EEG computational techniques. We have reviewed how resting-state EEG is computationally analyzed to support differential diagnosis between VS and MCS in view of applicability of these methods in clinical practice. The studies available so far have used different techniques and analyses; it is therefore hard to draw general conclusions. Studies using a discriminant analysis with a combination of various factors and reporting a cut-off are among the most interesting ones for a future clinical application.
2019
2019
computational methods; EEG; DOC; VS; MCS; machine learning; resting state analysis; deep learning; Computational methods; Deep learning; DOC; EEG; Machine learning; MCS; Resting state analysis; VS
Corchs, Silvia; Chioma, Giovanni; Dondi, Riccardo; Gasparini, Francesca; Manzoni, Sara; Markowska-Kacznar, Urszula; Mauri, Giancarlo; Zoppis, Italo; Morreale, Angela
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2127970
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