EEG dynamical correlates of focal and diffuse causes of coma

Quantitative EEG signatures of delirium and coma in mechanically ventilated ICU patients

OBJECTIVE

To identify quantitative electroencephalography (EEG)-based indicators of delirium or coma in mechanically ventilated patients.

METHODS

We prospectively enrolled 28 mechanically ventilated intensive care unit (ICU) patients to undergo 24-hour continuous EEG, 25 of whom completed the study. We assessed patients twice daily using the Richmond Agitation-Sedation Scale (RASS) and Confusion Assessment Method for the ICU (CAM-ICU). We evaluated the spectral profile, regional connectivity and complexity of 5-minute EEG segments after each assessment. We used penalized regression to select EEG metrics associated with delirium or coma, and compared mixed-effects models predicting delirium with and without the selected EEG metrics.

RESULTS

Delta variability, high-beta variability, relative theta power, and relative alpha power contributed independently to EEG-based identification of delirium or coma. A model with these metrics achieved better prediction of delirium or coma than a model with clinical variables alone (Akaike Information Criterion: 36 vs 43, p = 0.006 by likelihood ratio test). The area under the receiver operating characteristic curve for an ad hoc hypothetical delirium score using these metrics was 0.94 (95%CI 0.83-0.99).

CONCLUSIONS

We identified four EEG metrics that, in combination, provided excellent discrimination between delirious/comatose and non-delirious mechanically ventilated ICU patients.

SIGNIFICANCE

Our findings give insight to neurophysiologic changes underlying delirium and provide a basis for pragmatic, EEG-based delirium monitoring technology.

Computational Methods for Resting-State EEG of Patients With Disorders of Consciousness

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.

Improved Patient-Independent System for Detection of Electrical Onset of Seizures

PURPOSE

To design a non-patient-specific system to detect the electrical onset of seizures in patients with temporal lobe epilepsy.

METHODS

We used EEG data from 29 seizures of 18 temporal lobe epilepsy patients who underwent multiday video-scalp EEG monitoring as part of their presurgical evaluations. We segmented each data set into preictal and ictal phases, and identified spectral entropy, spectral energy, and signal energy as useful features for discriminating normal and seizure conditions. The performance of five different classifiers was analyzed using these features to design an automated detection system.

RESULTS

Among the five classifiers, decision tree, k-nearest neighbor, and support vector machine performed with sensitivity (specificity) of 79% (81%), 75% (85%), and 80% (86%), respectively. The other two, linear discriminant algorithm and Naive Bayes classifiers, performed with sensitivity (specificity) of 54% (94%), 47% (96%), respectively.

CONCLUSIONS

The support vector machine-based seizure detection system showed better detection capability in terms of sensitivity and specificity measures as compared to linear discriminant algorithm, Naive Bayes, decision tree, and k-nearest neighbor classifiers.

CONCLUSIONS

Our study shows that a generalized system to detect the electrical onset of seizures in temporal lobe epilepsy using scalp-recorded EEG is possible. If confirmed on a larger data set, our findings may have significant implications for the management of seizures, especially in patients with drug-resistant epilepsy.