BackgroundDespite multimodal assessment (clinical examination, biology, brain MRI, electroencephalography, somatosensory evoked potentials, mismatch negativity at auditory evoked potentials), coma prognostic evaluation remains challenging.MethodsWe present here a method to predict the return to consciousness and good neurological outcome based on classification of auditory evoked potentials obtained during an oddball paradigm. Data from event-related potentials (ERPs) were recorded noninvasively using four surface electroencephalography (EEG) electrodes in a cohort of 29 post-cardiac arrest comatose patients (between day 3 and day 6 following admission). We extracted retrospectively several EEG features (standard deviation and similarity for standard auditory stimulations and number of extrema and oscillations for deviant auditory stimulations) from the time responses in a window of few hundreds of milliseconds. The responses to the standard and the deviant auditory stimulations were thus considered independently. By combining these features, based on machine learning, we built a two-dimensional map to evaluate possible group clustering.ResultsAnalysis in two-dimensions of the present data revealed two separated clusters of patients with good versus bad neurological outcome. When favoring the highest specificity of our mathematical algorithms (0.91), we found a sensitivity of 0.83 and an accuracy of 0.90, maintained when calculation was performed using data from only one central electrode. Using Gaussian, K-neighborhood and SVM classifiers, we could predict the neurological outcome of post-anoxic comatose patients, the validity of the method being tested by a cross-validation procedure. Moreover, the same results were obtained with one single electrode (Cz).Conclusionstatistics of standard and deviant responses considered separately provide complementary and confirmatory predictions of the outcome of anoxic comatose patients, better assessed when combining these features on a two-dimensional statistical map. The benefit of this method compared to classical EEG and ERP predictors should be tested in a large prospective cohort. If validated, this method could provide an alternative tool to intensivists, to better evaluate neurological outcome and improve patient management, without neurophysiologist assistance.

Background: Severity of neuronal damage in comatose patients following anoxic brain injury is assessed through a multimodal evaluation. However, predicting the return to full consciousness of hospitalized post-anoxic comatose patients remains challenging. Methods: We present here a method to predict the return to consciousness and good neurological outcome based on the analysis of responses to auditory periodic stimulations to auditory evoked potentials. We extracted several EEG features from the time series responses in a window of few hundreds of milliseconds from the standard and deviant auditory stimulations that we considered independently. By combining these features, we built a two-dimensional map to evaluate possible group clustering. Using Gaussian, K-neighbourhood and SVM classifiers, we could predict the neurological outcome of post-anoxic comatose patients, the validity of the method being tested by a cross-validation procedure. This method was developed using data acquired retrospectively in a cohort of 29 post-cardiac arrest comatose patients, recorded between day 3 and day 6 following admission. Data from event-related potentials (ERPs) were recorded non-invasively with four surface cranial electrodes at electro-encephalography (EEG), that we computed secondarily. Results: Analysis in two-dimensions of the present data revealed two separated clusters of patients with good versus bad neurological outcome. When favouring the highest specificity of our mathematical algorithms (0.91), we found a sensitivity of 0.83 and an accuracy of 0.90, maintained when calculation was performed using data from only one central electrode. To conclude, statistics of standard and deviant responses considered separately provide complementary and confirmatory predictions of the outcome of anoxic comatose patients, better assessed when combining these features on a two-dimensional statistical map. The benefit of this method compared to classical EEG and ERP predictors should be tested in a large prospective cohort. If validated, this method could provide an alternative tool to intensivists, to better evaluate neurological outcome and improve patient management, without neurophysiologist assistance.

The severity of neuronal damages in comatose patients following anoxic brain injury can be probed by evoked auditory responses. However, it remains challenging to predict the return to full consciousness of post-anoxic coma of hospitalized patients. We presented here a method to predict the return to consciousness based on the analysis of periodic responses to auditory stimulations, recorded from surface cranial electrodes. The input data are event-related potentials (ERPs), recorded non-invasively with electro-encephalography (EEG). We extracted several novel features from the time series responses in a window of few hundreds of milliseconds from deviant and non-deviant auditory stimulations. We use these features to construct two-dimensional statistical maps, that show two separated clusters for recovered (conscience) and deceased patients, leading to a high classification success as tested by a cross-validation procedure. Finally, using Gaussian, K-neighborhood and SVM classifiers, we construct probabilistic maps to predict the outcome of post-anoxic coma. To conclude, statistics of deviant and non-deviant responses considered separately provide complementary and confirmatory predictions for the outcome of anoxic coma.