Spectral and network characterization of focal seizure types and phases.

BACKGROUND AND OBJECTIVE: Currently, epileptic seizure characterization relies on several clinical features that allow their classification into different types. The present work aims to characterize both seizure types and phases based exclusively on electrophysiological characteristics. METHODS: Based on the analysis of intracranial EEG recordings of 129 seizures from 22 patients obtained from the European Epilepsy Database, network and spectral measures were calculated in five-second temporal windows. Statistically significant differences between each window of the seizure phases (preictal, ictal, and postictal) and the interictal phase were used to identify/classify seizure types and their phases. A support vector machine (SVM) working on a multidimensional feature space of network and spectral measures was implemented for the classification of each seizure type; a traditional statistical approach was also conducted to highlight the underlying patterns to each seizure type or phase. RESULTS: The percentage of correct classification of seizure types, corrected by chance, provided by the SVM exceeded 70%, considering all measures and the entire seizure (preictal + ictal + postictal). This percentage increased to more than 80% when all the measures during the ictal period for the depth electrodes or during the postictal for subdural electrodes were considered. Regarding the statistical approach, several measures presented a monotonic ascending and descending behavior with respect to seizure severity; these changes were observed during the ictal and postictal periods. Some measures were specific of each seizure type. CONCLUSIONS: Our results provide a new framework to seizure characterization and reveal the possibility of an exclusively intracranial EEG-based classification. This could be used to build an automatic seizure classification system and provides new evidence of the network-related physiopathology of epilepsies. Thus, the novelty of this work is the possibility of differentiating seizure types based exclusively on the EEG recordings, providing evidence of the underlying patterns or characteristics to each seizure type and/or phase that would allow their optimal classification.

Potential EEG biomarkers of sedation doses in intensive care patients unveiled by using a machine learning approach.

OBJECTIVE: Sedation of neurocritically ill patients is one of the most challenging situation in ICUs. Quantitative knowledge on the sedation effect on brain activity in that complex scenario could help to uncover new markers for sedation assessment. Hence, we aim to evaluate the existence of changes of diverse EEG-derived measures in deeply-sedated (RASS-Richmond agitation-sedation scale -4 and -5) neurocritically ill patients, and also whether sedation doses are related with those eventual changes. APPROACH: We performed an observational prospective cohort study in the intensive care unit of the Hospital de la Princesa. Twenty-six adult patients suffered from traumatic brain injury and subarachnoid hemorrhage were included in the present study. Long-term continuous electroencephalographic (EEG) recordings (2141 h) and hourly annotated information were used to determine the relationship between intravenous sedation infusion doses and network and spectral EEG measures. To do that, two different strategies were followed: assessment of the statistical dependence between both variables using the Spearman correlation rank and by performing an automatic classification method based on a machine learning algorithm. MAIN RESULTS: More than 60% of patients presented a correlation greater than 0.5 in at least one of the calculated EEG measures with the sedation dose. The automatic classification method presented an accuracy of 84.3% in discriminating between different sedation doses. In both cases the nodes' degree was the most relevant measurement. SIGNIFICANCE: The results presented here provide evidences of brain activity changes during deep sedation linked to sedation doses. Particularly, the capability of network EEG-derived measures in discriminating between different sedation doses could be the framework for the development of accurate methods for sedation levels assessment.

Identifying causal relationships between EEG activity and intracranial pressure changes in neurocritical care patients.

OBJECTIVE: To explore and assess the relationship between electroencephalography (EEG) activity and intracranial pressure (ICP) in patients suffering from traumatic brain injury (TBI) and subarachnoid hemorrhage (SAH) during their stay in an intensive care unit. APPROACH: We performed an observational prospective cohort study of adult patients suffering from TBI or SAH. Continuous EEG-ECG was performed during ICP monitoring. In every patient, variables derived from the EEG were calculated and the Granger causality (GC) methodology was employed to assess whether, and in which direction, there is any relationship between EEG and ICP. MAIN RESULTS: One-thousand fifty-five hours of continuous multimodal monitoring were analyzed in 21 patients using the GC test. During 37.88% of the analyzed time, significant GC statistic was found in the direction from the EEG activity to the ICP, with typical lags of 25-50 s between them. When recordings were adjusted by sedation-perfusion and/or bolus-and handling, these percentages hardly changed. SIGNIFICANCE: Long-lasting, continuous and simultaneous EEG and ICP recordings from TBI and SAH patients provide highly rich and useful information, which has allowed for uncovering a strong relationship between both signals. The use of this relationship could lead to developing a medical device to measure ICP in a non-invasive way.