Monitoring epilepsy in the intensive care unit: Current state of facts and potential interest of high density EEG

Prevention, Treatment, and Monitoring of Seizures in the Intensive Care Unit

The diagnosis and management of seizures in the critically ill patient can sometimes present a unique challenge for practitioners due to lack of exposure and complex patient comorbidities. The reported incidence varies between 8% and 34% of critically ill patients, with many patients often showing no overt clinical signs of seizures. Outcomes in patients with unidentified seizure activity tend to be poor, and mortality significantly increases in those who have seizure activity longer than 30 min. Prompt diagnosis and provision of medical therapy are crucial in order to attain successful seizure termination and prevent poor outcomes. In this article, we review the epidemiology and pathophysiology of seizures in the critically ill, various seizure monitoring modalities, and recommended medical therapy.

An automated, machine learning-based detection algorithm for spike-wave discharges (SWDs) in a mouse model of absence epilepsy

OBJECTIVE

Manual detection of spike-wave discharges (SWDs) from electroencephalography (EEG) records is time intensive, costly, and subject to inconsistencies/biases. In addition, manual scoring often omits information on SWD confidence/intensity, which may be important for the investigation of mechanistic-based research questions. Our objective is to develop an automated method for the detection of SWDs in a mouse model of absence epilepsy that is focused on the characteristics of human scoring of preselected events to establish a confidence-based, continuous-valued scoring.

METHODS

We develop a support vector machine (SVM)-based algorithm for the automated detection of SWDs in the γ2R43Q mouse model of absence epilepsy. The algorithm first identifies putative SWD events using frequency- and amplitude-based peak detection. Four humans scored a set of 2500 putative events identified by the algorithm. Then, using predictors calculated from the wavelet transform of each event and the labels from human scoring, we trained an SVM to classify (SWD/nonSWD) and assign confidence scores to each event identified from 60, 24-hour EEG records. We provide a detailed assessment of intra- and interrater scoring that demonstrates advantages of automated scoring.

RESULTS

The algorithm scored SWDs along a continuum that is highly correlated with human confidence and that allows us to more effectively characterize ambiguous events. We demonstrate that events along our scoring continuum are temporally and proportionately correlated with abrupt changes in spectral power bands relevant to normal behavioral states including sleep.

SIGNIFICANCE

Although there are automated and semi-automated methods for the detection of SWDs in humans and rats, we contribute to the need for continued development of SWD detection in mice. Our results demonstrate the value of viewing detection of SWDs as a continuous classification problem to better understand "ground truth" in SWD detection (ie, the most reliable features agreed upon by humans that also correlate with objective physiologic measures).

A predictive epilepsy index based on probabilistic classification of interictal spike waveforms

Quantification of interictal spikes in EEG may provide insight on epilepsy disease burden, but manual quantification of spikes is time-consuming and subject to bias. We present a probability-based, automated method for the classification and quantification of interictal events, using EEG data from kainate- and saline-injected mice (C57BL/6J background) several weeks post-treatment. We first detected high-amplitude events, then projected event waveforms into Principal Components space and identified clusters of spike morphologies using a Gaussian Mixture Model. We calculated the odds-ratio of events from kainate- versus saline-treated mice within each cluster, converted these values to probability scores, P(kainate), and calculated an Hourly Epilepsy Index for each animal by summing the probabilities for events where the cluster P(kainate) > 0.5 and dividing the resultant sum by the record duration. This Index is predictive of whether an animal received an epileptogenic treatment (i.e., kainate), even if a seizure was never observed. We applied this method to an out-of-sample dataset to assess epileptiform spike morphologies in five kainate mice monitored for ~1 month. The magnitude of the Index increased over time in a subset of animals and revealed changes in the prevalence of epileptiform (P(kainate) > 0.5) spike morphologies. Importantly, in both data sets, animals that had electrographic seizures also had a high Index. This analysis is fast, unbiased, and provides information regarding the salience of spike morphologies for disease progression. Future refinement will allow a better understanding of the definition of interictal spikes in quantitative and unambiguous terms.

Seizures After Intracerebral Hemorrhage: Incidence, Risk Factors, and Impact on Mortality and Morbidity

OBJECTIVE

Spontaneous intracerebral hemorrhage (ICH) is one of the most frequent causes of epilepsy in the United States. However, reported risk factors for seizure after are inconsistent, and their impact on inpatient morbidity and mortality is unclear. We aimed to study the incidence, risk factors, and impact of seizures after ICH in a nationwide patient sample.

METHODS

We queried the Nationwide Inpatient Sample for patients admitted to the hospital with a primary diagnosis of ICH between the years 1999 and 2011. Patients were subsequently dichotomized into groups of those with a diagnosis consistent with seizure and those without. Multivariate logistic regression was used to assess risk factors for seizure in this patient sample, and the association between seizures and mortality and morbidity. Logistic regression was then used for trend analysis of incidence of seizure diagnoses over time.

RESULTS

We identified 220,075 patients admitted with a primary diagnosis of ICH. Of these, 11.87% had a diagnosis consistent with seizure. Factors associated with increased risk of seizure after ICH included higher categorical van Walraven score, encephalopathy, alcohol abuse, solid tumor, and prior stroke. Seizure was independently associated with decreased odds of morbidity (odds ratio [OR], 0.89; 95% confidence interval [CI], 0.86-0.92) and mortality (OR, 0.75; 95% CI, 0.72-0.77) in multivariate models controlling for existing comorbidities.

CONCLUSIONS

Seizures after were associated with decreased mortality and morbidity despite attempts to correct for existing comorbidities. Continuous monitoring of these patients for seizures may not be necessary in all circumstances, despite their frequency.