Prediction of Recovery from Traumatic Brain Injury with EEG Power Spectrum in Combination of Independent Component Analysis and RUSBoost Model

The computational electroencephalogram (EEG) is recently garnering significant attention in examining whether the quantitative EEG (qEEG) features can be used as new predictors for the prediction of recovery in moderate traumatic brain injury (TBI). However, the brain’s recorded electrical activity has always been contaminated with artifacts, which in turn further impede the subsequent processing steps. As a result, it is crucial to devise a strategy for meticulously flagging and extracting clean EEG data to retrieve high-quality discriminative features for successful model development. This work proposed the use of multiple artifact rejection algorithms (MARA), which is an independent component analysis (ICA)-based algorithm, to eliminate artifacts automatically, and explored their effects on the predictive performance of the random undersampling boosting (RUSBoost) model. Continuous EEG were acquired using 64 electrodes from 27 moderate TBI patients at four weeks to one-year post-accident. The MARA incorporates an artifact removal stage based on ICA prior to RUSBoost, SVM, DT, and k-NN classification. The area under the curve (AUC) of RUSBoost was higher in absolute power spectral density (PSD) in AUCδ = 0.75, AUC α = 0.73 and AUCθ = 0.71 bands than SVM, DT, and k-NN. The MARA has provided a good generalization performance of the RUSBoost prediction model.

Intracorporeal Cortical Telemetry as a Step to Automatic Closed-Loop EEG-Based CI Fitting: A Proof of Concept

Electrically evoked auditory potentials have been used to predict auditory thresholds in patients with a cochlear implant (CI). However, with exception of electrically evoked compound action potentials (eCAP), conventional extracorporeal EEG recording devices are still needed. Until now, built-in (intracorporeal) back-telemetry options are limited to eCAPs. Intracorporeal recording of auditory responses beyond the cochlea is still lacking. This study describes the feasibility of obtaining longer latency cortical responses by concatenating interleaved short recording time windows used for eCAP recordings. Extracochlear reference electrodes were dedicated to record cortical responses, while intracochlear electrodes were used for stimulation, enabling intracorporeal telemetry (i.e., without an EEG device) to assess higher cortical processing in CI recipients. Simultaneous extra- and intra-corporeal recordings showed that it is feasible to obtain intracorporeal slow vertex potentials with a CI similar to those obtained by conventional extracorporeal EEG recordings. Our data demonstrate a proof of concept of closed-loop intracorporeal auditory cortical response telemetry (ICT) with a cochlear implant device. This research breaks new ground for next generation CI devices to assess higher cortical neural processing based on acute or continuous EEG telemetry to enable individualized automatic and/or adaptive CI fitting with only a CI.

Combining Transcranial Doppler and EEG Data to Predict Delayed Cerebral Ischemia After Subarachnoid Hemorrhage

Background and Objectives:Delayed cerebral ischemia (DCI) is the leading complication of subarachnoid hemorrhage (SAH). Because DCI was traditionally thought to be caused by large vessel vasospasm, transcranial Doppler ultrasounds (TCDs) have been the standard of care. Continuous EEG has emerged as a promising complementary monitoring modality and predicts increased DCI risk. Our objective was to determine whether combining EEG and TCD data improves prediction of DCI after SAH. We hypothesize that integrating these diagnostic modalities improves DCI prediction.Methods:We retrospectively assessed patients with moderate-severe SAH (2011-2015, Fisher=3-4 or Hunt-Hess=4-5) who had both prospective TCD and EEG acquisition during hospitalization. Middle cerebral artery (MCA) peak systolic velocities (PSV) and the presence or absence of epileptiform abnormalities (EA), defined as seizures, epileptiform discharges, and rhythmic/periodic activity, were recorded daily. Logistic regressions were used to identify significant covariates of EA and TCD to predict DCI. Group-Based Trajectory Modeling (GBTM) was used to account for changes over time by identifying distinct group trajectories of MCA PSV and EA associated with DCI risk.Results:We assessed 107 patients, and DCI developed in 56 (51.9%). Univariate predictors of DCI are presence of high-MCA velocity (PSV≥200cm/s, Se=27%, Sp=89%) and EA (Se=66%, Sp=62%) both on or before day 3. Two univariate GBTM trajectories of EA predicted DCI (Se=64%, Sp=62.75%). Logistic regression and GBTM models using both TCD and EEG monitoring performed better. The best logistic regression and GBTM models used both TCD and EEG data, Hunt-Hess score at admission, and aneurysm treatment as predictors of DCI (Logistic Regression: Se=90%, Sp=70%; GBTM: Se=89%, Sp=67%).Discussion:EEG and TCD biomarkers combined provide the best prediction of DCI. The conjunction of clinical variables with the timing of EA and high-MCA velocities improved model performance. These results suggest that TCD and cEEG are promising complementary monitoring modalities for DCI prediction. Our model has potential to serve as a decision support tool in SAH management.Classification of Evidence:This study provides Class II evidence that combined TCD and EEG monitoring can identify delayed cerebral ischemia after subarachnoid hemorrhage.

Quantitative Electroencephalography for Early Detection of Elevated Intracranial Pressure in Critically Ill Children: Case Series and Proposed Protocol

Objective: To describe quantitative EEG (electroencephalography) suppression ratio in children with increased intracranial pressure comparing acute suppression ratio changes to imaging and/or examination findings. Methods: We retrospectively reviewed the suppression ratio from patients with neuroimaging and /or examination findings of increased intracranial pressure while on continuous EEG. The time of the first change in the suppression ratio was compared to the time of the first image and/or examination change confirming increased intracranial pressure. Results: Thirteen patients with a median age of 3.1 years(interquartile range 1.8-6.3) had a rise in the suppression ratio with median time from identification to acute neuroimaging or examination of increased intracranial pressure of 3.12 hours (interquartile range 2.2-33.5) after the first increase in the suppression ratio. Conclusions: Acute suppression ratio increase is seen prior to imaging and/or examination findings of increased intracranial pressure. With further study, the suppression ratio can be targeted with intracranial pressure–lowering agents to prevent morbidity and mortality associated with increased intracranial pressure.

Current and Future Uses of Continuous EEG in the NICU

Continuous EEG (cEEG) is a fundamental neurodiagnostic tool in the care of critically ill neonates and is increasingly recommended. cEEG enhances prognostication via assessment of the background brain activity, plays a role in predicting which neonates are at risk for seizures when combined with clinical factors, and allows for accurate diagnosis and management of neonatal seizures. Continuous EEG is the gold standard method for diagnosis of neonatal seizures and should be used for detection of seizures in high-risk clinical conditions, differential diagnosis of paroxysmal events, and assessment of response to treatment. High costs associated with cEEG are a limiting factor in its widespread implementation. Centralized remote cEEG interpretation, automated seizure detection, and pre-natal EEG are potential future applications of this neurodiagnostic tool.

P.096 Optimizing the Use of Continuous EEG Monitoring in Neonatal Encephalopathy

Background: Newborns with hypoxic-ischemic encephalopathy (HIE) are at high risk for seizures, the majority of which have no clinical signs and therefore require continuous electroencephalographic (cEEG) monitoring for their detection. We sought to determine which neonates are at highest risk for seizures in order to optimize allocation of scarce cEEG resources. Methods: We identified term neonates diagnosed with HIE who underwent at least 24 hours of protocol-based cEEG monitoring between 2016 and 2019. We quantified seizure incidence, timing and burden, and correlated these with potential risk factors such as HIE severity, use of therapeutic hypothermia, preceding suspected clinical seizures, amplitude-integrated EEG (aEEG) background and patterns suspicious for seizures, and use of anti-seizure drugs. Results: cEEG monitoring was completed in 218 neonates with HIE, of whom 164 (75%) underwent therapeutic hypothermia. Preceding clinical/aEEG seizures occurred in 147 (67%), 99 (67%) of whom had been cooled but only 22 (10%) had cEEG-confirmed seizures. Characterization of seizure burden and correlation with potential risk factors is ongoing. Conclusions: Although seizures are commonly suspected in neonates with HIE, they are infrequently confirmed during cEEG monitoring, creating opportunities for more efficient risk-based allocation of cEEG resources.

Decoding subjective emotional arousal from eeg during an immersive virtual reality experience

Immersive virtual reality (VR) enables naturalistic neuroscientific studies while maintaining experimental control, but dynamic and interactive stimuli pose methodological challenges. We here probed the link between emotional arousal, a fundamental property of affective experience, and parieto-occipital alpha power under naturalistic stimulation: 37 young healthy adults completed an immersive VR experience, which included rollercoaster rides, while their EEG was recorded. They then continuously rated their subjective emotional arousal while viewing a replay of their experience. The association between emotional arousal and parieto-occipital alpha power was tested and confirmed by (1) decomposing the continuous EEG signal while maximizing the comodulation between alpha power and arousal ratings and by (2) decoding periods of high and low arousal with discriminative common spatial patterns and a Long Short-Term Memory recurrent neural network. We successfully combine EEG and a naturalistic immersive VR experience to extend previous findings on the neurophysiology of emotional arousal towards real-world neuroscience.