Pitfalls in scalp EEG: Current obstacles and future directions.

Although electroencephalography (EEG) serves a critical role in the evaluation and management of seizure disorders, it is commonly misinterpreted, resulting in avoidable medical, social, and financial burdens to patients and health care systems. Overinterpretation of sharply contoured transient waveforms as being representative of interictal epileptiform abnormalities lies at the core of this problem. However, the magnitude of these errors is amplified by the high prevalence of paroxysmal events exhibited in clinical practice that compel investigation with EEG. Neurology training programs, which vary considerably both in the degree of exposure to EEG and the composition of EEG didactics, have not effectively addressed this widespread issue. Implementation of competency-based curricula in lieu of traditional educational approaches may enhance proficiency in EEG interpretation amongst general neurologists in the absence of formal subspecialty training. Efforts in this regard have led to the development of a systematic, high-fidelity approach to the interpretation of epileptiform discharges that is readily employable across medical centers. Additionally, machine learning techniques hold promise for accelerating accurate and reliable EEG interpretation, particularly in settings where subspecialty interpretive EEG services are not readily available. This review highlights common diagnostic errors in EEG interpretation, limitations in current educational paradigms, and initiatives aimed at resolving these challenges.

Quantifying interictal intracranial EEG to predict focal epilepsy.

Introduction: Intracranial EEG (IEEG) is used for 2 main purposes, to determine: (1) if epileptic networks are amenable to focal treatment and (2) where to intervene. Currently these questions are answered qualitatively and sometimes differently across centers. There is a need for objective, standardized methods to guide surgical decision making and to enable large scale data analysis across centers and prospective clinical trials. Methods: We analyzed interictal data from 101 patients with drug resistant epilepsy who underwent presurgical evaluation with IEEG. We chose interictal data because of its potential to reduce the morbidity and cost associated with ictal recording. 65 patients had unifocal seizure onset on IEEG, and 36 were non-focal or multi-focal. We quantified the spatial dispersion of implanted electrodes and interictal IEEG abnormalities for each patient. We compared these measures against the "5 Sense Score (5SS)," a pre-implant estimate of the likelihood of focal seizure onset, and assessed their ability to predict the clinicians' choice of therapeutic intervention and the patient outcome. Results: The spatial dispersion of IEEG electrodes predicted network focality with precision similar to the 5SS (AUC = 0.67), indicating that electrode placement accurately reflected pre-implant information. A cross-validated model combining the 5SS and the spatial dispersion of interictal IEEG abnormalities significantly improved this prediction (AUC = 0.79; p<0.05). The combined model predicted ultimate treatment strategy (surgery vs. device) with an AUC of 0.81 and post-surgical outcome at 2 years with an AUC of 0.70. The 5SS, interictal IEEG, and electrode placement were not correlated and provided complementary information. Conclusions: Quantitative, interictal IEEG significantly improved upon pre-implant estimates of network focality and predicted treatment with precision approaching that of clinical experts. We present this study as an important step in building standardized, quantitative tools to guide epilepsy surgery.

Quantifying trial-by-trial variability during cortico-cortical evoked potential mapping of epileptogenic tissue.

OBJECTIVE: Measuring cortico-cortical evoked potentials (CCEPs) is a promising tool for mapping epileptic networks, but it is not known how variability in brain state and stimulation technique might impact the use of CCEPs for epilepsy localization. We test the hypotheses that (1) CCEPs demonstrate systematic variability across trials and (2) CCEP amplitudes depend on the timing of stimulation with respect to endogenous, low-frequency oscillations. METHODS: We studied 11 patients who underwent CCEP mapping after stereo-electroencephalography electrode implantation for surgical evaluation of drug-resistant epilepsy. Evoked potentials were measured from all electrodes after each pulse of a 30 s, 1 Hz bipolar stimulation train. We quantified monotonic trends, phase dependence, and standard deviation (SD) of N1 (15-50 ms post-stimulation) and N2 (50-300 ms post-stimulation) amplitudes across the 30 stimulation trials for each patient. We used linear regression to quantify the relationship between measures of CCEP variability and the clinical seizure-onset zone (SOZ) or interictal spike rates. RESULTS: We found that N1 and N2 waveforms exhibited both positive and negative monotonic trends in amplitude across trials. SOZ electrodes and electrodes with high interictal spike rates had lower N1 and N2 amplitudes with higher SD across trials. Monotonic trends of N1 and N2 amplitude were more positive when stimulating from an area with higher interictal spike rate. We also found intermittent synchronization of trial-level N1 amplitude with low-frequency phase in the hippocampus, which did not localize the SOZ. SIGNIFICANCE: These findings suggest that standard approaches for CCEP mapping, which involve computing a trial-averaged response over a .2-1 Hz stimulation train, may be masking inter-trial variability that localizes to epileptogenic tissue. We also found that CCEP N1 amplitudes synchronize with ongoing low-frequency oscillations in the hippocampus. Further targeted experiments are needed to determine whether phase-locked stimulation could have a role in localizing epileptogenic tissue.

Spike patterns surrounding sleep and seizures localize the seizure-onset zone in focal epilepsy.

OBJECTIVE: Interictal spikes help localize seizure generators as part of surgical planning for drug-resistant epilepsy. However, there are often multiple spike populations whose frequencies change over time, influenced by brain state. Understanding state changes in spike rates will improve our ability to use spikes for surgical planning. Our goal was to determine the effect of sleep and seizures on interictal spikes, and to use sleep and seizure-related changes in spikes to localize the seizure-onset zone (SOZ). METHODS: We performed a retrospective analysis of intracranial electroencephalography (EEG) data from patients with focal epilepsy. We automatically detected interictal spikes and we classified different time periods as awake or asleep based on the ratio of alpha to delta power, with a secondary analysis using the recently published SleepSEEG algorithm. We analyzed spike rates surrounding sleep and seizures. We developed a model to localize the SOZ using state-dependent spike rates. RESULTS: We analyzed data from 101 patients (54 women, age range 16-69). The normalized alpha-delta power ratio accurately classified wake from sleep periods (area under the curve = .90). Spikes were more frequent in sleep than wakefulness and in the post-ictal compared to the pre-ictal state. Patients with temporal lobe epilepsy had a greater wake-to-sleep and pre- to post-ictal spike rate increase compared to patients with extra-temporal epilepsy. A machine-learning classifier incorporating state-dependent spike rates accurately identified the SOZ (area under the curve = .83). Spike rates tended to be higher and better localize the seizure-onset zone in non-rapid eye movement (NREM) sleep than in wake or REM sleep. SIGNIFICANCE: The change in spike rates surrounding sleep and seizures differs between temporal and extra-temporal lobe epilepsy. Spikes are more frequent and better localize the SOZ in sleep, particularly in NREM sleep. Quantitative analysis of spikes may provide useful ancillary data to localize the SOZ and improve surgical planning.

Extracting seizure frequency from epilepsy clinic notes: a machine reading approach to natural language processing.

OBJECTIVE: Seizure frequency and seizure freedom are among the most important outcome measures for patients with epilepsy. In this study, we aimed to automatically extract this clinical information from unstructured text in clinical notes. If successful, this could improve clinical decision-making in epilepsy patients and allow for rapid, large-scale retrospective research. MATERIALS AND METHODS: We developed a finetuning pipeline for pretrained neural models to classify patients as being seizure-free and to extract text containing their seizure frequency and date of last seizure from clinical notes. We annotated 1000 notes for use as training and testing data and determined how well 3 pretrained neural models, BERT, RoBERTa, and Bio_ClinicalBERT, could identify and extract the desired information after finetuning. RESULTS: The finetuned models (BERTFT, Bio_ClinicalBERTFT, and RoBERTaFT) achieved near-human performance when classifying patients as seizure free, with BERTFT and Bio_ClinicalBERTFT achieving accuracy scores over 80%. All 3 models also achieved human performance when extracting seizure frequency and date of last seizure, with overall F1 scores over 0.80. The best combination of models was Bio_ClinicalBERTFT for classification, and RoBERTaFT for text extraction. Most of the gains in performance due to finetuning required roughly 70 annotated notes. DISCUSSION AND CONCLUSION: Our novel machine reading approach to extracting important clinical outcomes performed at or near human performance on several tasks. This approach opens new possibilities to support clinical practice and conduct large-scale retrospective clinical research. Future studies can use our finetuning pipeline with minimal training annotations to answer new clinical questions.

Patterns of seizure spread in temporal lobe epilepsy are associated with distinct white matter tracts.

OBJECTIVE: It is commonly hypothesized that seizure spread patterns in patients with focal epilepsy are associated with structural brain pathways. However, this relationship is poorly understood and has not been fully demonstrated in patients with temporal lobe epilepsy. Here, we sought to determine whether directionality of seizure spread (DSS) is associated with specific cerebral white matter tracts in patients with temporal lobe epilepsy. METHODS: Thirty-three adult patients with temporal lobe epilepsy who underwent stereoelectroencephalography (sEEG) and magnetic resonance diffusion tensor imaging (MR-DTI) as part of their standard-of-care clinical evaluation were included in the study. DSS was defined as anterior-posterior (AP) or medial-lateral (ML) spread based upon sEEG evaluation by two independent specialists who demonstrated excellent inter-rater agreement (Cohen's kappa = .92). DTI connectometry was used to assess differences between seizure spread pattern groups along major fiber pathways regarding fractional anisotropy (FA). RESULTS: Twenty-four participants showed seizures with AP spread and nine participants showed seizures with ML spread. There were no significant differences between the groups on their demographic and clinical profile. Patients with ML seizures had higher FA along the corpus callosum and, to a lesser degree, some portions of the bilateral cingulate tracts. In contrast, patients with AP seizures had higher FA along several anterior-posterior white matter projections bundles, including the cingulate fasciculus and the inferior longitudinal, with significantly less involvement of the corpus callosum compared with ML seizures. SIGNIFICANCE: This study confirms the hypothesis that the anatomical pattern of electrophysiological ictal propagation is associated with the structural reinforcement of supporting pathways in temporal lobe epilepsy. This observation can help elucidate mechanisms of ictal propagation and may guide future translational approaches to curtail seizure spread.

Rate of colonization of health care workers by carbapenem-resistant Enterobacteriaceae in an endemic hospital: A prospective study.

The role of health care workers in transmission of carbapenem-resistant Enterobacteriaceae (CRE) has not been evaluated thoroughly. We sought to determine the rate of fecal carriage of CRE among health care workers in our hospital, which is endemic for CRE (prevalence of 19 out of 800 beds and incidence of 128 out of 49,325 hospital admissions). We found no carriers among the 177 health care workers that participated in the study, suggesting that transmission does not occur through personnel gastrointestinal carriage of the bacteria.