Citations

Abstract Noninvasive wearable devices have great potential to aid the management of epilepsy, but these devices must have robust signal quality, and patients must be willing to wear them for long periods of time. Automated machine learning classification of wearable biosensor signals requires quantitative measures of signal quality to automatically reject poor‐quality or corrupt data segments. In this study, commercially available wearable sensors were placed on patients with epilepsy undergoing in‐hospital or in‐home electroencephalographic (EEG) monitoring, and healthy volunteers. Empatica E4 and Biovotion Everion were used to record accelerometry (ACC), photoplethysmography (PPG), and electrodermal activity (EDA). Byteflies Sensor Dots were used to record ACC and PPG, the Activinsights GENEActiv watch to record ACC, and Epitel Epilog to record EEG data. PPG and EDA signals were recorded for multiple days, then epochs of high‐quality, marginal‐quality, or poor‐quality data were visually identified by reviewers, and reviewer annotations were compared to automated signal quality measures. For ACC, the ratio of spectral power from 0.8 to 5 Hz to broadband power was used to separate good‐quality signals from noise. For EDA, the rate of amplitude change and prevalence of sharp peaks significantly differentiated between good‐quality data and noise. Spectral entropy was used to assess PPG and showed significant differences between good‐, marginal‐, and poor‐quality signals. EEG data were evaluated using methods to identify a spectral noise cutoff frequency. Patients were asked to rate the usability and comfort of each device in several categories. Patients showed a significant preference for the wrist‐worn devices, and the Empatica E4 device was preferred most often. Current wearable devices can provide high‐quality data and are acceptable for routine use, but continued development is needed to improve data quality, consistency, and management, as well as acceptability to patients.

Abstract Advances in ambulatory intracranial EEG devices have enabled objective analyses of circadian and multiday seizure periodicities, and seizure clusters in humans. This study characterizes circadian and multiday seizure periodicities, and seizure clusters in dogs with naturally occurring focal epilepsy, and considers the implications of an animal model for the study of seizure risk patterns, seizure forecasting and personalized treatment protocols. In this retrospective cohort study, 16 dogs were continuously monitored with ambulatory intracranial EEG devices designed for humans. Detailed medication records were kept for all dogs. Seizure periodicity was evaluated with circular statistics methods. Circular non-uniformity was assessed for circadian, 7-day and approximately monthly periods. The Rayleigh test was used to assess statistical significance, with correction for multiple comparisons. Seizure clusters were evaluated with Fano factor (index of dispersion) measurements, and compared to a Poisson distribution. Relationships between interseizure interval (ISI) and seizure duration were evaluated. Six dogs met the inclusion criteria of having at least 30 seizures and were monitored for an average of 65 weeks. Three dogs had seizures with circadian seizure periodicity, one dog had a 7-day periodicity, and two dogs had approximately monthly periodicity. Four dogs had seizure clusters and significantly elevated Fano factor values. There were subject-specific differences in the dynamics of ISI and seizure durations, both within and between lead and clustered seizure categories. Our findings show that seizure timing in dogs with naturally occurring epilepsy is not random, and that circadian and multiday seizure periodicities, and seizure clusters are common. Circadian, 7-day, and monthly seizure periodicities occur independent of antiseizure medication dosing, and these patterns likely reflect endogenous rhythms of seizure risk.

AbstractObjectiveTo rigorously compare automated atlas‐based and manual tracing hippocampal segmentation for accuracy, repeatability, and clinical acceptability given a relevant range of imaging abnormalities in clinical epilepsy.MethodsForty‐nine patients with hippocampal asymmetry were identified from our institutional radiology database, including two patients with significant anatomic deformations. Manual hippocampal tracing was performed by experienced technologists on 3T MPRAGE images, measuring hippocampal volume up to the tectal plate, excluding the hippocampal tail. The same images were processed using NeuroQuant and FreeSurfer software. Ten subjects underwent repeated manual hippocampal tracings by two additional technologists blinded to previous results to evaluate consistency. Ten patients with two clinical MRI studies had volume measurements repeated using NeuroQuant and FreeSurfer.ResultsFreeSurfer raw volumes were significantly lower than NeuroQuant (P < 0.001, right and left), and hippocampal asymmetry estimates were lower for both automatic methods than manual tracing (P < 0.0001). Differences remained significant after scaling volumes to age, gender, and scanner matched normative percentiles. Volume reproducibility was fair (0.4–0.59) for manual tracing, and excellent (>0.75) for both automated methods. Asymmetry index reproducibility was excellent (>0.75) for manual tracing and FreeSurfer segmentation and fair (0.4–0.59) for NeuroQuant segmentation. Both automatic segmentation methods failed on the two cases with anatomic deformations. Segmentation errors were visually identified in 25 NeuroQuant and 27 FreeSurfer segmentations, and nine (18%) NeuroQuant and six (12%) FreeSurfer errors were judged clinically significant.InterpretationAutomated hippocampal volumes are more reproducible than hand‐traced hippocampal volumes. However, these methods fail in some cases, and significant segmentation errors can occur.

OBJECTIVEEpilepsy is common among patients with supratentorial brain tumors; approximately 40%–70% of patients with glioma develop brain tumor–related epilepsy (BTRE). Intraoperative localization of the epileptogenic zone during surgical tumor resection (real-time data) may improve intervention techniques in patients with lesional epilepsy, including BTRE. Accurate localization of the epileptogenic signals requires electrodes with high-density spatial organization that must be placed on the cortical surface during surgery. The authors investigated a 360° high-density ring-shaped cortical electrode assembly device, called the “circular grid,” that allows for simultaneous tumor resection and real-time electrophysiology data recording from the brain surface.METHODSThe authors collected data from 99 patients who underwent awake craniotomy from January 2008 to December 2018 (29 patients with the circular grid and 70 patients with strip electrodes), of whom 50 patients were matched-pair analyzed (25 patients with the circular grid and 25 patients with strip electrodes). Multiple variables were then retrospectively assessed to determine if utilization of this device provides more accurate real-time data and improves patient outcomes.RESULTSMatched-pair analysis showed higher extent of resection (p = 0.03) and a shorter transient motor recovery period during the hospitalization course (by approximately 6.6 days, p ≤ 0.05) in the circular grid patients. Postoperative versus preoperative Karnofsky Performance Scale (KPS) score difference/drop was greater for the strip electrode patients (p = 0.007). No significant difference in postoperative seizures between the 2 groups was present (p = 0.80).CONCLUSIONSThe circular grid is a safe, feasible tool that grants direct access to the cortical surgical surface for tissue resection while simultaneously monitoring electrical activity. Application of the circular grid to different brain pathologies may improve intraoperative epileptogenic detection accuracy and functional outcomes, while decreasing postoperative complications.

Abstract Objective This study investigates high‐frequency oscillations ( HFO s; 65–600 Hz) as a biomarker of epileptogenic brain and explores three barriers to their clinical translation: (1) Distinguishing pathological HFO s (path HFO ) from physiological HFO s (phys HFO ). (2) Classifying tissue under individual electrodes as epileptogenic (3) Reproducing results across laboratories. Methods We recorded HFO s using intracranial EEG ( iEEG ) in 90 patients with focal epilepsy and 11 patients without epilepsy. In nine patients with epilepsy putative phys HFO s were induced by cognitive or motor tasks. HFO s were identified using validated detectors. A support vector machine ( SVM ) using HFO features was developed to classify tissue under individual electrodes as normal or epileptogenic. Results There was significant overlap in the amplitude, frequency, and duration distributions for spontaneous phys HFO , task induced phys HFO , and path HFO , but the amplitudes of the path HFO were higher ( P < 0.0001). High gamma path HFO had the strongest association with seizure onset zone ( SOZ ), and were elevated on SOZ electrodes in 70% of epilepsy patients ( P < 0.0001). Failure to resect tissue generating high gamma path HFO was associated with poor outcomes ( P < 0.0001). A SVM classified individual electrodes as epileptogenic with 63.9% sensitivity and 73.7% specificity using SOZ as the target. Interpretation A broader range of interictal path HFO (65–600 Hz) than previously recognized are biomarkers of epileptogenic brain, and are associated with SOZ and surgical outcome. Classification of HFO s into physiological or pathological remains challenging. Classification of tissue under individual electrodes was demonstrated to be feasible. The open source data and algorithms provide a resource for future studies.

OBJECTIVE Although it is still early in its application, laser interstitial thermal therapy (LiTT) has increasingly been employed as a surgical option for patients with mesial temporal lobe epilepsy. This study aimed to describe mesial temporal lobe ablation volumes and seizure outcomes following LiTT across the Mayo Clinic’s 3 epilepsy surgery centers. METHODS This was a multi-site, single-institution, retrospective review of seizure outcomes and ablation volumes following LiTT for medically intractable mesial temporal lobe epilepsy between October 2011 and October 2015. Pre-ablation and post-ablation follow-up volumes of the hippocampus were measured using FreeSurfer, and the volume of ablated tissue was also measured on intraoperative MRI using a supervised spline-based edge detection algorithm. To determine seizure outcomes, results were compared between those patients who were seizure free and those who continued to experience seizures. RESULTS There were 23 patients who underwent mesial temporal LiTT within the study period. Fifteen patients (65%) had left-sided procedures. The median follow-up was 34 months (range 12–70 months). The mean ablation volume was 6888 mm 3 . Median hippocampal ablation was 65%, with a median amygdala ablation of 43%. At last follow-up, 11 (48%) of these patients were seizure free. There was no correlation between ablation volume and seizure freedom (p = 0.69). There was also no correlation between percent ablation of the amygdala (p = 0.28) or hippocampus (p = 0.82) and seizure outcomes. Twelve patients underwent formal testing with computational visual fields. Visual field changes were seen in 67% of patients who underwent testing. Comparing the 5 patients with clinically noticeable visual field deficits to the rest of the cohort showed no significant difference in ablation volume between those patients with visual field deficits and those without (p = 0.94). There were 11 patients with follow-up neuropsychological testing. Within this group, verbal learning retention was 76% in the patients with left-sided procedures and 89% in those with right-sided procedures. CONCLUSIONS In this study, there was no significant correlation between the ablation volume after LiTT and seizure outcomes. Visual field deficits were common in formally tested patients, much as in patients treated with open temporal lobectomy. Further studies are required to determine the role of amygdalohippocampal ablation.

Abstract Objective . An ability to map seizure-generating brain tissue, i.e. the seizure onset zone (SOZ), without recording actual seizures could reduce the duration of invasive EEG monitoring for patients with drug-resistant epilepsy. A widely-adopted practice in the literature is to compare the incidence (events/time) of putative pathological electrophysiological biomarkers associated with epileptic brain tissue with the SOZ determined from spontaneous seizures recorded with intracranial EEG, primarily using a single biomarker. Clinical translation of the previous efforts suffers from their inability to generalize across multiple patients because of (a) the inter-patient variability and (b) the temporal variability in the epileptogenic activity. Approach . Here, we report an artificial intelligence-based approach for combining multiple interictal electrophysiological biomarkers and their temporal characteristics as a way of accounting for the above barriers and show that it can reliably identify seizure onset zones in a study cohort of 82 patients who underwent evaluation for drug-resistant epilepsy. Main results . Our investigation provides evidence that utilizing the complementary information provided by multiple electrophysiological biomarkers and their temporal characteristics can significantly improve the localization potential compared to previously published single-biomarker incidence-based approaches, resulting in an average area under ROC curve (AUC) value of 0.73 in a cohort of 82 patients. Our results also suggest that recording durations between 90 min and 2 h are sufficient to localize SOZs with accuracies that may prove clinically relevant. Significance . The successful validation of our approach on a large cohort of 82 patients warrants future investigation on the feasibility of utilizing intra-operative EEG monitoring and artificial intelligence to localize epileptogenic brain tissue. Broadly, our study demonstrates the use of artificial intelligence coupled with careful feature engineering in augmenting clinical decision making.