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Loss of consciousness is a hallmark of many epileptic seizures and carries risks of serious injury and sudden death. While cortical sleep-like activities accompany loss of consciousness during focal impaired awareness seizures, the mechanisms of loss of consciousness during focal to bilateral tonic-clonic seizures remain unclear. Quantifying differences in markers of cortical activation and ictal recruitment between focal impaired awareness and focal to bilateral tonic-clonic seizures may also help us to understand their different consequences for clinical outcomes and to optimize neuromodulation therapies. We quantified clinical signs of loss of consciousness and intracranial EEG activity during 129 focal impaired awareness and 50 focal to bilateral tonic-clonic from 41 patients. We characterized intracranial EEG changes both in the seizure onset zone and in areas remote from the seizure onset zone with a total of 3386 electrodes distributed across brain areas. First, we compared the dynamics of intracranial EEG sleep-like activities: slow-wave activity (1–4 Hz) and beta/delta ratio (a validated marker of cortical activation) during focal impaired awareness versus focal to bilateral tonic-clonic. Second, we quantified differences between focal to bilateral tonic-clonic and focal impaired awareness for a marker validated to detect ictal cross-frequency coupling: phase-locked high gamma (high-gamma phased-locked to low frequencies) and a marker of ictal recruitment: the epileptogenicity index. Third, we assessed changes in intracranial EEG activity preceding and accompanying behavioural generalization onset and their correlation with electromyogram channels. In addition, we analysed human cortical multi-unit activity recorded with Utah arrays during three focal to bilateral tonic-clonic seizures. Compared to focal impaired awareness, focal to bilateral tonic-clonic seizures were characterized by deeper loss of consciousness, even before generalization occurred. Unlike during focal impaired awareness, early loss of consciousness before generalization was accompanied by paradoxical decreases in slow-wave activity and by increases in high-gamma activity in parieto-occipital and temporal cortex. After generalization, when all patients displayed loss of consciousness, stronger increases in slow-wave activity were observed in parieto-occipital cortex, while more widespread increases in cortical activation (beta/delta ratio), ictal cross-frequency coupling (phase-locked high gamma) and ictal recruitment (epileptogenicity index). Behavioural generalization coincided with a whole-brain increase in high-gamma activity, which was especially synchronous in deep sources and could not be explained by EMG. Similarly, multi-unit activity analysis of focal to bilateral tonic-clonic revealed sustained increases in cortical firing rates during and after generalization onset in areas remote from the seizure onset zone. Overall, these results indicate that unlike during focal impaired awareness, the neural signatures of loss of consciousness during focal to bilateral tonic-clonic consist of paradoxical increases in cortical activation and neuronal firing found most consistently in posterior brain regions. These findings suggest differences in the mechanisms of ictal loss of consciousness between focal impaired awareness and focal to bilateral tonic-clonic and may account for the more negative prognostic consequences of focal to bilateral tonic-clonic.

Direct cortical stimulation has been applied in epilepsy for nearly one century and has experienced a renaissance given unprecedented opportunities to probe, excite and inhibit the human brain. Evidence suggests stimulation can increase diagnostic and therapeutic utility in patients with drug-resistant epilepsies. However, choosing appropriate stimulation parameters is not a trivial issue, which is further complicated by the fact that epilepsy is characterized by complex brain state dynamics. In this article derived from discussions at the ICTALS 2022 conference, we succinctly review the literature on cortical stimulation applied acutely and chronically to the epileptic brain for localization, monitoring, and therapeutic purposes. In particular, we discuss how stimulation is used to probe brain excitability, discuss evidence on usefulness of stimulation to trigger and stop seizures, review therapeutic applications of stimulation, and finally discuss how stimulation parameters are impacted by brain dynamics. Although research has advanced considerably over the past decade, there are still significant hurdles to optimize use of this technique. For example, it remains unclear to what extent short timescale diagnostic biomarkers can predict long-term outcomes and to what extent these biomarkers add information to already existing biomarkers from passive EEG recordings. Further questions include the extent to which closed loop stimulation offers advantages over open loop stimulation, what the optimal closed loop timescales may be, and whether biomarker-informed stimulation can lead to seizure freedom. The ultimate goal of bioelectronic medicine remains not just to stop seizures but rather to cure epilepsy and its comorbidities.

Deep brain stimulation and responsive neurostimulation (RNS) use high-frequency stimulation (HFS) per the pivotal trials and manufacturer-recommended therapy protocols. However, not all patients respond to HFS. In this retrospective case series, 10 patients implanted with the RNS System were programmed with low-frequency stimulation (LFS) to treat their seizures; nine of these patients were previously treated with HFS (100 Hz or greater). LFS was defined as frequency 10 Hz. Burst duration was increased to at least 1000 ms. With HFS, patients had a median seizure reduction (MSR) of 13% (interquartile range [IQR] = -67 to 54) after a median of 19 months (IQR = 8-49). In contrast, LFS was associated with a 67% MSR (IQR = 13-95) when compared to HFS and 76% MSR (IQR = 43-91) when compared to baseline prior to implantation. Charge delivered per hour and pulses per day were not significantly different between HFS and LFS, although time stimulated per day was longer for LFS (228 min) than for HFS (7 min). There were no LFS-specific adverse effects reported by any of the patients. LFS could represent an alternative, effective method for delivering stimulation in focal drug-resistant epilepsy patients treated with the RNS System.

Objective Seizure unpredictability is rated as one of the most challenging aspects of living with epilepsy. Seizure likelihood can be influenced by a range of environmental and physiological factors that are difficult to measure and quantify. However, some generalizable patterns have been demonstrated in seizure onset. A majority of people with epilepsy exhibit circadian rhythms in their seizure times, and many also show slower, multiday patterns. Seizure cycles can be measured using a range of recording modalities, including self-reported electronic seizure diaries. This study aimed to develop personalized forecasts from a mobile seizure diary app. Methods Forecasts based on circadian and multiday seizure cycles were tested pseudoprospectively using data from 50 app users (mean of 109 seizures per subject). Individuals' strongest cycles were estimated from their reported seizure times and used to derive the likelihood of future seizures. The forecasting approach was validated using self-reported events and electrographic seizures from the Neurovista dataset, an existing database of long-term electroencephalography that has been widely used to develop forecasting algorithms. Results The validation dataset showed that forecasts of seizure likelihood based on self-reported cycles were predictive of electrographic seizures for approximately half the cohort. Forecasts using only mobile app diaries allowed users to spend an average of 67.1% of their time in a low-risk state, with 14.8% of their time in a high-risk warning state. On average, 69.1% of seizures occurred during high-risk states and 10.5% of seizures occurred in low-risk states. Significance Seizure diary apps can provide personalized forecasts of seizure likelihood that are accurate and clinically relevant for electrographic seizures. These results have immediate potential for translation to a prospective seizure forecasting trial using a mobile diary app. It is our hope that seizure forecasting apps will one day give people with epilepsy greater confidence in managing their daily activities.

BACKGROUND: Drug resistant epilepsy (DRE) patients not amenable to epilepsy surgery can benefit from neurostimulation. Few data compare different neuromodulation strategies. OBJECTIVE: Compare five invasive neuromodulation strategies for treatment of DRE: anterior thalamic nuclei deep brain stimulation (ANT-DBS), centromedian thalamic nuclei DBS (CM-DBS), responsive neurostimulation (RNS), chronic subthreshold stimulation (CSS), and vagus nerve stimulation (VNS). METHODS: Single center retrospective review and phone survey for patients implanted with invasive neuromodulation for 2004–2021. RESULTS: N=159 (ANT-DBS=38, CM-DBS=19, RNS=30, CSS=32, VNS=40). Total median seizure reduction (MSR) was 61% for the entire cohort (IQR 5–90) and in descending order: CSS (85%), CM-DBS (63%), ANT-DBS (52%), RNS (50%), and VNS (50%); p=0.07. Responder rate was 60% after median follow-up time of 26 months. Seizure severity, life satisfaction and quality of sleep were improved. Cortical stimulation (RNS and CSS) was associated with improved seizure reduction compared to subcortical stimulation (ANT-DBS, CM-DBS, and VNS) (67% vs. 52%). Effectiveness was similar for focal epilepsy vs generalized epilepsy, closed loop vs open loop stimulation, pediatric vs. adult cases, and high frequency (>100 Hz) vs low frequency (

Highlights • Electrophysiology of amputated hand preserved in contralateral sensorimotor cortex. • Typical focal increase of HFB power and distributed decrease in LFB. • Finger representations are intact and decodable at high (>90%) accuracy. • Optimal decoding 1–3 s after onset, with at least 13–13 mm cortical coverage. • Electrophysiology remain intact after long term amputation and can be used for BCIs., Electrophysiological signals in the human motor system may change in different ways after deafferentation, with some studies emphasizing reorganization while others propose retained physiology. Understanding whether motor electrophysiology is retained over longer periods of time can be invaluable for patients with paralysis (e.g. ALS or brainstem stroke) when signals from sensorimotor areas may be used for communication or control over neural prosthetic devices. In addition, a maintained electrophysiology can potentially benefit the treatment of phantom limb pains through prolonged use of these signals in a brain-machine interface (BCI). Here, we were presented with the unique opportunity to investigate the physiology of the sensorimotor cortex in a patient with an amputated arm using electrocorticographic (ECoG) measurements. While implanted with an ECoG grid for clinical evaluation of electrical stimulation for phantom limb pain, the patient performed attempted finger movements with the contralateral (lost) hand and executed finger movements with the ipsilateral (healthy) hand. The electrophysiology of the sensorimotor cortex contralateral to the amputated hand remained very similar to that of hand movement in healthy people, with a spatially focused increase of high-frequency band (65–175 Hz; HFB) power over the hand region and a distributed decrease in low-frequency band (15–28 Hz; LFB) power. The representation of the three different fingers (thumb, index and little) remained intact and HFB patterns could be decoded using support vector learning at single-trial classification accuracies of >90%, based on the first 1–3 s of the HFB response. These results indicate that hand representations are largely retained in the motor cortex. The intact physiological response of the amputated hand, the high distinguishability of the fingers and fast temporal peak are encouraging for neural prosthetic devices that target the sensorimotor cortex.

Copyright © 2021 Brinkmann, Karoly, Nurse, Dumanis, Nasseri, Viana, Schulze-Bonhage, Freestone, Worrell, Richardson and Cook. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms., It is a major challenge in clinical epilepsy to diagnose and treat a disease characterized by infrequent seizures based on patient or caregiver reports and limited duration clinical testing. The poor reliability of self-reported seizure diaries for many people with epilepsy is well-established, but these records remain necessary in clinical care and therapeutic studies. A number of wearable devices have emerged, which may be capable of detecting seizures, recording seizure data, and alerting caregivers. Developments in non-invasive wearable sensors to measure accelerometry, photoplethysmography (PPG), electrodermal activity (EDA), electromyography (EMG), and other signals outside of the traditional clinical environment may be able to identify seizure-related changes. Non-invasive scalp electroencephalography (EEG) and minimally invasive subscalp EEG may allow direct measurement of seizure activity. However, significant network and computational infrastructure is needed for continuous, secure transmission of data. The large volume of data acquired by these devices necessitates computer-assisted review and detection to reduce the burden on human reviewers. Furthermore, user acceptability of such devices must be a paramount consideration to ensure adherence with long-term device use. Such devices can identify tonic-clonic seizures, but identification of other seizure semiologies with non-EEG wearables is an ongoing challenge. Identification of electrographic seizures with subscalp EEG systems has recently been demonstrated over long (>6 month) durations, and this shows promise for accurate, objective seizure records. While the ability to detect and forecast seizures from ambulatory intracranial EEG is established, invasive devices may not be acceptable for many individuals with epilepsy. Recent studies show promising results for probabilistic forecasts of seizure risk from long-term wearable devices and electronic diaries of self-reported seizures. There may also be predictive value in individuals' symptoms, mood, and cognitive performance. However, seizure forecasting requires perpetual use of a device for monitoring, increasing the importance of the system's acceptability to users. Furthermore, long-term studies with concurrent EEG confirmation are lacking currently. This review describes the current evidence and challenges in the use of minimally and non-invasive devices for long-term epilepsy monitoring, the essential components in remote monitoring systems, and explores the feasibility to detect and forecast impending seizures via long-term use of these systems.

Objective This study investigates high‐frequency oscillations (HFOs; 65–600 Hz) as a biomarker of epileptogenic brain and explores three barriers to their clinical translation: (1) Distinguishing pathological HFOs (pathHFO) from physiological HFOs (physHFO). (2) Classifying tissue under individual electrodes as epileptogenic (3) Reproducing results across laboratories. Methods We recorded HFOs using intracranial EEG (iEEG) in 90 patients with focal epilepsy and 11 patients without epilepsy. In nine patients with epilepsy putative physHFOs were induced by cognitive or motor tasks. HFOs 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 physHFO, task induced physHFO, and pathHFO, but the amplitudes of the pathHFO were higher (P

Objectives To develop quantitative measures for estimating seizure probability, we examine intracranial EEG data from patient groups with three qualitative seizure probabilities: patients with drug resistant focal epilepsy (high), these patients during cortical stimulation (intermediate), and patients who have no history of seizures (low). Methods Patients with focal epilepsy were implanted with subdural electrodes during presurgical evaluation. Patients without seizures were implanted during treatment with motor cortex stimulation for atypical facial pain. Results The rate and amplitude of spikes correlate with qualitative seizure probability across patient groups and with proximity to the seizure onset zone in focal epilepsy patients. Spikes occur earlier during the negative oscillation of underlying slow activity (0.5–2 Hz) when seizure probability is increased. Similarly, coupling between slow and fast activity is increased. Conclusions There is likely a continuum of sharply contoured activity between non-epileptiform and epileptiform. Characteristics of spiking and how spikes relate to slow activity can be combined to predict seizure onset zones. Significance Intracranial EEG data from patients without seizures represent a unique comparison group and highlight changes seen in spiking and slow wave activity with increased seizure probability. Slow wave activity and related physiology are an important potential biomarker for estimating seizure probability.

Epilepsy is a heterogeneous condition with disparate etiologies and phenotypic and genotypic characteristics. Clinical and research aspects are accordingly varied, ranging from epidemiological to molecular, spanning clinical trials and outcomes, gene and drug discovery, imaging, electroencephalography, pathology, epilepsy surgery, digital technologies, and numerous others. Epilepsy data are collected in the terabytes and petabytes, pushing the limits of current capabilities. Modern computing firepower and advances in machine and deep learning, pioneered in other diseases, open up exciting possibilities for epilepsy too. However, without carefully designed approaches to acquiring, standardizing, curating, and making available such data, there is a risk of failure. Thus, careful construction of relevant ontologies, with intimate stakeholder inputs, provides the requisite scaffolding for more ambitious big data undertakings, such as an epilepsy data commons. In this review, we assess the clinical and research epilepsy landscapes in the big data arena, current challenges, and future directions, and make the case for a systematic approach to epilepsy big data.

Objective When considering all patients with focal drug-resistant epilepsy, as high as 40–50% of patients suffer seizure recurrence after surgery. To achieve seizure freedom without side effects, accurate localization of the epileptogenic tissue is crucial before its resection. We investigate an automated, fast, objective mapping process that uses only interictal data. Methods We propose a novel approach based on multiple iEEG features, which are used to train a support vector machine (SVM) model for classification of iEEG electrodes as normal or pathologic using 30 min of inter-ictal recording. Results The tissue under the iEEG electrodes, classified as epileptogenic, was removed in 17/18 excellent outcome patients and was not entirely resected in 8/10 poor outcome patients. The overall best result was achieved in a subset of 9 excellent outcome patients with the area under the receiver operating curve = 0.95. Conclusion SVM models combining multiple iEEG features show better performance than algorithms using a single iEEG marker. Multiple iEEG and connectivity features in presurgical evaluation could improve epileptogenic tissue localization, which may improve surgical outcome and minimize risk of side effects. Significance In this study, promising results were achieved in localization of epileptogenic regions by SVM models that combine multiple features from 30 min of inter-ictal iEEG recordings.

Long-term stability of the electrode-tissue interface may be required to maintain optimal neural recording with subdural and deep brain implants and to permit appropriate delivery of neuromodulation therapy. Although short-term changes in impedance at the electrode-tissue interface are known to occur, long-term changes in impedance have not previously been examined in detail in humans. To provide further information about short- and long-term impedance changes in chronically implanted electrodes, a dataset from 191 persons with medically intractable epilepsy participating in a trial of an investigational responsive neurostimulation device (the RNS(®) System, NeuroPace, Inc.) was reviewed. Monopolar impedance measurements were available for 391 depth and subdural leads containing a total of 1564 electrodes; measurements were available for median 802 days post-implant (range 28-1634). Although there were statistically significant short-term impedance changes, long-term impedance was stable after one year. Impedances for depth electrodes transiently increased during the third week after lead implantation and impedances for subdural electrodes increased over 12 weeks post-implant, then were stable over the subsequent long-term follow-up. Both depth and subdural electrode impedances demonstrated long-term stability, suggesting that the quality of long-term electrographic recordings (the data used to control responsive brain stimulation) can be maintained over time.

The goals of Epilepsy Benchmark Area III involve identifying areas that are ripe for progress in terms of controlling seizures and patient symptoms in light of the most recent advances in both basic and clinical research. These goals were developed with an emphasis on potential new therapeutic strategies that will reduce seizure burden and improve quality of life for patients with epilepsy. In particular, we continue to support the proposition that a better understanding of how seizures are initiated, propagated, and terminated in different forms of epilepsy is central to enabling new approaches to treatment, including pharmacological as well as surgical and device-oriented approaches. The stubbornly high rate of treatment-resistant epilepsy-one-third of patients-emphasizes the urgent need for new therapeutic strategies, including pharmacological, procedural, device linked, and genetic. The development of new approaches can be advanced by better animal models of seizure initiation that represent salient features of human epilepsy, as well as humanized models such as induced pluripotent stem cells and organoids. The rapid advances in genetic understanding of a subset of epilepsies provide a path to new and direct patient-relevant cellular and animal models, which could catalyze conceptualization of new treatments that may be broadly applicable across multiple forms of epilepsies beyond those arising from variation in a single gene. Remarkable advances in machine learning algorithms and miniaturization of devices and increases in computational power together provide an enhanced opportunity to detect and mitigate seizures in real time via devices that interrupt electrical activity directly or administer effective pharmaceuticals. Each of these potential areas for advance will be discussed in turn.

Focal seizures appear to start abruptly and unpredictably when recorded from volumes of brain probed by clinical intracranial electroencephalograms. To investigate the spatiotemporal scale of focal epilepsy, wide-bandwidth electrophysiological recordings were obtained using clinical macro- and research microelectrodes in patients with epilepsy and control subjects with intractable facial pain. Seizure-like events not detectable on clinical macroelectrodes were observed on isolated microelectrodes. These ‘microseizures’ were sparsely distributed, more frequent in brain regions that generated seizures, and sporadically evolved into large-scale clinical seizures. Rare microseizures observed in control patients suggest that this phenomenon is ubiquitous, but their density distinguishes normal from epileptic brain. Epileptogenesis may involve the creation of these topographically fractured microdomains and ictogenesis (seizure generation), the dynamics of their interaction and spread.

Although subtraction ictal SPECT coregistered to MRI (SISCOM) is clinically useful in epilepsy surgery evaluation, it does not determine whether the ictal-interictal subtraction difference is statistically different from the expected random variation between 2 SPECT studies. We developed a statistical parametric mapping and MRI voxel-based method of analyzing ictal-interictal SPECT difference data (statistical ictal SPECT coregistered to MRI [STATISCOM]) and compared it with SISCOM.Two serial SPECT studies were performed in 11 healthy volunteers without epilepsy (control subjects) to measure random variation between serial studies from individuals. STATISCOM and SISCOM images from 87 consecutive patients who had ictal SPECT studies and subsequent temporal lobectomy were assessed by reviewers blinded to clinical data and outcome.Interobserver agreement between blinded reviewers was higher for STATISCOM images than for SISCOM images (kappa = 0.81 vs kappa = 0.36). STATISCOM identified a hyperperfusion focus in 84% of patients, SISCOM in 66% (p0.05). STATISCOM correctly localized the temporal lobe epilepsy (TLE) subtypes (mesial vs lateral neocortical) in 68% of patients compared with 24% by SISCOM (p = 0.02); subgroup analysis of patients without lesions (as determined by MRI) showed superiority of STATISCOM (80% vs 47%; p = 0.04). Moreover, the probability of seizure-free outcome was higher when STATISCOM correctly localized the TLE subtype than when it was indeterminate (81% vs 53%; p = 0.03).Statistical ictal SPECT coregistered to MRI (STATISCOM) was superior to subtraction ictal SPECT coregistered to MRI for seizure localization before temporal lobe epilepsy (TLE) surgery. STATISCOM localization to the correct TLE subtype was prognostically important for postsurgical seizure freedom.

Neuronal oscillations span a wide range of spatial and temporal scales that extend beyond traditional clinical EEG. Recent research suggests that high-frequency oscillations (HFO), in the ripple (80-250 Hz) and fast ripple (250-1000 Hz) frequency range, may be signatures of epileptogenic brain and involved in the generation of seizures. However, most research investigating HFO in humans comes from microwire recordings, whose relationship to standard clinical intracranial EEG (iEEG) has not been explored. In this study iEEG recordings (DC - 9000 Hz) were obtained from human medial temporal lobe using custom depth electrodes containing both microwires and clinical macroelectrodes. Ripple and fast-ripple HFO recorded from both microwires and clinical macroelectrodes were increased in seizure generating brain regions compared to control regions. The distribution of HFO frequencies recorded from the macroelectrodes was concentrated in the ripple frequency range, compared to a broad distribution of HFO frequencies recorded from microwires. The average frequency of ripple HFO recorded from macroelectrodes was lower than that recorded from microwires (143.3 +/- 49.3 Hz versus 116.3 +/- 38.4, Wilcoxon rank sum P

Objective To develop a prospective method for optimizing seizure prediction, given an array of implanted electrodes and a set of candidate quantitative features computed at each contact location. Methods The method employs a genetic-based selection process, and then tunes a probabilistic neural network classifier to predict seizures within a 10 min prediction horizon. Initial seizure and interictal data were used for training, and the remaining IEEG data were used for testing. The method continues to train and learn over time. Results Validation of these results over two workshop patients demonstrated a sensitivity of 100%, and 1.1 false positives per hour for Patient E, using a 2.4 s block predictor, and a failure of the method on Patient B. Conclusions This study demonstrates a prospective, exploratory implementation of a seizure prediction method designed to adapt to individual patients with a wide variety of pre-ictal patterns, implanted electrodes and seizure types. Its current performance is limited likely by the small number of input channels and quantitative features employed in this study, and segmentation of the data set into training and testing sets rather than using all continuous data available. Significance This technique theoretically has the potential to address the challenge presented by the heterogeneity of EEG patterns seen in medication-resistant epilepsy. A more comprehensive implementation utilizing all electrode sites, a broader feature library, and automated multi-feature fusion will be required to fully judge the method's potential for predicting seizures.

Neocortical seizures are often poorly localized, explosive and widespread at onset, making them poorly amenable to epilepsy surgery in the absence of associated focal brain lesions. We describe, for the first time in an unselected group of patients with neocortical epilepsy, the finding that high-frequency (60-100 Hz) epileptiform oscillations are highly localized in the seizure onset zone, both before and temporally removed from seizure onset. These findings were observed in all six patients with neocortical epilepsy out of 23 consecutive patients implanted with intracranial electrodes for pre-surgical evaluation during the study period. The majority of seizures (62%) in these patients were anticipated by an increase in high-frequency activity in the 20 min prior to neocortical seizure onset. Contrary to observations in normal brain, high-frequency activity was strongly modulated by behavioural state, and was maximal during slow-wave sleep, which may explain the propensity for neocortical onset seizures to begin during sleep. These findings point to an important role for neuromodulatory circuits, probably involving the thalamus, in mechanisms underlying seizure generation in neocortical epilepsy. These findings demonstrate that high-frequency epileptiform oscillations may prove clinically useful in localizing the seizure onset zone in neocortical epilepsy, for identifying periods of increased probability of seizure onset, and in elucidating mechanisms underlying neocortical ictogenesis. Confirmation that prolonged bursts of high-frequency activity may predict focal onset neocortical seizures will require prospective validation on continuous, prolonged recordings in a larger number of patients. Importantly, the results show that the dynamic range utilized in current clinical practice for localization of epileptogenic brain largely ignores fundamental oscillations that are signatures of an epileptogenic brain. It may prove that many currently available clinical EEG systems and EEG analysis methods utilize a dynamic range that discards clinically important information.

Objective Recent studies indicate that pathologic high-frequency oscillations (HFOs) are signatures of epileptogenic brain. Automated tools are required to characterize these events. We present a new algorithm tuned to detect HFOs from 30 to 85Hz, and validate it against human expert electroencephalographers. Methods We randomly selected 28 3-min single-channel epochs of intracranial EEG (IEEG) from two patients. Three human reviewers and three automated detectors marked all records to identify candidate HFOs. Subsequently, human reviewers verified all markings. Results A total of 1330 events were collectively identified. The new method presented here achieved 89.7% accuracy against a consensus set of human expert markings. A one-way ANOVA determined no difference between the mean F -measures of the human reviewers and automated algorithm. Human κ statistics (mean κ =0.38) demonstrated marginal identification consistency, primarily due to false negative errors. Conclusions We present an HFO detector that improves upon existing algorithms, and performs as well as human experts on our test data set. Validation of detector performance must be compared to more than one expert because of interrater variability. Significance This algorithm will be useful for analyzing large EEG databases to determine the pathophysiological significance of HFO events in human epileptic networks.

Increases in accumulated energy on intracranial EEG are associated with oncoming seizures in retrospective studies, supporting the idea that seizures are generated over time. Published seizure prediction methods require comparison to 'baseline' data, sleep staging, and selecting seizures that are not clustered closely in time. In this study, we attempt to remove these constraints by using a continuously adapting energy threshold, and to identify stereotyped energy variations through the seizure cycle (inter-, pre-, post- and ictal periods).Accumulated energy was approximated by using moving averages of signal energy, computed for window lengths of 1 and 20 min, and an adaptive decision threshold. Predictions occurred when energy within the shorter running window exceeded the decision threshold.Predictions for time horizons of less than 3h did not achieve statistical significance in the data sets analyzed that had an average inter-seizure interval ranging from 2.9 to 8.6h. 51.6% of seizures across all patients exhibited stereotyped pre-ictal energy bursting and quiet periods.Accumulating energy alone is not sufficient for predicting seizures using a 20 min running baseline for comparison. Stereotyped energy patterns through the seizure cycle may provide clues to mechanisms underlying seizure generation.Energy-based seizure prediction will require fusion of multiple complimentary features and perhaps longer running averages to compensate for post-ictal and sleep-induced energy changes.

Ovarian hyperstimulation syndrome (OHSS) is a rare complication in patients undergoing ovarian induction therapy.1 The clinical symptoms include nausea, vomiting, abdominal and pleural effusions, and ovarian enlargement. Hemoconcentration, owing to the large fluid shifts from the intravascular space to the peritoneal cavity, results in increased blood viscosity and sometimes a coagulopathy that can lead to arterial and venous occlusions. Thromboembolic stroke,1-3⇓⇓ cerebral venous thrombosis,4 and systemic arteriovenous thrombosis5 have been reported in OHSS. We describe a young woman who developed OHSS after in vitro fertilization and subsequently had an embolic stroke from an intracardiac thrombus. A 34-year-old right-handed woman underwent in vitro fertilization–embryo transfer treatment. The standard stimulation protocol included pituitary downregulation with injections of the gonadotropin-releasing hormone agonist leuprolide, followed by recombinant follicle-stimulating hormone. Peak serum estradiol level was 2,852 pg/mL, at which time human chorionic gonadotropin was given intramuscularly. Oocyte retrieval was followed by daily intramuscular progesterone administration and oocyte fertilization. Two embryos were transferred 2 days after oocyte retrieval. Seven days later, she was hospitalized with symptoms …