iEEG Recordings Research Articles

Neural repetition suppression effects in the human hippocampus

Neurons in the temporal lobe cortex exhibit reduced responses when a stimulus or a stimulus feature is repeated. This phenomenon, termed “repetition suppression”, is the basis for many functional imaging studies that have used Blood Oxygenation Level Dependent (BOLD) activity differences between novel and repeated items as an index of neural selectivity in hippocampal subfields. However, it is not clear how hippocampal neural activity changes across repeated exposure to a stimulus. Here, we used direct intracranial electroencephalography (iEEG) recordings of hippocampal activity to examine whether neural activity in the human hippocampus is modulated across successive repetitions of an item. Time-frequency analyses revealed that high-frequency activity, which is thought to include gamma oscillations and possible correlates of multi-unit activity, declined monotonically across successive presentations of an item. In contrast, low-frequency oscillations in the alpha and beta bands monotonically increased across successive presentations of an object. These results provide support for the assumption that, at least under some circumstances, repetition suppression (as measured by declines in high-frequency activity) can be observed in the hippocampus, and these effects are accompanied by increases in low-frequency oscillations as well.

Two types of ictal phase-amplitude couplings in epilepsy patients revealed by spectral harmonicity of intracerebral EEG recordings

ObjectiveSpectral harmonicity of the ictal activity was analyzed regarding two clinically relevant aspects, (1) as a confounding factor producing ‘spurious’ phase-amplitude couplings (PAC) which may lead to wrong conclusions about the underlying ictal mechanisms, and (2) its role in how good PAC is in correspondence to the seizure onset zone (SOZ) classification performed by the epileptologists. MethodsPAC patterns observed in intracerebral electroencephalography (iEEG) recordings were retrospectively studied during seizures of seven patients with pharmacoresistant focal epilepsy. The time locked index (TLI) measure was introduced to quantify the degree of harmonicity between frequency bands associated to the emergence of PAC during epileptic seizures. Results(1) Harmonic and non harmonic PAC patterns coexist during the seizure dynamics in iEEG recordings with macroelectrodes. (2) Harmonic PAC patterns are an emergent property of the periodic non sinusoidal waveform constituting the epileptiform activity. (3) The TLI metric allows to distinguish the non harmonic PAC pattern, which has been previously associated with the ictal core through the paroxysmal depolarizing shifts mechanism of seizure propagation. ConclusionsOur results suggest that the spectral harmonicity of the ictal activity plays a relevant role in the visual analysis of the iEEG recordings performed by the epileptologists to define the SOZ, and that it should be considered for the proper interpretation of ictal mechanisms. SignificanceThe proposed harmonicity analysis can be used to improve the delineation of the SOZ by reliably identifying non harmonic PAC patterns emerging from fully recruited cortical and subcortical areas.

Seizure pathways change on circadian and slower timescales in individual patients with focal epilepsy

Personalized medicine requires that treatments adapt to not only the patient but also changing factors within each individual. Although epilepsy is a dynamic disorder characterized by pathological fluctuations in brain state, surprisingly little is known about whether and how seizures vary in the same patient. We quantitatively compared within-patient seizure network evolutions using intracranial electroencephalographic (iEEG) recordings of over 500 seizures from 31 patients with focal epilepsy (mean 16.5 seizures per patient). In all patients, we found variability in seizure paths through the space of possible network dynamics. Seizures with similar pathways tended to occur closer together in time, and a simple model suggested that seizure pathways change on circadian and/or slower timescales in the majority of patients. These temporal relationships occurred independent of whether the patient underwent antiepileptic medication reduction. Our results suggest that various modulatory processes, operating at different timescales, shape within-patient seizure evolutions, leading to variable seizure pathways that may require tailored treatment approaches.

Critical slowing down as a biomarker for seizure susceptibility

The human brain has the capacity to rapidly change state, and in epilepsy these state changes can be catastrophic, resulting in loss of consciousness, injury and even death. Theoretical interpretations considering the brain as a dynamical system suggest that prior to a seizure, recorded brain signals may exhibit critical slowing down, a warning signal preceding many critical transitions in dynamical systems. Using long-term intracranial electroencephalography (iEEG) recordings from fourteen patients with focal epilepsy, we monitored key signatures of critical slowing down prior to seizures. The metrics used to detect critical slowing down fluctuated over temporally long scales (hours to days), longer than would be detectable in standard clinical evaluation settings. Seizure risk was associated with a combination of these signals together with epileptiform discharges. These results provide strong validation of theoretical models and demonstrate that critical slowing down is a reliable indicator that could be used in seizure forecasting algorithms.

Cingulate gyrus epilepsy: semiology, invasive EEG, and surgical approaches.

The semiology of cingulate gyrus epilepsy is varied and may involve the paracentral area, the adjacent limbic system, and/or the orbitofrontal gyrus. Invasive electroencephalography (iEEG) recording is usually required for patients with deeply located epileptogenic foci. This paper reports on the authors' experiences in the diagnosis and surgical treatment of patients with focal epilepsy originating in the cingulate gyrus. Eighteen patients (median age 24 years, range 5-53 years) with a mean seizure history of 23 years (range 2-32 years) were analyzed retrospectively. The results of presurgical evaluation, surgical strategy, and postoperative pathology are reported, as well as follow-up concerning functional morbidity and seizures (median follow-up 7 years, range 2-12 years). Patients with cingulate gyrus epilepsy presented with a variety of semiologies and scalp EEG patterns. Prior to ictal onset, 11 (61%) of the patients presented with aura. Initial ictal symptoms included limb posturing in 12 (67%), vocalization in 5, and hypermotor movement in 4. In most patients (n = 16, 89%), ictal EEG presented as widespread patterns with bilateral hemispheric origin, as well as muscle artifacts obscuring the onset of EEG during the ictal period in 11 patients. Among the 18 patients who underwent resection, the pathology revealed mild malformation of cortical development in 2, focal cortical dysplasia (FCD) Ib in 4, FCD IIa in 4, FCD IIb in 4, astrocytoma in 1, ganglioglioma in 1, and gliosis in 2. The seizure outcome after surgery was satisfactory: Engel class IA in 12 patients, IIB in 3, IIIA in 1, IIIB in 1, and IVB in 1 at the 2-year follow-up. In this study, the authors exploited the improved access to the cingulate epileptogenic network made possible by the use of 3D electrodes implanted using stereoelectroencephalography methodology. Under iEEG recording and intraoperative neuromonitoring, epilepsy surgery on lesions in the cingulate gyrus can result in good outcomes in terms of seizure recurrence and the incidence of postoperative permanent deficits.

Interictal Epileptiform Discharges and the Quality of Human Intracranial Neurophysiology Data.

Intracranial electroencephalography (IEEG) involves recording from electrodes placed directly onto the cortical surface or deep brain locations. It is performed on patients with medically refractory epilepsy, undergoing pre-surgical seizure localization. IEEG recordings, combined with advancements in computational capacity and analysis tools, have accelerated cognitive neuroscience. This Perspective describes a potential pitfall latent in many of these recordings by virtue of the subject population—namely interictal epileptiform discharges (IEDs), which can cause spurious results due to the contamination of normal neurophysiological signals by pathological waveforms related to epilepsy. We first discuss the nature of IED hazards, and why they deserve the attention of neurophysiology researchers. We then describe four general strategies used when handling IEDs (manual identification, automated identification, manual-automated hybrids, and ignoring by leaving them in the data), and discuss their pros, cons, and contextual factors. Finally, we describe current practices of human neurophysiology researchers worldwide based on a cross-sectional literature review and a voluntary survey. We put these results in the context of the listed strategies and make suggestions on improving awareness and clarity of reporting to enrich both data quality and communication in the field.

Task-dependent effects of intracranial hippocampal stimulation on human memory and hippocampal theta power

BackgroundDespite its potential to revolutionize the treatment of memory dysfunction, the efficacy of direct electrical hippocampal stimulation for memory performance has not yet been well characterized. One of the main challenges to cross-study comparison in this area of research is the diversity of the cognitive tasks used to measure memory performance. ObjectiveWe hypothesized that the tasks that differentially engage the hippocampus may be differentially influenced by hippocampal stimulation and the behavioral effects would be related to the underlying hippocampal activity. MethodsTo investigate this issue, we recorded intracranial EEG from and directly applied stimulation to the hippocampus of 10 epilepsy patients while they performed two different verbal memory tasks – a word pair associative memory task and a single item memory task. ResultsHippocampal stimulation modulated memory performance in a task-dependent manner, improving associative memory performance, while impairing item memory performance. In addition, subjects with poorer baseline cognitive function improved much more with stimulation. iEEG recordings from the hippocampus during non-stimulation encoding blocks revealed that the associative memory task elicited stronger theta oscillations than did item memory and that stronger theta power was related to memory performance. ConclusionsWe show here for the first time that stimulation-induced associative memory enhancement was linked to increased theta power during retrieval. These results suggest that hippocampal stimulation enhances associative memory but not item memory because it engages more hippocampal theta activity and that, in general, increasing hippocampal theta may provide a neural mechanism for successful memory enhancement.

Dataset of human medial temporal lobe neurons, scalp and intracranial EEG during a verbal working memory task

We present an electrophysiological dataset recorded from nine subjects during a verbal working memory task. Subjects were epilepsy patients undergoing intracranial monitoring for the localization of epileptic seizures. Subjects performed a modified Sternberg task in which the encoding of memory items, maintenance, and recall were temporally separated. The dataset includes simultaneously recorded scalp EEG with the 10–20 system, intracranial EEG (iEEG) recorded with depth electrodes, waveforms and spike times of neurons recorded in the medial temporal lobe, and localization information on the depth electrodes. Subject characteristics and information on each trial (set size, match/mismatch, correct/incorrect, response, and response time) are given. For technical validation of data, we provide spike sorting quality metrics and the spectra of iEEG and scalp EEG signals. This dataset enables the investigation of working memory by providing simultaneous scalp EEG and iEEG recordings, which can be used for connectivity analysis, along with hard-to-obtain neuronal recordings from humans.

Channel-Wise Characterization of High Frequency Oscillations for Automated Identification of the Seizure Onset Zone

High frequency oscillations (HFOs) in intracranial electroencephalography (iEEG) recordings are a promising clinical biomarker that can help define the epileptogenic regions in the brain. The aim of this study is to characterize the spatial and temporal distribution of HFOs in channel-wise instead of event-level as usual and to develop an automated the seizure onset zone (SOZ) identification by using a support vector machine (SVM) approach on the channel-wise features in a short-term recording. In this work, five consecutive patients with medically intractable epilepsy were enrolled. For each patient, ten-minute segments were defined from two hours of iEEG recordings during sleep state. A total of 17 channel-wise features including 6 rate-based, 6 duration-based, 3 amplitude-based, and 2 power-based features of HFOs were extracted from each 10-min segment, which including ripples (Rs, 80-250 Hz) and fast ripples (FRs, 250-500Hz) were detected automatically using validated detectors. Each channel-wise feature was ranked by using the Student's t-test method and the most distinctive features were selected to explore the characteristics of HFOs in each channel. A supervised-learning based SVM classifier with the selected channel-wise features or their combinations was developed to identify each channel within the independently clinician-defined SOZ or not. Over 3,816 chanel-10-min segments of iEEG recordings, the evaluated accuracy, sensitivity, and specificity of the proposed approach with the optimal combination of top five ranked features for SOZ identification are 86.6%, 73.0%, and 94.1%, respectively, for ten-fold cross-validation, and 86.0%, 79.2 %, and 91.8%, respectively, for the leave-1-out cross-validation. Compared with the recently reported SOZ detectors based on event-wise feature of HFOs, the channel-wise features and the combination with machine learning approach demonstrate its feasibility in SOZ identification with a relative higher performance and potentially reduce the time needed currently for long-term recording and manual inspection.

Chapter 19 - iEEG: Dura-lining electrodes

Intracranial electroencephalography (iEEG) is measured from electrodes placed in or on the brain. These measurements have an excellent signal-to-noise ratio and iEEG signals have often been used to decode brain activity or drive brain-computer interfaces (BCIs). iEEG recordings are typically done for seizure monitoring in epilepsy patients who have these electrodes placed for a clinical purpose: to localize both brain regions that are essential for function and others where seizures start. Brain regions not involved in epilepsy are thought to function normally and provide a unique opportunity to learn about human neurophysiology. Intracranial electrodes measure the aggregate activity of large neuronal populations and recorded signals contain many features. Different features are extracted by analyzing these signals in the time and frequency domain. The time domain may reveal an evoked potential at a particular time after the onset of an event. Decomposition into the frequency domain may show narrowband peaks in the spectrum at specific frequencies or broadband signal changes that span a wide range of frequencies. Broadband power increases are generally observed when a brain region is active while most other features are highly specific to brain regions, inputs, and tasks. Here we describe the spatiotemporal dynamics of several iEEG signals that have often been used to decode brain activity and drive BCIs.

Excitation and Inhibition Balance Underlying Epileptiform Activity.

The phenomenon of postictal generalized EEG suppression state (PGES) - a period with suppressed activity following seizure termination and has been found to be associated with sudden unexpected death in epilepsy - remains poorly understood. This article aims to examine the how the balance of excitation and inhibition (E/I balance) affect the dynamics of seizure and PGES. A network of 1000 Izhikevich model neurons was developed and only the strengths of synaptic connections were adjusted to recreate the dynamics observed in recordings of seizure and PGES from human patients. A rapid rise followed by a slow decay of dominant frequency was observed in iEEG recordings of ictal periods and reproduced in the simulated local field potential by changing the E/I balance of the model network. The rate of this dominant frequency evolution was quantified by a single measure, β, which was found to have a significant rank correlation with the duration of PGES in iEEG data and the rate of E/I balance shift in the model. Significance and Conclusion: (i) highlighting the importance of E/I balance in the dynamics of seizure and PGES; (ii) suggesting the measure, β, as a marker for PGES and the shift in E/I balance as a neural correlate for this marker.

IEEGview: an open-source multifunction GUI-based Matlab toolbox for localization and visualization of human intracranial electrodes

Objective. The precise localization of intracranial electrodes is a fundamental step relevant to the analysis of intracranial electroencephalography (iEEG) recordings in various fields. With the increasing development of iEEG studies in human neuroscience, higher requirements have been posed on the localization process, resulting in urgent demand for more integrated, easy-operation and versatile tools for electrode localization and visualization. With the aim of addressing this need, we develop an easy-to-use and multifunction toolbox called iEEGview, which can be used for the localization and visualization of human intracranial electrodes. Approach. iEEGview is written in Matlab scripts and implemented with a GUI. From the GUI, by taking only pre-implant MRI and post-implant CT images as input, users can directly run the full localization pipeline including brain segmentation, image co-registration, electrode reconstruction, anatomical information identification, activation map generation and electrode projection from native brain space into common brain space for group analysis. Additionally, iEEGview implements methods for brain shift correction, visual location inspection on MRI slices and computation of certainty index in anatomical label assignment. Main results. All the introduced functions of iEEGview work reliably and successfully, and are tested by images from 28 human subjects implanted with depth and/or subdural electrodes. Significance. iEEGview is the first public Matlab GUI-based software for intracranial electrode localization and visualization that holds integrated capabilities together within one pipeline. iEEGview promotes convenience and efficiency for the localization process, provides rich localization information for further analysis and offers solutions for addressing raised technical challenges. Therefore, it can serve as a useful tool in facilitating iEEG studies.

Semi-supervised training data selection improves seizure forecasting in canines with epilepsy

ObjectiveConventional selection of pre-ictal EEG epochs for seizure prediction algorithm training data typically assumes a continuous pre-ictal brain state preceding a seizure. This is carried out by defining a fixed duration, pre-ictal time period before seizures from which pre-ictal training data epochs are uniformly sampled. However, stochastic physiological and pathological fluctuations in EEG data characteristics and underlying brain states suggest that pre-ictal state dynamics may be more complex, and selection of pre-ictal training data segments to reflect this could improve algorithm performance. MethodsWe propose a semi-supervised technique to select pre-ictal training data most distinguishable from interictal EEG according to pre-specified data characteristics. The proposed method uses hierarchical clustering to identify optimal pre-ictal data epochs. ResultsIn this paper we compare the performance of a seizure forecasting algorithm with and without hierarchical clustering of pre-ictal periods in chronic iEEG recordings from six canines with naturally occurring epilepsy. Hierarchical clustering of training data improved results for Time In Warning (TIW) (0.18 vs. 0.23) and False Positive Rate (FPR) (0.5 vs. 0.59) when evaluated across all subjects (p < 0.001, n = 6). Results were mixed when evaluating TIW, FPR, and Sensitivity for individual dogs. ConclusionHierarchical clustering is a helpful method for training data selection overall, but should be evaluated on a subject-wise basis. SignificanceThe clustering method can be used to optimize results of forecasting towards sensitivity or TIW or FPR, and therefore can be useful for epilepsy management.

Neural correlates of unstructured motor behaviors

Objective. We studied the relationship between uninstructed, unstructured movements and neural activity in three epilepsy patients with intracranial electroencephalographic (iEEG) recordings. Approach. We used a custom system to continuously record high definition video precisely time-aligned to clinical iEEG data. From these video recordings, movement periods were annotated via semi-automatic tracking based on dense optical flow. Main results. We found that neural signal features (8–32 Hz and 76–100 Hz power) previously identified from task-based experiments are also modulated before and during a variety of movement behaviors. These movement behaviors are coarsely labeled by time period and movement side (e.g. ‘Idle’ and ‘Move’, ‘Right’ and ‘Left’); movements within a label can include a wide variety of uninstructed behaviors. A rigorous nested cross-validation framework was used to classify both movement onset and lateralization with statistical significance for all subjects. Significance. We demonstrate an evaluation framework to study neural activity related to natural movements not evoked by a task, annotated over hours of video. This work further establishes the feasibility to study neural correlates of unstructured behavior through continuous recording in the epilepsy monitoring unit. The insights gained from such studies may advance our understanding of how the brain naturally controls movement, which may inform the development of more robust and generalizable brain–computer interfaces.

Bispectrum and Recurrent Neural Networks: Improved Classification of Interictal and Preictal States

This work proposes a novel approach for the classification of interictal and preictal brain states based on bispectrum analysis and recurrent Long Short-Term Memory (LSTM) neural networks. Two features were first extracted from bilateral intracranial electroencephalography (iEEG) recordings of dogs with naturally occurring focal epilepsy. Single-layer LSTM networks were trained to classify 5-min long feature vectors as preictal or interictal. Classification performances were compared to previous work involving multilayer perceptron networks and higher-order spectral (HOS) features on the same dataset. The proposed LSTM network proved superior to the multilayer perceptron network and achieved an average classification accuracy of 86.29% on held-out data. Results imply the possibility of forecasting epileptic seizures using recurrent neural networks, with minimal feature extraction.

Multi-feature localization of epileptic foci from interictal, intracranial EEG

ObjectiveWhen 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. MethodsWe 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. ResultsThe 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. ConclusionSVM 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. SignificanceIn 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.

Slow-Fast Duffing Neural Mass Model.

Epileptic seizures may be initiated by random neuronal fluctuations and/or by pathological slow regulatory dynamics of ion currents. This paper presents extensions to the Jansen and Rit neural mass model (JRNMM) to replicate paroxysmal transitions in intracranial electroencephalogram (iEEG) recordings. First, the Duffing NMM (DNMM) is introduced to emulate stochastic generators of seizures. The DNMM is constructed by applying perturbations to linear models of synaptic transmission in each neural population of the JRNMM. Then, the slow-fast DNMM is introduced by considering slow dynamics (relative to membrane potential and firing rate) of some internal parameters of the DNMM to replicate pathological evolution of ion currents. Through simulation, it is illustrated that the slow-fast DNMM exhibits transitions to and from seizures with etiologies that are linked either to random input fluctuations or pathological evolution of slow states. Estimation and optimization of a log likelihood function (LLF) using a continuous-discrete unscented Kalman filter (CD-UKF) and a genetic algorithm (GA) are performed to capture dynamics of iEEG data with paroxysmal transitions.

P05-F Sleep stage dependent functional connectivity in SEEG

Objective To determine changes in cortical functional connectivity in relation to sleep stage. Method We analyzed intracranial EEG (iEEG) data recorded from 5 patients with intractable focal epilepsy undergoing presurgical work-up with depth electrodes. Additionally, patients underwent parallel scalp-EEG recording, giving the opportunity for sleep staging. In each patient we analyzed 30 s long segments of iEEG recordings from NREM N2 and REM sleep using a non-linear correlation method, H2 (Wendling, 2001). We compared the mean strength of overall correlation for each corresponding contiguous pair of cortical contacts in REM versus NREM sleep-stage. Functional connectivity was evaluated for the whole spectrum of frequencies and supplementary in an extended gamma-range (30–120 Hz). Results We consistently observed a significant reduction of the mean strength of whole-spectrum correlation in REM versus NREM N2 sleep in contact pairs positioned in the frontal cortex. Additionally we observed a drop in connectivity of the insular cortex, albeit with a larger inter-individual variability. Conversely there was a consistent increase in connectivity in REM versus NREM N2 sleep of the lateral temporal structures (mainly the middle temporal gyrus). The extended gamma-spectrum changes did not always mirror the whole-spectrum changes in connectivity, either by not obtaining significance or by having contradictory values. Conclusions cortical connectivity changes with sleep stage; frontal and insular cortices have a lower overall connectivity during REM sleep as compared to NREM N2 sleep, while lateral temporal structures demonstrate an increase in connectivity. These changes are at most minimally accounted for by changes in gamma-spectrum connectivity.

Hyperdimensional Computing With Local Binary Patterns: One-Shot Learning of Seizure Onset and Identification of Ictogenic Brain Regions Using Short-Time iEEG Recordings.

We develop a fast learning algorithm combining symbolic dynamics and brain-inspired hyperdimensional computing for both seizure onset detection and identification of ictogenic (seizure generating) brain regions from intracranial electroencephalography (iEEG). Our algorithm first transforms iEEG time series from each electrode into symbolic local binary pattern codes, from which a holographic distributed representation of the brain state of interest is constructed across all the electrodes and over time in a hyperdimensional space. The representation is used to quickly learn from few seizures, detect their onset, and identify the spatial brain regions that generated them. We assess our algorithm on our dataset that contains 99 short-time iEEG recordings from 16 drug-resistant epilepsy patients being implanted with 36-100 electrodes. For the majority of the patients (ten out of 16), our algorithm quickly learns from one or two seizures and perfectly (100%) generalizes on novel seizures using k-fold cross-validation. For the remaining six patients, the algorithm requires three to six seizures for learning. Our algorithm surpasses the state-of-the-art including deep learning algorithms by achieving higher specificity (94.84% versus 94.77%) and macroaveraging accuracy (95.42% versus 94.96%), and 74× lower memory footprint, but slightly higher average latency in detection (15.9s versus 14.7s). Moreover, the algorithm can reliably identify (with a p-value ) the relevant electrodes covering an ictogenic brain region at two levels of granularity: cerebral hemispheres and lobes. Our algorithm provides: 1) a unified method for both learning and classification tasks with end-to-end binary operations; 2) one-shot learning from seizure examples; 3) linear computational scalability for increasing number of electrodes; and 4) generation of transparent codes that enables post-translational support for clinical decision making. Our source code and anonymized iEEG dataset are freely available at http://ieeg-swez.ethz.ch.

Impact of withdrawal of antiepileptic medication on the duration of focal onset seizures

PurposeTo systematically evaluate the duration of focal onset seizures under medication withdrawal as a function of drug half-life. MethodsAdults with drug resistant focal epilepsy and invasive electroencephalographic (iEEG) recording between 01/2006 and 06/2016 (n = 128) were identified. Patients with multifocal or unknown epileptic foci were excluded, as well as subclinical seizures, isolated auras, or status epileptic. Antiepileptic drugs (AEDs) were withdrawn upon admission. The seizure duration was determined based on the invasive EEG data, and the latency since start of the monitoring was noted in hours. A negative binomial mixed model was used to compare the seizure durations before and after a cut-off, which was set at 2.5 half-lives of the individual anticonvulsive medication as this is thought to separate therapeutic and ineffective drug levels. ResultsIn total, 70 patients were included in the study and the duration of 672 seizures analyzed. On average, the patients were treated with 2.36 ± 0.78 AEDs. The individual cut-off of 2.5 half-lives was on average reached after 95.02 ± 80.18 h. The seizure frequency (321 vs. 351) and the rate of generalization (15.6% vs. 16.8%) was comparable before and after the individual cut-off point. The mean seizure duration was not statistically significantly prolonged after 2.5 half-lives by a factor of 1.168 for focal onset seizures (p = 0.090) and a factor of 1.091 for secondary generalized seizures (p = 0.545). ConclusionsAlthough AED withdrawal increases the likelihood for epileptic seizures, it did not prolong the seizure duration, nor did it increase the rate of secondary generalization in our study.