In this paper, we study temporal couplings between interictal events of spatially remote regions in order to localize the leading epileptic regions from intracerebral electroencephalogram (iEEG). We aim to assess whether quantitative epileptic graph analysis during interictal period may be helpful to predict the seizure onset zone of ictal iEEG. Using wavelet transform, cross-correlation coefficient, and multiple hypothesis test, we propose a differential connectivity graph (DCG) to represent the connections that change significantly between epileptic and non-epileptic states as defined by the interictal events. Post-processings based on mutual information and multi-objective optimization are proposed to localize the leading epileptic regions through DCG. The suggested approach is applied on iEEG recordings of five patients suffering from focal epilepsy. Quantitative comparisons of the proposed epileptic regions within ictal onset zones detected by visual inspection and using electrically stimulated seizures, reveal good performance of the present method.
Epilepsy; functional connectivity graph; intracerebral EEG; permutation-based multiple hypothesis test; wavelet cross-correlation coefficient.
Patients with normal MR imaging (nonlesional) findings and medically refractory extratemporal epilepsy make up a disproportionate number of nonexcellent outcomes after epilepsy surgery. In this paper, the authors investigated the usefulness of intracranial electroencephalography (iEEG) in the identification of surgical candidates.
Between 1992 and 2002, 51 consecutive patients with normal MR imaging findings and extratemporal epilepsy underwent intracranial electrode monitoring. The implantation of intracranial electrodes was determined by seizure semiology, interictal and ictal scalp EEG, SPECT, and in some patients PET studies. The demographics of patients at the time of surgery, lobar localization of electrode implantation, duration of follow-up, and Engel outcome score were abstracted from the Mayo Rochester Epilepsy Surgery Database. A blinded independent review of the iEEG records was conducted for this study.
Thirty-one (61%) of the 51 patients who underwent iEEG ultimately underwent resection for their epilepsy. For 28 (90.3%) of the 31 patients who had epilepsy surgery, adequate information regarding follow-up (> 1 year), seizure frequency, and iEEG recordings was available. Twenty-six (92.9%) of 28 patients had frontal lobe resections, and 2 had parietal lobe resections. The most common iEEG pattern at seizure onset in the surgically treated group was a focal high-frequency discharge (in 15 [53.6%] of 28 patients). Ten (35.7%) of the 28 surgically treated patients were seizure free. Fourteen (50%) had Engel Class I outcomes, and overall, 17 (60.7%) had significant improvement (Engel Class I and IIAB with ≥ 80% seizure reduction). Focal high-frequency oscillation at seizure onset was associated with Engel Class I surgical outcome (12 [85.7%] of 14 patients, p = 0.02), and it was uncommon in the nonexcellent outcome group (3 [21.4%] of 14 patients).
A focal high-frequency oscillation (> 20 Hz) at seizure onset on iEEG may identify patients with nonlesional extratemporal epilepsy who are likely to have an Engel Class I outcome after epilepsy surgery. The prospect of excellent outcome in nonlesional extratemporal lobe epilepsy prior to intracranial monitoring is poor (14 [27.5%] of 51 patients). However, iEEG can further stratify patients and help identify those with a greater likelihood of Engel Class I outcome after surgery.
electroencephalography; epilepsy surgery; high-frequency oscillation
To investigate the feasibility of using noninvasive EEG source imaging approach to image continuous seizure activity in pediatric epilepsy patients.
Nine pediatric patients with medically intractable epilepsy were included in this study. Eight of the patients had extratemporal lobe epilepsy and one had temporal lobe epilepsy. All of the patients underwent resective surgery and seven of them underwent intracranial EEG (iEEG) monitoring. The ictal EEG was analyzed using a noninvasive dynamic seizure imaging (DSI) approach. The DSI approach separates scalp EEGs into independent components and extracts the spatio-temporal ictal features to achieve dynamic imaging of seizure sources. Surgical resection and intracranial recordings were used to validate the noninvasive imaging results.
The DSI determined seizure onset zones (SOZs) in these patients were localized within or in close vicinity to the surgically resected region. In the seven patients with intracranial monitoring, the estimated seizure onset sources were concordant with the seizure onset zones of iEEG. The DSI also localized the multiple foci involved in the later seizure propagation, which were confirmed by the iEEG recordings.
Dynamic seizure imaging can noninvasively image the seizure activations in pediatric patients with both temporal and extratemporal lobe epilepsy.
EEG seizure imaging can potentially be used to noninvasively image the SOZs and aid the pre-surgical planning in pediatric epilepsy patients.
Pediatric patients; Epilepsy; EEG; Dynamic seizure imaging; Intracranial recording; Surgical resection
Scalp electroencephalography (EEG) has been established as a major component of the pre-surgical evaluation for epilepsy surgery. However, its ability to localize seizure onset zones (SOZ) has been significantly restricted by its low spatial resolution and indirect correlation with underlying brain activities. Here we report a novel non-invasive dynamic seizure imaging (DSI) approach based upon high-density EEG recordings. This novel approach was particularly designed to image the dynamic changes of ictal rhythmic discharges that evolve through time, space and frequency. This method was evaluated in a group of 8 epilepsy patients and results were rigorously validated using intracranial EEG (iEEG) (n = 3) and surgical outcome (n = 7). The DSI localized the ictal activity in concordance with surgically resected zones and ictal iEEG recordings in the cohort of patients. The present promising results support the ability to precisely and accurately image dynamic seizure activity from non-invasive measurements. The successful establishment of such a non-invasive seizure imaging modality for surgical evaluation will have a significant impact in the management of medically intractable epilepsy.
High-resolution EEG; Dynamic seizure imaging; Pre-surgical planning
The characterization of spatial network dynamics is desirable for a better understanding of seizure physiology. The goal of this work is to develop a computational method for identifying transient spatial patterns from intracranial electroencephalographic (iEEG) data.
Starting with bivariate synchrony measures, such as phase correlation, a two-step clustering procedure is used to identify statistically significant spatial network patterns, whose temporal evolution can be inferred. We refer to this as the composite synchrony profile (CSP) method.
The CSP method was verified with simulated data and evaluated using ictal and interictal recordings from three patients with intractable epilepsy. Application of the CSP method to these clinical iEEG datasets revealed a set of distinct CSPs with topographies consistent with medial temporal/limbic and superior parietal/medial frontal networks thought to be involved in the seizure generation process.
By combining relatively straightforward multivariate signal processing techniques, such as phase synchrony, with clustering and statistical hypothesis testing, the methods we describe may prove useful for network definition and identification.
The network patterns we observe using the CSP method cannot be inferred from direct visual inspection of the raw time series data, nor are they apparent in voltage-based topographic map sequences.
Epilepsy; network dynamics; spatial dynamics; seizure onset; phase synchrony
Directed graphs (digraphs) derived from interictal periods of intracerebral EEG (iEEG) recordings can be used to estimate the leading interictal epileptic regions for presurgery evaluations. For this purpose, quantification of the emittance contribution of each node to the rest of digraph is important. However, the usual digraph measures are not very well suited for this quantification. Here we compare the efficiency of recently introduced local information measure LI and a new measure called total global efficiency with classical measures like global efficiency, local efficiency and node degree. For evaluation, the estimated leading interictal epileptic regions based on five measures are compared with seizure onset zones obtained by visual inspection of epileptologists for five patients. The comparison revealed the superior performance of LI measure. We showed efficiency of different digraph measures for the purpose of source and sink node identification.
Brain Mapping; methods; Electroencephalography; Epilepsy; physiopathology; Hippocampus; physiopathology; Humans; Directed graph measurements; Epilepsy; Intracerebral EEG recordings; Multiple graph analysis; Mutual information
Analysis of intracranial electroencephalographic (iEEG) recordings in patients with temporal lobe epilepsy (TLE) has revealed characteristic dynamical features that distinguish the interictal, ictal, and postictal states and inter-state transitions. Experimental investigations into the mechanisms underlying these observations require the use of an animal model. A rat TLE model was used to test for differences in iEEG dynamics between well-defined states and to test specific hypotheses: 1) the short-term maximum Lyapunov exponent (STLmax), a measure of signal order, is lowest and closest in value among cortical sites during the ictal state, and highest and most divergent during the postictal state; 2) STLmax values estimated from the stimulated hippocampus are the lowest among all cortical sites; and 3) the transition from the interictal to ictal state is associated with a convergence in STLmax values among cortical sites. iEEGs were recorded from bilateral frontal cortices and hippocampi. STLmax and T-index (a measure of convergence/divergence of STLmax between recorded brain areas) were compared among the four different periods. Statistical tests (ANOVA and multiple comparisons) revealed that ictal STLmax was lower (p < 0.05) than other periods, STLmax values corresponding to the stimulated hippocampus were lower than those estimated from other cortical regions, and T-index values were highest during the postictal period and lowest during the ictal period. Also, the T-index values corresponding to the preictal period were lower than those during the interictal period (p < 0.05). These results indicate that a rat TLE model demonstrates several important dynamical signal characteristics similar to those found in human TLE and support future use of the model to study epileptic state transitions.
Seizures; Temporal lobe epilepsy; EEG dynamics
High-frequency oscillations (HFOs) known as ripples (80–250 Hz) and fast ripples (250–500 Hz) can be recorded from macroelectrodes inserted in patients with intractable focal epilepsy. They are most likely linked to epileptogenesis and have been found in the seizure onset zone (SOZ) of human ictal and interictal recordings. HFOs occur frequently at the time of interictal spikes, but were also found independently. This study analyses the relationship between spikes and HFOs and the occurrence of HFOs in nonspiking channels.
Intracerebral EEGs of 10 patients with intractable focal epilepsy were studied using macroelectrodes. Rates of HFOs within and outside spikes, the overlap between events, event durations, and the percentage of spikes carrying HFOs were calculated and compared according to anatomical localization, spiking activity, and relationship to the SOZ.
HFOs were found in all patients, significantly more within mesial temporal lobe structures than in neocortex. HFOs could be seen in spiking as well as nonspiking channels in all structures. Rates and durations of HFOs were significantly higher in the SOZ than outside. It was possible to establish a rate of HFOs to identify the SOZ with better sensitivity and specificity than with the rate of spikes.
HFOs occurred to a large extent independently of spikes. They are most frequent in mesial temporal structures. They are prominent in the SOZ and provide additional information on epileptogenicity independently of spikes. It was possible to identify the SOZ with a high specificity by looking at only 10 min of HFO activity.
PMID: 18479382 CAMSID: cams3466
Epilepsy; High-frequency oscillations; Spikes; Seizure onset zone; Intracranial electrodes
The purpose of this study is to assess the accuracy of spatiotemporal source analysis of magnetoencephalography (MEG) and scalp electroencephalography (EEG) for representing the propagation of frontotemporal spikes in patients with partial epilepsy. This study focuses on frontotemporal spikes, which are typically characterized by a preceding anterior temporal peak followed by an ipsilateral inferior frontal peak. Ten patients with frontotemporal spikes on MEG/EEG were studied. We analyzed the propagation of temporal to frontal epileptic spikes on both MEG and EEG independently by using a cortically-constrained minimum norm estimate (MNE). Spatiotemporal source distribution of each spike was obtained on the cortical surface derived from the patient’s MRI. All patients underwent an extraoperative intracranial EEG (IEEG) recording covering temporal and frontal lobes after presurgical evaluation. We extracted source waveforms of MEG and EEG from the source distribution of interictal spikes at the sites corresponding to the location of intracranial electrodes. The time differences of the ipsilateral temporal and frontal peaks as obtained by MEG, EEG and IEEG were statistically compared in each patient. In all patients, MEG and IEEG showed similar time differences between temporal and frontal peaks. The time differences of EEG spikes were significantly smaller than those of IEEG in nine of ten patients. Spatiotemporal analysis of MEG spikes models the time course of frontotemporal spikes as observed on IEEG more adequately than EEG in our patients. Spatiotemporal source analysis may be useful for planning epilepsy surgery, by predicting the pattern of IEEG spikes.
epilepsy; magnetoencephalography; electroencephalography; propagation; spatiotemporal source analysis; minimum norm estimate
Improved non-invasive localization of the epileptogenic foci prior to epilepsy surgery would improve surgical outcome in patients with partial seizure disorders. A critical component for the identification of the epileptogenic brain is the analysis of electrophysiological data obtained during ictal activity from prolonged intracranial recordings. The development of a noninvasive means to identify the seizure onset zone (SOZ) would thus play an important role in treating patients with intractable epilepsy. In the present study, we have investigated non-invasive imaging of epileptiform activity in patients with medically intractable epilepsy by means of a cortical potential imaging (CPI) technique. Eight pediatric patients (1M/7F, ages 4–14 year) with intractable partial epilepsy were studied. Each patient had multiple (6 to 14) interictal spikes (IIS) subjected to the CPI analysis. Realistic geometry boundary element head models were built using each individual’s MRI in order to maximize the imaging precision. CPI analysis was performed on the IISs, and extrema in the estimated CPI images were compared with SOZs as determined from the ictal electrocorticogram (ECoG) recordings, as well as the resected areas in the patients and surgical outcomes. The distances between the maximum cortical activities of the IISs reflected by the estimated cortical potential distributions and the SOZs were determined to quantitatively evaluate the performance of the CPI in localizing the epileptogenic zone. Ictal ECoG recordings revealed that six patients exhibited a single epileptogenic focus while two patients had multiple foci. In each patient, the CPI results revealed an area of activity overlapping with the SOZs as identified by ictal ECoG. The distance from the extreme of the CPI images at the peak of IIS to the nearest intracranial electrode associated with the onset of the ictal activity was evaluated for each patient and the averaged distance was 4.6 mm. In the group of patients studied, the CPI imaged epileptogenic foci were within the resected areas. According to the follow-up of the eight patients included, two were seizure free and six had substantial reduction in seizure frequency. These promising results demonstrate the potential for noninvasive localization of the epileptogenic focus from interictal scalp EEG recordings. Confirmation of our results may have a significant impact on the process of presurgical planning in pediatric patients with intractable epilepsy by dramatically reducing or potentially eliminating the use of intracranial recording.
Cortical imaging; localization; epileptogenic focus; interictal spike; pediatric
High frequency oscillations (HFOs) can be recorded with depth electrodes in focal epilepsy patients. They occur during seizures and interictally and seem important in seizure genesis. We investigated whether interictal and ictal HFOs occur in the same regions and how they relate to epileptiform spikes.
In 25 patients, spikes, ripples (80–250 Hz) and fast ripples (FR: 250–500 Hz) and their co-occurrences were marked during interictal slow wave sleep (5–10 min), during 10 preictal seconds and 5 s following seizure onset. We compared occurrence and spatial distribution between these periods.
HFOs and spikes increased from interictal to ictal periods: the percentage of time occupied by ripples increased from 2.3% to 6.5%, FR from 0.2% to 0.8%, spikes from 1.1% to 4.8%. HFOs increased from interictal to preictal periods in contrast to spikes. Spikes were in different channels in the interictal, preictal and ictal periods whereas HFOs largely remained in the same channels.
HFOs remain confined to the same, possibly epileptogenic, area, during interictal and ictal periods, while spikes are more widespread during seizures than interictally.
Ictal and interictal HFOs represent the same (epileptogenic) area and are probably similar phenomena.
PMID: 21030302 CAMSID: cams3344
High frequency oscillations; Focal epilepsy; Epilepsy surgery; Depth EEG; Ictogenesis
Purpose. To investigate EEG and SPECT in the surgical outcome of patients with normal MRI (nonlesional) and extratemporal lobe epilepsy. Methods. We retrospectively identified 41 consecutive patients with nonlesional extratemporal epilepsy who underwent epilepsy surgery between 1997 and 2007. The history, noninvasive diagnostic studies (scalp EEG, MRI, and SPECT) and intracranial EEG (iEEG) monitoring was reviewed. Scalp and iEEG ictal onset patterns were defined. The association of preoperative studies and postoperative seizure freedom was analyzed using Kaplan-Meier analysis, log-rank test, and Cox proportional hazard. Results. Thirty-six of 41 patients had adequate information with a minimum of 1-year followup. Favorable surgical outcome was identified in 49% of patients at 1 year, and 35% at 4-year. On scalp EEG, an ictal onset pattern consisting of focal beta-frequency discharge (>13–125 Hz) was associated with favorable surgical outcome (P = 0.02). Similarly, a focal fast-frequency oscillation (>13–125 Hz) on iEEG at ictal onset was associated with favorable outcome (P = 0.03). Discussion. A focal fast-frequency discharge at ictal onset identifies nonlesional MRI, extratemporal epilepsy patients likely to have a favorable outcome after resective epilepsy surgery.
To evaluate the diagnostic value of individual noninvasive presurgical modalities and to study their role in surgical management of nonlesional pediatric epilepsy patients.
We retrospectively studied 14 children (3–18 years) with nonlesional intractable focal epilepsy. Clinical characteristics, surgical outcome, localizing features on 3 presurgical diagnostic tests (subtraction peri-ictal SPECT coregistered to MRI [SISCOM], statistical parametric mapping [SPM] analysis of [18F] FDG-PET, magnetoencephalography [MEG]), and intracranial EEG (iEEG) were reviewed. The localization of each individual test was determined for lobar location by visual inspection. Concordance of localization between each test and iEEG was scored as follows: 2 = lobar concordance; 1 = hemispheric concordance; 0 = discordance or nonlocalization. Total concordance score in each patient was measured by the summation of concordance scores for all 3 tests.
Seven (50%) of 14 patients were seizure-free for at least 12 months after surgery. One (7%) had only rare seizures and 6 (43%) had persistent seizures. MEG (79%, 11/14) and SISCOM (79%, 11/14) showed greater lobar concordance with iEEG than SPM-PET (13%, 3/14) (p < 0.05). SPM-PET provided hemispheric lateralization (71%, 10/14) more often than lobar localization. Total concordance score tended to be greater for seizure-free patients (4.7) than for non–seizure-free patients (3.9).
Our data suggest that MEG and SISCOM are better tools for lobar localization than SPM analysis of FDG-PET in children with nonlesional epilepsy. A multimodality approach may improve surgical outcome as well as selection of surgical candidates in patients without MRI abnormalities.
This paper presents an application of genetic programming (GP) to optimally select and fuse conventional features (C-features) for the detection of epileptic waveforms within intracranial electroencephalogram (IEEG) recordings that precede seizures, known as seizure-precursors. Evidence suggests that seizure-precursors may localize regions important to seizure generation on the IEEG and epilepsy treatment. However, current methods to detect epileptic precursors lack a sound approach to automatically select and combine C-features that best distinguish epileptic events from background, relying on visual review predominantly. This work suggests GP as an optimal alternative to create a single feature after evaluating the performance of a binary detector that uses: 1) genetically programmed features; 2) features selected via GP; 3) forward sequentially selected features; and 4) visually selected features. Results demonstrate that a detector with a genetically programmed feature outperforms the other three approaches, achieving over 78.5% positive predictive value, 83.5% sensitivity, and 93% specificity at the 95% level of confidence.
genetic programming; C-features; feature-selection; feature-fusion; binary detection; epilepsy; epileptic precursors; IEEG
Localizing an epileptic network is essential for guiding neurosurgery and antiepileptic medical devices as well as elucidating mechanisms that may explain seizure-generation and epilepsy. There is increasing evidence that pathological oscillations may be specific to diseased networks in patients with epilepsy and that these oscillations may be a key biomarker for generating and indentifying epileptic networks. We present a semi-automated method that detects, maps, and mines pathological gamma (30–100 Hz) oscillations (PGOs) in human epileptic brain to possibly localize epileptic networks. We apply the method to standard clinical iEEG (<100 Hz) with interictal PGOs and seizures from six patients with medically refractory epilepsy. We demonstrate that electrodes with consistent PGO discharges do not always coincide with clinically determined seizure onset zone (SOZ) electrodes but at times PGO-dense electrodes include secondary seizure-areas (SS) or even areas without seizures (NS). In 4/5 patients with epilepsy surgery, we observe poor (Engel Class 4) post-surgical outcomes and identify more PGO-activity in SS or NS than in SOZ. Additional studies are needed to further clarify the role of PGOs in epileptic brain.
Epileptic network; Interictal epileptic discharge; Pathological gamma oscillation; Detection; Mapping; Data-mining
We present a multistage fuzzy rule-based algorithm for epileptic seizure onset detection. Amplitude, frequency, and entropy-based features were extracted from intracranial electroencephalogram (iEEG) recordings and considered as the inputs for a fuzzy system. These features extracted from multichannel iEEG signals were combined using fuzzy algorithms both in feature domain and in spatial domain. Fuzzy rules were derived based on experts' knowledge and reasoning. An adaptive fuzzy subsystem was used for combining characteristics features extracted from iEEG. For the spatial combination, three channels from epileptogenic zone and one from remote zone were considered into another fuzzy subsystem. Finally, a threshold procedure was applied to the fuzzy output derived from the final fuzzy subsystem. The method was evaluated on iEEG datasets selected from Freiburg Seizure Prediction EEG (FSPEEG) database. A total of 112.45 hours of intracranial EEG recordings was selected from 20 patients having 56 seizures was used for the system performance evaluation. The overall sensitivity of 95.8% with false detection rate of 0.26 per hour and average detection latency of 15.8 seconds was achieved.
In the present study, we have developed a novel patient-specific rule-based seizure prediction system for focal neocortical epilepsy.
Five univariate measures including correlation dimension, correlation entropy, noise level, Lempel-Ziv complexity, and largest Lyapunov exponent as well as one bivariate measure, nonlinear interdependence, were extracted from non-overlapping 10-second segments of intracranial electroencephalogram (iEEG) data recorded using electrodes implanted deep in the brain and/or placed on the cortical surface. The spatio-temporal information was then integrated by using rules established based on patient-specific changes observed in the period prior to a seizure sample for each patient. The system was tested on 316 h of iEEG data containing 49 seizures recorded in eleven patients with medically intractable focal neocortical epilepsy.
For seizure occurrence periods of 30 and 50 min our method showed an average sensitivity of 79.9% and 90.2% with an average false prediction rate of 0.17 and 0.11/h, respectively. In terms of sensitivity and false prediction rate, the system showed superiority to random and periodical predictors.
The nonlinear analysis of iEEG in the period prior to seizures revealed patient-specific spatio-temporal changes that were significantly different from those observed within baselines in the majority of the seizures analyzed in this study.
The present results suggest that the patient specific rule-based approach may become a potentially useful approach for predicting seizures prior to onset.
Focal epilepsy; intracranial EEG; nonlinear dynamics; seizure prediction
Few studies have included magnetoencephalography (MEG) when assessing the diagnostic value of presurgical modalities in a nonlesional epilepsy population. Here, we compare single photon emission computed tomography (SPECT), positron emission tomography (PET), video-EEG (VEEG), and MEG, with intracranial EEG (iEEG) to determine the value of individual modalities to surgical decisions. We analyzed 23 adult epilepsy patients with no abnormal MRI findings who had undergone surgical resection. Localization of individual presurgical tests was determined for hemispheric and lobar locations based on visual analysis. Each localization result was compared with the ictal onset zone (IOZ) defined by using iEEG. The highest to the lowest hemispheric concordance rates were MEG (83%) > ictal VEEG (78%) > PET (70%) > ictal SPECT (57%). The highest to lowest lobar concordance rates were ictal VEEG = MEG (65%) > PET (57%) > ictal SPECT (52%). Statistical analysis showed MEG to have a higher hemispheric concordance than that of ictal SPECT (P = 0.031). We analyzed the effects of MEG clustered-area resection on surgical outcome. Patients who had resection of MEG clusters showed a better surgical outcome than those without such resection (P = 0.038). It is suggested that MEG-based localization had the highest concordance with the iEEG-defined IOZ. Furthermore, MEG cluster resection has prognostic significance in predicting surgical outcome.
Magnetoencephalography; Presurgical Evaluation; Nonlesional Epilepsy; Surgical Outcome
Magnetoencephalography (MEG) has been shown a useful diagnostic tool for presurgical evaluation of pediatric medically intractable partial epilepsy as MEG source localization has been shown to improve the likelihood of seizure onset zone (SOZ) sampling during subsequent evaluation with intracranial EEG (ICEEG). We investigated whether ictal MEG onset source localization further improves results of interictal MEG in defining the SOZ.
We identified 20 pediatric patients with one habitual seizure during MEG recordings between October 2007 and April 2011. MEG was recorded with sampling rates of 600 Hz and 4000 Hz for 10 and 2 minutes respectively. Continuous head
localization (CHL) was applied. Source localization analyses were applied using multiple algorithms, both at the beginning of ictal onset and for interictal MEG discharges. Ictal MEG onsets were identified by visual inspection and power spectrum using short-time Fourier transform (STFT). Source localizations were compared with ICEEG, surgical procedure and outcome.
Eight patients met all inclusion criteria. Five of the 8 patients (63%) had concordant ictal MEG onset source localization and interictal MEG discharge source localizations in the same lobe, but the source of ictal MEG onset was closer to the SOZ defined by ICEEG.
Although the capture of seizures during MEG recording is challenging, the source localization for ictal MEG onset proved to be a useful tool for presurgical evaluation in our pediatric population with medically intractable epilepsy.
ictal MEG; interictal MEG discharge; source localization; presurgical evaluation; surgical outcome
Epilepsy affects 50 million people worldwide, and seizures in 30% of the cases remain drug resistant. This has increased interest in responsive neurostimulation, which is most effective when administered during seizure onset. We propose a novel framework for seizure onset detection that involves (i) constructing statistics from multichannel intracranial EEG (iEEG) to distinguish nonictal versus ictal states; (ii) modeling the dynamics of these statistics in each state and the state transitions; you can remove this word if there is no room. (iii) developing an optimal control-based “quickest detection” (QD) strategy to estimate the transition times from nonictal to ictal states from sequential iEEG measurements. The QD strategy minimizes a cost function of detection delay and false positive probability. The solution is a threshold that non-monotonically decreases over time and avoids responding to rare events that normally trigger false positives. We applied QD to four drug resistant epileptic patients (168 hour continuous recordings, 26–44 electrodes, 33 seizures) and achieved 100% sensitivity with low false positive rates (0.16 false positive/hour). This article is part of a Supplemental Special Issue entitled The Future of Automated Seizure Detection and Prediction.
► A control-theoretical framework for automatic online seizure detection is proposed. ► This framework combines iEEGs, network-based statistics, and optimization tools. ► The detection algorithm minimizes detection delays and probability of false alarms. ► Reported results show 100% sensitivity and low false positive rates.
Quickest detection; Bayesian estimation; Multivariate analysis; Intracranial electroencephalogram; Optimal control; Hidden Markov model; Dynamic programming; Networks
This paper presents grammatical evolution (GE) as an approach to select and combine features for detecting epileptic oscillations within clinical intracranial electroencephalogram (iEEG) recordings of patients with epilepsy. Clinical iEEG is used in preoperative evaluations of a patient who may have surgery to treat epileptic seizures. Literature suggests that pathological oscillations may indicate the region(s) of brain that cause epileptic seizures, which could be surgically removed for therapy. If this presumption is true, then the effectiveness of surgical treatment could depend on the effectiveness in pinpointing critically diseased brain, which in turn depends on the most accurate detection of pathological oscillations. Moreover, the accuracy of detecting pathological oscillations depends greatly on the selected feature(s) that must objectively distinguish epileptic events from average activity, a task that visual review is inevitably too subjective and insufficient to resolve. Consequently, this work suggests an automated algorithm that incorporates grammatical evolution (GE) to construct the most sufficient feature(s) to detect epileptic oscillations within the iEEG of a patient. We estimate the performance of GE relative to three alternative methods of selecting or combining features that distinguish an epileptic gamma (~65-95 Hz) oscillation from normal activity: forward sequential feature-selection, backward sequential feature-selection, and genetic programming. We demonstrate that a detector with a grammatically evolved feature exhibits a sensitivity and selectivity that is comparable to a previous detector with a genetically programmed feature, making GE a useful alternative to designing detectors.
grammatical evolution; detector; epileptic oscillations; intracranial EEG
Electroencephalography (EEG) has an important role in the diagnosis and classification of epilepsy. It can provide information for predicting the response to antiseizure drugs and to identify the surgically remediable epilepsies. In temporal lobe epilepsy (TLE) seizures could originate in the medial or lateral neocortical temporal region, and many of these patients are refractory to medical treatment. However, majority of patients have had excellent results after surgery and this often relies on the EEG and magnetic resonance imaging (MRI) data in presurgical evaluation. If the scalp EEG data is insufficient or discordant, invasive EEG recording with placement of intracranial electrodes could identify the seizure focus prior to surgery. This paper highlights the general information regarding the use of EEG in epilepsy, EEG patterns resembling epileptiform discharges, and the interictal, ictal and postictal findings in mesial temporal lobe epilepsy using scalp and intracranial recordings prior to surgery. The utility of the automated seizure detection and computerized mathematical models for increasing yield of non-invasive localization is discussed. This paper also describes the sensitivity, specificity, and predictive value of EEG for seizure recurrence after withdrawal of medications following seizure freedom with medical and surgical therapy.
High-frequency oscillations (HFOs) can be recorded in epileptic patients with clinical intracranial EEG. HFOs have been associated with seizure genesis because they occur in the seizure focus and during seizure onset. HFOs are also found interictally, partly co-occurring with epileptic spikes. We studied how HFOs are influenced by antiepileptic medication and seizure occurrence, to improve understanding of the pathophysiology and clinical meaning of HFOs.
Intracerebral depth EEG was partly sampled at 2,000 Hz in 42 patients with intractable focal epilepsy. Patients with five or more usable nights of recording were selected. A sample of slow-wave sleep from each night was analyzed, and HFOs (ripples: 80–250 Hz, fast ripples: 250–500 Hz) and spikes were identified on all artifact-free channels. The HFOs and spikes were compared before and after seizures with stable medication dose and during medication reduction with no intervening seizures.
Twelve patients with five to eight nights were included. After seizures, there was an increase in spikes, whereas HFO rates remained the same. Medication reduction was followed by an increase in HFO rates and mean duration.
Contrary to spikes, high-frequency oscillations (HFOs) do not increase after seizures, but do so after medication reduction, similarly to seizures. This implies that spikes and HFOs have different pathophysiologic mechanisms and that HFOs are more tightly linked to seizures than spikes. HFOs seem to play an important role in seizure genesis and can be a useful clinical marker for disease activity.
PMID: 19289737 CAMSID: cams3470
Electrode arrays are sometimes implanted in the brains of patients with intractable epilepsy to better localize seizure foci before epilepsy surgery. Analysis of intracranial EEG (iEEG) recordings is typically performed in the electrode channel domain without explicit separation of the sources that generate the signals. However, intracranial EEG signals, like scalp EEG signals, could be linear mixtures of local activity and volume-conducted activity arising in multiple source areas. Independent component analysis (ICA) has recently been applied to scalp EEG data, and shown to separate the signal mixtures into independently generated brain and non-brain source signals. Here, we applied ICA to unmix source signals from intracranial EEG recordings from four epilepsy patients during a visually cued finger movement task in the presence of background pathological brain activity. This ICA decomposition demonstrated that the iEEG recordings were not maximally independent, but rather are linear mixtures of activity from multiple sources. Many of the independent component (IC) projections to the iEEG recording grid were consistent with sources from single brain regions, including components exhibiting classic movement-related dynamics. Notably, the largest IC projection to each channel accounted for no more than 20–80% of the channel signal variance, implying that in general intracranial recordings cannot be accurately interpreted as recordings of independent brain sources. These results suggest that ICA can be used to identify and monitor major field sources of local and distributed functional networks generating iEEG data. ICA decomposition methods are useful for improving the fidelity of source signals of interest, likely including distinguishing the sources of pathological brain activity.
ICA; intracranial; EEG; electrocorticography; ECoG; epilepsy; mu
Intracranial electroencephalography (iEEG) is clinically indicated for medically refractory epilepsy and is a promising approach for developing neural prosthetics. These recordings also provide valuable data for cognitive neuroscience research. Accurate localization of iEEG electrodes is essential for evaluating specific brain regions underlying the electrodes that indicate normal or pathological activity, as well as for relating research findings to neuroimaging and lesion studies. However, electrodes are frequently tucked underneath the edge of a craniotomy, inserted via a burr hole, or placed deep within the brain, where their locations cannot be verified visually or with neuronavigational systems. We show that one existing method, registration of postimplant CT with preoperative MRI, can result in errors exceeding 1 cm.
We present a novel method for localizing iEEG electrodes using routinely acquired surgical photographs, X-ray radiographs, and magnetic resonance imaging (MRI) scans. Known control points are used to compute projective transforms that link the different image sets, ultimately allowing hidden electrodes to be localized, in addition to refining the location of manually registered visible electrodes. As the technique does not require any calibration between the different image modalities, it can be applied to existing image databases. The final result is a set of electrode positions on the patient’s rendered MRI yielding locations relative to sulcal and gyral landmarks on individual anatomy, as well as MNI coordinates. We demonstrate the results of our method in eight epilepsy patients implanted with electrode grids spanning the left hemisphere.
Subdural electrodes; Intracranial electroencephalography; Electrocorticography; Epilepsy surgery; 3-D localization; Coregistration; Surface rendering; X-ray; CT; MRI