Rapid identification of epileptogenic sites in the intracranial EEG (original) (raw)
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Electroencephalography in epilepsy: look for what could be beyond the visual inspection
Neurological Sciences, 2019
Since its starting point in 1929, human scalp electroencephalography (EEG) has been routinely interpreted by visual inspection of waveforms using the assumption that the activity at a given electrode is a representation of the activity of the cerebral cortex under it, but such a method has some limitations. In this review, we will discuss three advanced methods to obtain valuable information from scalp EEG in epilepsy using innovative technologies. Authors who had previous publications in the field provided a narrative review. Spike voltage topography of interictal spikes is a potential way to improve non-invasive EEG localization in focal epilepsies. Electrical source imaging is also a complementary technique in localization of the epileptogenic zone in patients who are candidates for epilepsy surgery. Quantitative EEG simplifies the large amount of information in continuous EEG by providing a static graphical display. Scalp electroencephalography has the potential to offer more spatial and temporal information than the traditional way of visual inspection alone in patients with epilepsy. Fortunately, with the help of modern digital EEG equipment and computer-assisted analysis, this information is more accessible.
Automated long-term EEG analysis to localize the epileptogenic zone
Epilepsia Open
Objective: We investigated the performance of automatic spike detection and subsequent electroencephalogram (EEG) source imaging to localize the epileptogenic zone (EZ) from long-term EEG recorded during video-EEG monitoring. Methods: In 32 patients, spikes were automatically detected in the EEG and clustered according to their morphology. The two spike clusters with most single events in each patient were averaged and localized in the brain at the half-rising time and peak of the spike using EEG source imaging. On the basis of the distance from the sources to the resection and the known patient outcome after surgery, the performance of the automated EEG analysis to localize the EZ was quantified. Results: In 28 out of the 32 patients, the automatically detected spike clusters corresponded with the reported interictal findings. The median distance to the resection in patients with Engel class I outcome was 6.5 and 15 mm for spike cluster 1 and 27 and 26 mm for cluster 2, at the peak and the half-rising time of the spike, respectively. Spike occurrence (cluster 1 vs. cluster 2) and spike timing (peak vs. half-rising) significantly influenced the distance to the resection (p < 0.05). For patients with Engel class II, III, and IV outcomes, the median distance increased to 36 and 36 mm for cluster 1. Localizing spike cluster 1 at the peak resulted in a sensitivity of 70% and specificity of 100%, positive prediction value (PPV) of 100%, and negative predictive value (NPV) of 53%. Including the results of spike cluster 2 led to an increased sensitivity of 79% NPV of 55% and diagnostic OR of 11.4, while the specificity dropped to 75% and the PPV to 90%. Significance: We showed that automated analysis of long-term EEG recordings results in a high sensitivity and specificity to localize the epileptogenic focus.
Epilepsia, 2004
Seizures recorded during long-term monitoring with implanted intracranial electrodes are typically interpreted by visual inspection alone by using digital display systems. When high-frequency activity is digitized and displayed on a typical monitor, it is altered in ways that are not always appreciated and that may have an impact on the intracranial EEG (ICEEG) interpretation. We describe a case of a neocorticalonset seizure in which false localization occurred with a 12-s per screen display. Because frequencies in excess of 100 Hz are not uncommon in neocortical seizures, at most 4 to 5 s of EEG, depending on the screen resolution, data-sampling rate, and other factors, should be displayed at one time during visual interpretation to localize the seizure onset. Alternatively, spectral analysis should be performed on recordings of neocortical seizures to detect high-frequency activity that may be missed on visual inspection.
Brain topography, 2014
In patients diagnosed with pharmaco-resistant epilepsy, cerebral areas responsible for seizure generation can be defined by performing implantation of intracranial electrodes. The identification of the epileptogenic zone (EZ) is based on visual inspection of the intracranial electroencephalogram (IEEG) performed by highly qualified neurophysiologists. New computer-based quantitative EEG analyses have been developed in collaboration with the signal analysis community to expedite EZ detection. The aim of the present report is to compare different signal analysis approaches developed in four different European laboratories working in close collaboration with four European Epilepsy Centers. Computer-based signal analysis methods were retrospectively applied to IEEG recordings performed in four patients undergoing pre-surgical exploration of pharmaco-resistant epilepsy. The four methods elaborated by the different teams to identify the EZ are based either on frequency analysis, on nonlin...
2007
A graphical and analytical description of epileptic seizures based on amplitude modulation and frequency modulation components of intracranial EEG (iEEG) is proposed. This representation allows the characterization of seizures and their different stages from the iEEG by means of triangles whose vertexes are the centroids (c m ) of the signal during preictal, ictal and postictal periods. The centroid is the point defined by the average values of instantaneous amplitude and frequency, a i and f i respectively. Data were obtained from 8 patients with recurrent epilepsy, 170 records were processed, 62 of which were seizures and 108 interictal signals. Results show that the centroids of the ictal periods are located in a region of the space a i -f i distant from the centroids corresponding to interictal and postictal periods. This original representation of epileptic seizures can facilitate the visualization of stagetransitions and discrimination between the different stages of the iEEG signal. An additional advantage of the method is that the information contained in the signal is synthesized significantly.
EPINETLAB: A Software for Seizure-Onset Zone Identification From Intracranial EEG Signal in Epilepsy
Frontiers in Neuroinformatics, 2018
The pre-operative workup of patients with drug-resistant epilepsy requires in some candidates the identification from intracranial EEG (iEEG) of the seizure-onset zone (SOZ), defined as the area responsible of the generation of the seizure and therefore candidate for resection. High-frequency oscillations (HFOs) contained in the iEEG signal have been proposed as biomarker of the SOZ. Their visual identification is a very onerous process and an automated detection tool could be an extremely valuable aid for clinicians, reducing operator-dependent bias, and computational time. In this manuscript, we present the EPINETLAB software, developed as a collection of routines integrated in the EEGLAB framework that aim to provide clinicians with a structured analysis pipeline for HFOs detection and SOZ identification. The tool implements an analysis strategy developed by our group and underwent a preliminary clinical validation that identifies the HFOs area by extracting the statistical properties of HFOs signal and that provides useful information for a topographic characterization of the relationship between clinically defined SOZ and HFO area. Additional functionalities such as inspection of spectral properties of ictal iEEG data and import and analysis of source-space magnetoencephalographic (MEG) data were also included. EPINETLAB was developed with user-friendliness in mind to support clinicians in the identification and quantitative assessment of HFOs in iEEG and source space MEG data and aid the evaluation of the SOZ for pre-surgical assessment.
Two main focal seizure patterns revealed by intracerebral electroencephalographic biomarker analysis
Epilepsia, 2018
Long-term recording with intracerebral electrodes is commonly utilized to identify brain areas responsible for seizure generation (epileptogenic zone) and to tailor therapeutic surgical resections in patients with focal drug-resistant epilepsy. This invasive diagnostic procedure generates a wealth of data that contribute to understanding human epilepsy. We analyze intracerebral signals to identify and classify focal ictal patterns. Methods: We retrospectively analyzed stereo-electroencephalographic (EEG) data in a cohort of patients either cryptogenic (magnetic resonance imaging negative) or presenting with noncongruent anatomoelectroclinical data. A computer-assisted method based on EEG signal analysis in frequency and space domains was applied to 467 seizures recorded in 105 patients submitted to stereo-EEG presurgical monitoring. Results: Two main focal seizure patterns were identified. P-type seizures, typical of neocortex, were observed in 73 patients (69.5%), lasted 22 ± 13 seconds (mean + SD), and were characterized by a sharp-onset/sharp-offset transient superimposed on low-voltage fast activity (126 ± 19 Hz). L-type seizures were observed in 43 patients (40.9%) and consistently involved mesial temporal structures; they lasted longer (93 ± 48 second), started with 116 ± 21 Hz low-voltage fast activity superimposed on a slow potential shift, and terminated with large-amplitude, periodic bursting activity. In 23 patients (21.9%), the L-type seizure was preceded by a P seizure. Spasmlike and unclassifiable EEG seizures were observed in 11.4% of cases. Significance: The proposed computer-assisted approach revealed signal information concealed to visual inspection that contributes to identifying two principal seizure patterns typical of the neocortex and of mesial temporal networks.
Focal epileptiform activity described by a large computerised EEG database
Clinical Neurophysiology, 2007
Objective: To study the age-related topographical tendency of expressing epileptiform activity, and the effect of focal epileptiform activity (FEA) on the general cortical brain activity. Methods: 1647 consecutive routine EEGs containing FEA were visually assessed for FEA location and asymmetry. Background activity was compared with that in normal EEGs from 3268 drug-free outpatient controls. Results: FEA localisation was age-related (p < 0.0005) except for the temporal region (p = 0.22) where FEA was found equally often in the young and the old. The left hemisphere was more prone to FEA (p = 0.018). The left-right asymmetry varied by age (p = 0.013). FEA asymmetry occurred most frequently in EEGs from patients older than 80 years, and least frequent in the age-group 20-39 years. FEA was associated with lower alpha rhythm (AR) frequencies (p = 0.0041) and higher AR amplitudes (p = 0.0023), as well as higher general background activity (GBA) amplitude (p < 0.0005), while GBA frequencies were the same (p = 0.96). Conclusions: Topographical localisation of FEA was age-dependent. There was an overall left dominance, but the side asymmetry was modest and varied by age. FEA was associated with changes in AR and GBA. Significance: The results demonstrate that FEA is associated with cerebral cortical dysfunction also distant from the epileptic focus.