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1.  Leaving tissue associated with infrequent intracranial EEG seizure onsets is compatible with post-operative seizure freedom 
Journal of pediatric epilepsy  2012;1(4):211-219.
Identify seizure onset electrodes that need to be resected for seizure freedom in children undergoing intracranial electroencephalography recording for treatment of medically refractory epilepsy. All children undergoing intracranial electroencephalography subdural grid electrode placement at the Children’s Hospital of Philadelphia from 2002-2008 were asked to enroll. We utilized intraoperative pictures to determine the location of the electrodes and define the resection cavity. A total of 15 patients had surgical fields that allowed for complete identification of the electrodes over the area of resection. Eight of 15 patients were seizure free after a follow up of 1.7 to 8 yr. Only one seizure-free patient had complete resection of all seizure onset associated tissue. Seizure free patients had resection of 64.1% of the seizure onset electrode associated tissue, compared to 35.2% in the not seizure free patients (p=0.05). Resection of tissue associated with infrequent seizure onsets did not appear to be important for seizure freedom. Resecting ≥ 90% of the electrodes from the predominant seizure contacts predicted post-operative seizure freedom (p=0.007). The best predictor of seizure freedom was resecting ≥ 90% of tissue involved in majority of a patient’s seizures. Resection of tissue under infrequent seizure onset electrodes was not necessary for seizure freedom.
PMCID: PMC3930198  PMID: 24563805
Epilepsy; epilepsy surgery; cortical dysplasia; neocortical epilepsy; intracranial electroencephalography
2.  Mapping and mining interictal pathological gamma (30–100 Hz) oscillations with clinical intracranial EEG in patients with epilepsy 
Expert systems with applications  2012;39(8):7355-7370.
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.
PMCID: PMC3480232  PMID: 23105174
Epileptic network; Interictal epileptic discharge; Pathological gamma oscillation; Detection; Mapping; Data-mining
3.  Data mining neocortical high-frequency oscillations in epilepsy and controls 
Brain  2011;134(10):2948-2959.
Transient high-frequency (100–500 Hz) oscillations of the local field potential have been studied extensively in human mesial temporal lobe. Previous studies report that both ripple (100–250 Hz) and fast ripple (250–500 Hz) oscillations are increased in the seizure-onset zone of patients with mesial temporal lobe epilepsy. Comparatively little is known, however, about their spatial distribution with respect to seizure-onset zone in neocortical epilepsy, or their prevalence in normal brain. We present a quantitative analysis of high-frequency oscillations and their rates of occurrence in a group of nine patients with neocortical epilepsy and two control patients with no history of seizures. Oscillations were automatically detected and classified using an unsupervised approach in a data set of unprecedented volume in epilepsy research, over 12 terabytes of continuous long-term micro- and macro-electrode intracranial recordings, without human preprocessing, enabling selection-bias-free estimates of oscillation rates. There are three main results: (i) a cluster of ripple frequency oscillations with median spectral centroid = 137 Hz is increased in the seizure-onset zone more frequently than a cluster of fast ripple frequency oscillations (median spectral centroid = 305 Hz); (ii) we found no difference in the rates of high frequency oscillations in control neocortex and the non-seizure-onset zone neocortex of patients with epilepsy, despite the possibility of different underlying mechanisms of generation; and (iii) while previous studies have demonstrated that oscillations recorded by parenchyma-penetrating micro-electrodes have higher peak 100–500 Hz frequencies than penetrating macro-electrodes, this was not found for the epipial electrodes used here to record from the neocortical surface. We conclude that the relative rate of ripple frequency oscillations is a potential biomarker for epileptic neocortex, but that larger prospective studies correlating high-frequency oscillations rates with seizure-onset zone, resected tissue and surgical outcome are required to determine the true predictive value.
PMCID: PMC3187540  PMID: 21903727
high-frequency oscillations; epilepsy; intracranial EEG
4.  Interictal EEG spikes identify the region of seizure onset in some, but not all pediatric epilepsy patients 
Epilepsia  2009;51(4):592-601.
The role of sharps and spikes, interictal epileptiform discharges (IEDs), in guiding epilepsy surgery in children remains controversial, particularly with intracranial EEG (IEEG). While ictal recording is the mainstay of localizing epileptic networks for surgical resection, current practice dictates removing regions generating frequent IEDs if they are near the ictal onset zone. Indeed, past studies suggest an inconsistent relationship between IED and seizure onset location, though these studies were based upon relatively short EEG epochs.
We employ a previously validated, computerized spike detector, to measure and localize IED activity over prolonged, representative segments of IEEG recorded from 19 children with intractable, mostly extra temporal lobe epilepsy. Approximately 8 hours of IEEG, randomly selected thirty-minute segments of continuous interictal IEEG per patient were analyzed over all intracranial electrode contacts.
When spike frequency was averaged over the 16-time segments, electrodes with the highest mean spike frequency were found to be within the seizure onset region in 11 of 19 patients. There was significant variability between individual 30-minute segments in these patients, indicating that large statistical samples of interictal activity were required for improved localization. Low voltage fast EEG at seizure onset was the only clinical factor predicting IED localization to the seizure onset region.
Our data suggest that automated IED detection over multiple representative samples of IEEG may be of utility in planning epilepsy surgery for children with intractable epilepsy. Further research is required to better determine which patients may benefit from this technique a priori.
PMCID: PMC2907216  PMID: 19780794
Spike density; intracranial EEG; Seizure onset; Pediatric Epilepsy
5.  Human and Automated Detection of High-Frequency Oscillations in Clinical Intracranial EEG Recordings 
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 – 85 Hz, and validate it against human expert electroencephalographers.
We randomly selected 28 3-minute 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.
A total of 1,330 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 Kappa statistics (mean κ = 0.38) demonstrated marginal identification consistency, primarily due to false negative errors.
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.
This algorithm will be useful for analyzing large EEG databases to determine the pathophysiological significance of HFO events in human epileptic networks.
PMCID: PMC2020804  PMID: 17382583
high-frequency oscillation; HFO; intracranial EEG; epilepsy

Results 1-5 (5)