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1.  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.
doi:10.1016/j.eswa.2012.01.071
PMCID: PMC3480232  PMID: 23105174
Epileptic network; Interictal epileptic discharge; Pathological gamma oscillation; Detection; Mapping; Data-mining
2.  Human and Automated Detection of High-Frequency Oscillations in Clinical Intracranial EEG Recordings 
Objective
Recent studies indicate that pathologic high-frequency oscillations (HFOs) are signatures of epileptogenic brain. Automated tools are required to characterize these events. We present a new algorithm tuned to detect HFOs from 30 – 85 Hz, and validate it against human expert electroencephalographers.
Methods
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.
Results
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.
Conclusions
We present an HFO detector that improves upon existing algorithms, and performs as well as human experts on our test data set. Validation of detector performance must be compared to more than one expert because of interrater variability.
Significance
This algorithm will be useful for analyzing large EEG databases to determine the pathophysiological significance of HFO events in human epileptic networks.
doi:10.1016/j.clinph.2006.12.019
PMCID: PMC2020804  PMID: 17382583
high-frequency oscillation; HFO; intracranial EEG; epilepsy

Results 1-2 (2)