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1.  Dorsolateral Frontal Lobe Epilepsy 
Dorsolateral frontal lobe seizures often present as a diagnostic challenge. The diverse semiologies may not produce lateralizing or localizing signs, and can appear bizarre and suggest psychogenic events. Unfortunately, scalp EEG and MRI are often unsatisfactory. It is not uncommon that these traditional diagnostic studies are either unhelpful or even misleading. In some cases SPECT and PET imaging can be an effective tool to identify the origin of seizures. However, these techniques and other emerging techniques all have limitations, and new approaches are needed to improve source localization.
PMCID: PMC3463872  PMID: 23027094
Frontal lobe epilepsy; EEG
2.  Automated identification of multiple seizure-related and interictal epileptiform event types in the EEG of mice 
Scientific Reports  2013;3:1483.
Visual scoring of murine EEG signals is time-consuming and subject to low inter-observer reproducibility. The Racine scale for behavioral seizure severity does not provide information about interictal or sub-clinical epileptiform activity. An automated algorithm for murine EEG analysis was developed using total signal variation and wavelet decomposition to identify spike, seizure, and other abnormal signal types in single-channel EEG collected from kainic acid-treated mice. The algorithm was validated on multi-channel EEG collected from γ-butyrolacetone-treated mice experiencing absence seizures. The algorithm identified epileptiform activity with high fidelity compared to visual scoring, correctly classifying spikes and seizures with 99% accuracy and 91% precision. The algorithm correctly identifed a spike-wave discharge focus in an absence-type seizure recorded by 36 cortical electrodes. The algorithm provides a reliable and automated method for quantification of multiple classes of epileptiform activity within the murine EEG and is tunable to a variety of event types and seizure categories.
PMCID: PMC3604748  PMID: 23514826
3.  High-frequency oscillations and other electrophysiological biomarkers of epilepsy: clinical studies 
Biomarkers in Medicine  2011;5(5):557-566.
Accurate localization of epileptogenic brain is critical for successful epilepsy surgery. Recent research using wide bandwidth intracranial EEG has demonstrated that interictal high-frequency oscillations are preferentially localized to the brain region generating spontaneous seizures, and are a potential biomarker of epileptogenic brain. The existence of an interictal, electrophysiological biomarker of epileptogenic brain has the potential to significantly advance epilepsy surgery by improving outcomes through improved localization and potentially eliminating the reliance on chronic intracranial EEG monitoring.
PMCID: PMC3254091  PMID: 22003904
epilepsy surgery; epileptogenic zone; fast ripple; high-frequency oscillation; ripple; seizure onset zone; wide bandwidth EEG
4.  Microseizures and the spatiotemporal scales of human partial epilepsy 
Brain  2010;133(9):2789-2797.
Focal seizures appear to start abruptly and unpredictably when recorded from volumes of brain probed by clinical intracranial electroencephalograms. To investigate the spatiotemporal scale of focal epilepsy, wide-bandwidth electrophysiological recordings were obtained using clinical macro- and research microelectrodes in patients with epilepsy and control subjects with intractable facial pain. Seizure-like events not detectable on clinical macroelectrodes were observed on isolated microelectrodes. These ‘microseizures’ were sparsely distributed, more frequent in brain regions that generated seizures, and sporadically evolved into large-scale clinical seizures. Rare microseizures observed in control patients suggest that this phenomenon is ubiquitous, but their density distinguishes normal from epileptic brain. Epileptogenesis may involve the creation of these topographically fractured microdomains and ictogenesis (seizure generation), the dynamics of their interaction and spread.
PMCID: PMC2929333  PMID: 20685804
epilepsy; seizure; intracranial EEG; microseizure; microcircuit; seizure generation; ictogenesis; epileptogenesis
5.  Continuous energy variation during the seizure cycle: towards an on-line accumulated energy 
Increases in accumulated energy on intracranial EEG are associated with oncoming seizures in retrospective studies, supporting the idea that seizures are generated over time. Published seizure prediction methods require comparison to ‘baseline’ data, sleep staging, and selecting seizures that are not clustered closely in time. In this study, we attempt to remove these constraints by using a continuously adapting energy threshold, and to identify stereotyped energy variations through the seizure cycle (inter-, pre-, post- and ictal periods).
Accumulated energy was approximated by using moving averages of signal energy, computed for window lengths of 1 and 20 min, and an adaptive decision threshold. Predictions occurred when energy within the shorter running window exceeded the decision threshold.
Predictions for time horizons of less than 3 h did not achieve statistical significance in the data sets analyzed that had an average inter-seizure interval ranging from 2.9 to 8.6 h. 51.6% of seizures across all patients exhibited stereotyped pre-ictal energy bursting and quiet periods.
Accumulating energy alone is not sufficient for predicting seizures using a 20 min running baseline for comparison. Stereotyped energy patterns through the seizure cycle may provide clues to mechanisms underlying seizure generation.
Energy-based seizure prediction will require fusion of multiple complimentary features and perhaps longer running averages to compensate for post-ictal and sleep-induced energy changes.
PMCID: PMC2941767  PMID: 15721065
Intracranial EEG energy; Interictal and ictal energy; Seizure prediction; Accumulated energy; Average inter-seizure interval
6.  High-frequency oscillations in human temporal lobe: simultaneous microwire and clinical macroelectrode recordings 
Brain : a journal of neurology  2008;131(Pt 4):928-937.
Neuronal oscillations span a wide range of spatial and temporal scales that extend beyond traditional clinical EEG. Recent research suggests that high-frequency oscillations (HFO), in the ripple (80–250Hz) and fast ripple (250–1000Hz) frequency range, may be signatures of epileptogenic brain and involved in the generation of seizures. However, most research investigating HFO in humans comes from microwire recordings, whose relationship to standard clinical intracranial EEG (iEEG) has not been explored. In this study iEEG recordings (DC − 9000Hz) were obtained from human medial temporal lobe using custom depth electrodes containing both microwires and clinical macroelectrodes. Ripple and fast-ripple HFO recorded from both microwires and clinical macroelectrodes were increased in seizure generating brain regions compared to control regions. The distribution of HFO frequencies recorded from the macroelectrodes was concentrated in the ripple frequency range, compared to a broad distribution of HFO frequencies recorded from microwires. The average frequency of ripple HFO recorded from macroelectrodes was lower than that recorded from microwires (143.3 ± 49.3 Hz versus 116.3 ± 38.4, Wilcoxon rank sum P<0.0001). Fast-ripple HFO were most often recorded on a single microwire, supporting the hypothesis that fast-ripple HFO are primarily generated by highly localized, sub-millimeter scale neuronal assemblies that are most effectively sampled by microwire electrodes. Future research will address the clinical utility of these recordings for localizing epileptogenic networks and understanding seizure generation.
PMCID: PMC2760070  PMID: 18263625
high-frequency oscillations; ripple; fast ripple; intracranial EEG; epilepsy
7.  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 1-7 (7)