A novel implanted device to wirelessly record and analyze continuous intracranial canine EEG.

We present results from continuous intracranial electroencephalographic (iEEG) monitoring in 6 dogs with naturally occurring epilepsy, a disorder similar to the human condition in its clinical presentation, epidemiology, electrophysiology and response to therapy. Recordings were obtained using a novel implantable device wirelessly linked to an external, portable real-time processing unit. We demonstrate previously uncharacterized intracranial seizure onset patterns in these animals that are strikingly similar in appearance to human partial onset epilepsy. We propose: (1) canine epilepsy as an appropriate model for testing human antiepileptic devices and new approaches to epilepsy surgery, and (2) this new technology as a versatile platform for evaluating seizures and response to therapy in the natural, ambulatory setting.

High-frequency oscillations in human temporal lobe: simultaneous microwire and clinical macroelectrode recordings.

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 (80-250 Hz) and fast ripple (250-1000 Hz) 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 - 9000 Hz) 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.

Comparison of novel computer detectors and human performance for spike detection in intracranial EEG.

OBJECTIVE: Interictal spikes in intracranial EEG (iEEG) may correlate with epileptogenic cortex, but review of interictal iEEG is labor intensive. Accurate automated spike detectors are necessary for understanding the role of spikes in epileptogenesis. METHODS: The sensitivity, accuracy and reproducibility of three automated iEEG spike detectors were compared against two human EEG readers using iEEG segments from eight patients. A consensus set of detections was generated for detector calibration. Spike verification was calculated after both human EEG readers independently reviewed all detections. RESULTS: Humans and two of the three automated detectors demonstrated comparable accuracy. In four patients, automated spike detection sensitivity was >70% and accuracy was >50%. In the remaining four patients, EEG background morphology resulted in poorer performance. Blinded human verification accuracy was 76.7+/-6.6% for computer-detected spikes, and 84.5+/-4.1% for human-detected spikes. CONCLUSIONS: Automated iEEG spike detectors perform comparably to humans, but sensitivity and accuracy are patient dependent. Humans verified the majority of computer-detected spikes. SIGNIFICANCE: In some patients automated detectors may be used for mapping spike occurrences in epileptic networks. This may reveal associations between spike distribution, seizure onset, and pathology.

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 to 85 Hz, and validate it against human expert electroencephalographers. METHODS: We randomly selected 28 3-min 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 1330 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 kappa=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.

A stochastic framework for evaluating seizure prediction algorithms using hidden Markov models.

Responsive, implantable stimulation devices to treat epilepsy are now in clinical trials. New evidence suggests that these devices may be more effective when they deliver therapy before seizure onset. Despite years of effort, prospective seizure prediction, which could improve device performance, remains elusive. In large part, this is explained by lack of agreement on a statistical framework for modeling seizure generation and a method for validating algorithm performance. We present a novel stochastic framework based on a three-state hidden Markov model (HMM) (representing interictal, preictal, and seizure states) with the feature that periods of increased seizure probability can transition back to the interictal state. This notion reflects clinical experience and may enhance interpretation of published seizure prediction studies. Our model accommodates clipped EEG segments and formalizes intuitive notions regarding statistical validation. We derive equations for type I and type II errors as a function of the number of seizures, duration of interictal data, and prediction horizon length and we demonstrate the model's utility with a novel seizure detection algorithm that appeared to predicted seizure onset. We propose this framework as a vital tool for designing and validating prediction algorithms and for facilitating collaborative research in this area.