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Abstract: 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 85Hz, 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 κ 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. [Copyright &y& Elsevier]

Abstract We present a novel strategy for approximate furthest neighbor search that selects a set of candidate points using the data distribution. This strategy leads to an algorithm, which we call DrusillaSelect, that is able to outperform existing approximate furthest neighbor strategies. Our strategy is motivated by a study of the behavior of the furthest neighbor search problem, which has significantly different structure than the nearest neighbor search problem, and can be understood with the help of an information-theoretic hardness measure that we introduce. We also present a variant of the algorithm that gives an absolute approximation guarantee; under some assumptions, the guaranteed approximation can be achieved in provably less time than brute-force search. Performance studies indicate that DrusillaSelect can achieve comparable levels of approximation to other algorithms, even on the hardest datasets, while giving up to an order of magnitude speedup. An implementation is available in the mlpack machine learning library (found at http://www.mlpack.org). [ABSTRACT FROM AUTHOR]

Abstract: 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. [Copyright &y& Elsevier]