![]() ![]() There are many automated approaches for detecting interictal events, including template-matching, non-linear energy operator (NEO) detection wavelet filtering and line length, and neural networks among others. Together with the laborious nature of manual EEG analysis, this means that the frequency of interictal spiking is not typically quantified for diagnostic purposes in the clinic, though there are special cases, and is underutilized in epilepsy research. But the definition of spike-like events for subsequent manual scoring remains an area of some disagreement among clinicians and researchers. ![]() Thus, the identification and analysis of subclinical, interictal events such as spikes may be important for diagnosis, identification of epileptic foci and understanding the progression of epilepsy. Īdding to the challenge of analyzing EEG for epilepsy is that often convulsive seizure frequency is relatively low, often much less than one/day, making capturing confirmatory seizure events on EEG rare. Manual scoring is labor-intensive and prone to low interobserver agreement which often precludes fine-scale and quantitative analysis of EEG, especially for chronic (>24 hours) recordings commonly employed in clinical and research settings. Most EEG analysis, including the scoring of seizures and interictal epileptiform events, is carried out manually by trained scorers. The electroencephalogram (EEG) is an essential tool for monitoring seizures, diagnosing epilepsy and understanding epileptogenesis. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: Data are available in the BioStudies database ( ) under accession number S-BSST180.įunding: This work was funded in part by the National Institute for Health RO1 NS075366 (MVJ) and the Department of Defense PR161864 (RKM).Ĭompeting interests: The authors have declared that no competing interests exist. Received: AugAccepted: OctoPublished: November 6, 2018Ĭopyright: © 2018 Pfammatter et al. PLoS ONE 13(11):Įditor: Giuseppe Biagini, University of Modena and Reggio Emilia, ITALY Future refinement will allow a better understanding of the definition of interictal spikes in quantitative and unambiguous terms.Ĭitation: Pfammatter JA, Bergstrom RA, Wallace EP, Maganti RK, Jones MV (2018) A predictive epilepsy index based on probabilistic classification of interictal spike waveforms. This analysis is fast, unbiased, and provides information regarding the salience of spike morphologies for disease progression. Importantly, in both data sets, animals that had electrographic seizures also had a high Index. The magnitude of the Index increased over time in a subset of animals and revealed changes in the prevalence of epileptiform (P(kainate) > 0.5) spike morphologies. We applied this method to an out-of-sample dataset to assess epileptiform spike morphologies in five kainate mice monitored for ~1 month. This Index is predictive of whether an animal received an epileptogenic treatment (i.e., kainate), even if a seizure was never observed. ![]() We calculated the odds-ratio of events from kainate- versus saline-treated mice within each cluster, converted these values to probability scores, P(kainate), and calculated an Hourly Epilepsy Index for each animal by summing the probabilities for events where the cluster P(kainate) > 0.5 and dividing the resultant sum by the record duration. We first detected high-amplitude events, then projected event waveforms into Principal Components space and identified clusters of spike morphologies using a Gaussian Mixture Model. We present a probability-based, automated method for the classification and quantification of interictal events, using EEG data from kainate- and saline-injected mice (C57BL/6J background) several weeks post-treatment. Quantification of interictal spikes in EEG may provide insight on epilepsy disease burden, but manual quantification of spikes is time-consuming and subject to bias. ![]()
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