A sparse representation strategy to eliminate pseudo-HFO events from intracranial EEG for seizure onset zone localization
العنوان: | A sparse representation strategy to eliminate pseudo-HFO events from intracranial EEG for seizure onset zone localization |
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المؤلفون: | Behrang Fazli Besheli, Zhiyi Sha, Jay R Gavvala, Candan Gurses, Sacit Karamursel, Michael M Quach, Daniel J Curry, Sameer A Sheth, David J Francis, Thomas R Henry, Nuri F Ince |
المصدر: | J Neural Eng |
بيانات النشر: | IOP Publishing, 2022. |
سنة النشر: | 2022 |
مصطلحات موضوعية: | Cellular and Molecular Neuroscience, Seizures, Biomedical Engineering, Humans, Reproducibility of Results, Electroencephalography, Electrocorticography, Artifacts, Article |
الوصف: | Objective. High-frequency oscillations (HFOs) are considered a biomarker of the epileptogenic zone in intracranial EEG recordings. However, automated HFO detectors confound true oscillations with spurious events caused by the presence of artifacts. Approach. We hypothesized that, unlike pseudo-HFOs with sharp transients or arbitrary shapes, real HFOs have a signal characteristic that can be represented using a small number of oscillatory bases. Based on this hypothesis using a sparse representation framework, this study introduces a new classification approach to distinguish true HFOs from the pseudo-events that mislead seizure onset zone (SOZ) localization. Moreover, we further classified the HFOs into ripples and fast ripples by introducing an adaptive reconstruction scheme using sparse representation. By visualizing the raw waveforms and time-frequency representation of events recorded from 16 patients, three experts labeled 6400 candidate events that passed an initial amplitude-threshold-based HFO detector. We formed a redundant analytical multiscale dictionary built from smooth oscillatory Gabor atoms and represented each event with orthogonal matching pursuit by using a small number of dictionary elements. We used the approximation error and residual signal at each iteration to extract features that can distinguish the HFOs from any type of artifact regardless of their corresponding source. We validated our model on sixteen subjects with thirty minutes of continuous interictal intracranial EEG recording from each. Main results. We showed that the accuracy of SOZ detection after applying our method was significantly improved. In particular, we achieved a 96.65% classification accuracy in labeled events and a 17.57% improvement in SOZ detection on continuous data. Our sparse representation framework can also distinguish between ripples and fast ripples. Significance. We show that by using a sparse representation approach we can remove the pseudo-HFOs from the pool of events and improve the reliability of detected HFOs in large data sets and minimize manual artifact elimination. |
تدمد: | 1741-2552 1741-2560 |
URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e56b78c1bdeb77997c78a4e96a6b1c87 https://doi.org/10.1088/1741-2552/ac8766 |
حقوق: | OPEN |
رقم الأكسشن: | edsair.doi.dedup.....e56b78c1bdeb77997c78a4e96a6b1c87 |
قاعدة البيانات: | OpenAIRE |
تدمد: | 17412552 17412560 |
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