title: Robust Wavelet Stabilized 'Footprints of Uncertainty' for Fuzzy System Classifiers to Automatically Detect Sharp Waves in the EEG after Hypoxia Ischemia.
abstract: Currently, there are no developed methods to detect sharp wave transients that exist in the latent phase after hypoxia-ischemia (HI) in the electroencephalogram (EEG) in order to determine if these micro-scale transients are potential biomarkers of HI. A major issue with sharp waves in the HI-EEG is that they possess a large variability in their sharp wave profile making it difficult to build a compact 'footprint of uncertainty' (FOU) required for ideal performance of a Type-2 fuzzy logic system (FLS) classifier. In this paper, we develop a novel computational EEG analysis method to robustly detect sharp waves using over 30[Formula: see text]h of post occlusion HI-EEG from an equivalent, in utero, preterm fetal sheep model cohort. We demonstrate that initial wavelet transform (WT) of the sharp waves stabilizes the variation in their profile and thus permits a highly compact FOU to be built, hence, optimizing the performance of a Type-2 FLS. We demonstrate that this method leads to higher overall performance of [Formula: see text] for the clinical [Formula: see text] sampled EEG and [Formula: see text] for the high resolution [Formula: see text] sampled EEG that is improved upon over conventional standard wavelet [Formula: see text] and [Formula: see text], respectively, and fuzzy approaches [Formula: see text] and [Formula: see text], respectively, when performed in isolation.
authors.affiliation: 1 Department of Engineering Science, The University of Auckland, Auckland, New Zealand.
authors.forename: Hamid
authors.initials: H
authors.lastname: Abbasi
authors.affiliation: 2 Department of Physiology, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand.
authors.forename: Laura
authors.initials: L
authors.lastname: Bennet
authors.affiliation: 2 Department of Physiology, Faculty of Medical and Health Sciences, The University of Auckland, Auckland, New Zealand.
authors.forename: Alistair J
authors.initials: AJ
authors.lastname: Gunn
authors.affiliation: 1 Department of Engineering Science, The University of Auckland, Auckland, New Zealand.
authors.forename: Charles P
authors.initials: CP
authors.lastname: Unsworth
mesh_terms: D000818:Animals; D002545:Brain Ischemia / Q000000981:diagnostic imaging* / Q000503:physiopathology; D015331:Cohort Studies; D004195:Disease Models, Animal; D004569:Electroencephalography / Q000379:methods*; D005260:Female; D005311:Fetal Hypoxia / Q000000981:diagnostic imaging* / Q000503:physiopathology; D017143:Fuzzy Logic; D000069550:Machine Learning; D011247:Pregnancy; D011296:Prenatal Diagnosis / Q000379:methods*; D012680:Sensitivity and Specificity; D034561:Sheep, Domestic; D035501:Uncertainty; D058067:Wavelet Analysis
keywords: EEG; Type-2 fuzzy; automatic detection; high frequency micro-scale seizures; hypoxic-ischemic encephalopathy (HIE); machine learning; sharp wave detection; wavelet transform