Using type-2 fuzzy logic systems for spike detection in the hypoxic ischemic EEG of the preterm fetal sheep

Perinatal hypoxia is a major cause of brain injury in preterm babies. Thus, neuro-protective treatments play a pivotal role during the first 6-8 hours post hypoxic-ischemic insult. However, at present it is not possible to determine which infants are suffering from hypoxic ischemia. Recent investigations suggest that there are high frequency micro-scale transients exist in the first 6-8 hours of a hypoxic ischemic EEG which could be utilized as the useful benchmarks for the prediction of hypoxia. Type-2 Fuzzy Logic Systems (Type-2 FLS) have the capability to handle inherent uncertainties in nonlinear signals. This paper describes the application of a Type-2 FLS to detect spikes in the preterm fetal sheep electroencephalogram (EEG) after asphyxia in utero. The Type-2 FLS differentiates each detected event in terms of its spikiness and specifies the potential events based on their degree of similarity to an EEG expert definition of a standard spike. An adaptive thresholding method has been employed in order to increase the spike detection ability of the purposed system. The sensitivity and selectivity verify enhanced performance of the Type-2 FLS for spike detection in fetal sheep EEG signals with a 98.1% and 93.7% respectively which are significantly improved in comparison to our previous methods.