Advanced Deep Learning Spectroscopy of Scalogram Infused CNN Classifiers for Robust Identification of Post‐Hypoxic Epileptiform EEG Spikes

Abstract

There is a lack of reliable prognostic biomarkers for hypoxic‐ischemic (HI) brain injury in preterm infants. Herein, spectrally detailed wavelet scalograms (WSs), derived from the 1024 Hz sampled electroencephalograms (EEG) of preterm fetal sheep after HI (n = 7), are infused into a high‐performance deep convolutional neural network (CNN) pattern classifier to identify high‐frequency spike transient biomarkers. The deep WS‐CNN pattern classifier identifies EEG spikes with remarkable accuracy of 99.81 = 0.15% (area under curve, AUC = 1.000), cross‐validated across 5010 EEG waveforms, during the first 6 h post‐HI (42 h total), an important clinical period for diagnosis of HI brain injury. Further, a feature‐fusion strategy is introduced to extract the spectrally dominant features of the raw EEG epochs to form robust 3D input matrix sets to be infused into the deep 2D‐CNNs for pattern classification. The results show that the proposed WS‐CNN approach is less sensitive to the potential morphological variations of spikes across all subjects compared to other deep CNNs and spectral‐fuzzy classifiers, allowing the user to flexibly choose an approach depending on their computational requirements. Collectively, the data provide a reliable framework that could help support well‐timed diagnosis of at‐risk neonates in clinical practice.