Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers

Abstract

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic–ischemic encephalopathy (HIE). Studies of neuroprotection in animal models consistently suggest that treatment must be started as early as possible in the first 6 h after hypoxia–ischemia (HI), the so-called latent phase before secondary deterioration, to improve outcomes. We have shown in preterm sheep that EEG biomarkers of injury, in the form of high-frequency micro-scale spike transients, develop and evolve in this critical latent phase after severe asphyxia. Real-time automatic identification of such events is important for the early and accurate detection of HI injury, so that the right treatment can be implemented at the right time. We have previously reported successful strategies for accurate identification of EEG patterns after HI. In this study, we report an alternative high-performance approach based on the fusion of spectral Fourier analysis and Type-I fuzzy classifiers (FFT-Type-I-FLC). We assessed its performance in over 2520 min of latent phase EEG recordings from seven asphyxiated in utero preterm fetal sheep exposed to a range of different occlusion periods. The FFT-Type-I-FLC classifier demonstrated 98.9 ± 1.0% accuracy for identification of high-frequency spike transients in the gamma frequency band (namely 80–120 Hz) post-HI. The spectral-based approach (FFT-Type-I-FLC classifier) has similar accuracy to our previous reverse biorthogonal wavelets rbio2.8 basis function and type-1 fuzzy classifier (rbio-WT-Type-1-FLC), providing competitive performance (within the margin of error: 0.89%), but it is computationally simpler and would be readily adapted to identify other potentially relevant EEG waveforms.

Rh-CSF1 attenuates neuroinflammation via the CSF1R/PLCG2/PKCε pathway in a rat model of neonatal HIE

“…Hypoxic-ischemic encephalopathy (HIE) is a lifethreatening cerebrovascular disease which is associated with high morbidity and mortality in infants, resulting in life-long neurodevelopmental impairment, such as cerebral palsy, mental retardation, cognitive deficits, epilepsy, and learning and visual impairments [1][2][3]. The incidence of HIE in live births is 0.1-0.8% in developed countries, but may vary, with rates as high as 2.6% in undeveloped countries [4].…”

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

“…[7,8] We have previously shown, in preterm fetal sheep EEG recordings, that epileptiform transients such as sharp waves and high-frequency microscale spike transients in the gamma frequency band (80-120 Hz), superimposed on a suppressed EEG background, are key EEG waveforms and are predictive of neural outcome during the first 6 h of recovery from an HI insult (see Figure 1). [6,[8][9][10][11] Similar EEG transients are also seen clinically and are associated with adverse neurological outcomes. [12] Current studies on the application of automated convolutional neural network (CNN)-based strategies in clinical neonatal EEG studies have primarily focused on developing consistently interpretable determinations of seizures and other EEG features, but not microseizures or post-HI EEG biomarkers.…”

“…[13][14][15][16][17] Our team has previously shown various successful fusion strategies for automatic detection of post-HI microscale EEG transients. [8,10] Further, we have undertaken preliminary examination of the wavelet-scalogram (WS) CNN structure to robustly identify post-HI sharp waves from an EEG background and artifact during the latent phase after the HI insult with 95.34% accuracy. [17] In the current study, we evaluated, for the first time, how application of spectrally rich WSs to our post-HI experimental EEG data could be used as inputs to a pattern classifier in the form of a 17-layer deep 2D-CNN for the accurate identification of microscale gamma spike transients in the latent phase.…”

“…We have previously described the appearance of microscale gamma spike transients after an HI insult. [8,10,11] Briefly, they are EEG epileptiform waveforms characterized as events with a pointed peak, with amplitude of >20 μV and duration of <12.5 ms (equivalent to the frequency range of >80 Hz) (see Figure 1). To evaluate the performance of the classifier and for consistency between all methods individual microscale gamma spikes (i.e., excluding spike complexes) with an amplitude of at least 14 μV were assessed for automatic classification against manual quantifications.…”

“…The superior compatibility of the reverse biorthogonal wavelet rbio2.8 basis function for gamma spike detection in comparison to other wavelet basis functions would allow us to extract minimal features from a spike transient to be used in a 2D-CNN. [10] Here, for the first time, we also show an approach that, instead of the full-range spectral features (scalograms) as in the WS-CNN approach, only extracts the spectrally dominant features of the raw ECoG epochs, using wavelet and Fourier spectrums (WF), to form robust input matrix sets. In this method, the CWT coefficients of each zero-meaned ECoG segment (72 Â 1) using rbio2.8 at scale 7 ( Figure S3E-H of Appendix F, Supporting Information) as well as the inverse Fourier transform time-series of the data (IFFT: spectral components within 80-120 Hz preserved- Figure S3I-L, Supporting Information) along with the original raw ECoG segment ( Figure S3A-D, Supporting Information), were combined to form robust 3D input-matrix sets of size 72 Â 3 Â 1 ( Figure S3M,N, Supporting Information) to be fed directly into the deep 2D-CNN classifier (WF-CNN).…”