Automatically Identified Micro-scale Sharp-wave Transients in the Early-Latent Phase of Hypoxic-Ischemic EEG from Preterm Fetal Sheep Reveal Timing Relationship to Subcortical Neuronal Survival

2D Wavelet-Scalogram Deep-Learning for Seizures Pattern Identification in the Post-Hypoxic-Ischemic EEG of Preterm Fetal Sheep

Neonatal seizures after an hypoxic-ischemic (HI) event in preterm newborns can contribute to neural injury and cause impaired brain development. Preterm neonatal seizures are often not detected or their occurrence underestimated. Therefore, there is a need to improve knowledge about preterm seizures that can help establish diagnostic tools for accurate identification of seizures and for determining morphological differences. We have previously shown the superior utility of deep-learning algorithms for the accurate identification and quantification of post-HI microscale epileptiform transients (e.g., gamma spikes and sharp waves) in preterm fetal sheep models; before the irreversible secondary phase of cerebral energy failure starts by the bursts of high-amplitude stereotypic evolving seizures (HAS) in the signal. We have previously developed successful deep-learning algorithms that accurately identify and quantify the micro-scale transients, during the latent phase. Building up on our deep-learning strategies, this work introduces a real-time deep-learning-based pattern fusion approach to identify HAS in the 256Hz sampled post-HI data from our preterm fetuses. Here, for the first time, we propose a 17-layer deep convolutional neural network (CNN) classifier fed with 2D wavelet-scalogram (WS) images of the EEG patterns for accurate seizure identification. The WS-CNN classifier was cross-validated over 1812 manually annotated EEG segments during ~6 to 48 hours post-HI recordings. The classifier accurately recognized HAS patterns with 97.19% overall accuracy (AUC = 0.96).Clinical relevance-The promising results from this preliminary work indicate the ability of the proposed WS-CNN pattern classifier to identify HI-related seizures in the neonatal preterm brain using 256Hz EEG; the frequency commonly used clinically for data collection.

A novel approach to segment cortical neurons in histological images of the near-term fetal sheep brain model

Oxygen deprivation (hypoxia) and reduced blood supply (ischemia) can occur before, during or shortly after birth and can result in death, brain damage and long-term disability. Assessing neuronal survival after hypoxia-ischemia in the near-term fetal sheep brain model is essential for the development of novel treatment strategies. As manual quantification of neurons in histological images varies between different assessors and is extremely time-consuming, automation of the process is needed and has not been currently achieved. To achieve automation, successfully segmenting the neurons from the background is very important. Due to presence of densely populated overlapping cells and with no prior information of shapes and sizes, the segmentation of neurons from the image is complex. Initially, we segmented the RGB images by using K-means clustering to primarily segment the neurons from the background based on their colour value, a distance transform for seed detection and watershed method for separating overlapping objects. However, this resulted in unsatisfactory sensitivity and performance due to over-segmentation if we use the RGB image directly. In this paper, we propose a semi-automated modified approach to segment neurons that tackles the over-segmentation issue that we encountered. Initially, we separated the red, green and blue colour channel information from the RGB image. We determined that by applying the same segmentation method first to the blue channel image, then by performing segmentation on the green channel for the neurons that remain unsegmented from the blue channel segmentation and finally by performing segmentation on red channel for neurons that were still unsegmented from the green channel segmentation, improved performance results could be achieved. The modified approach increased performance for the healthy and ischemic animal images from 89.7% to 98.08% and from 94.36% to 98.06% respectively as compared to using RGB image directly.

2D Wavelet Scalogram Training of Deep Convolutional Neural Network for Automatic Identification of Micro-Scale Sharp Wave Biomarkers in the Hypoxic-Ischemic EEG of Preterm Sheep

We have recently demonstrated that micro-scale Sharp waves in the first few hours EEG of asphyxiated preterm fetal sheep models are the reliable prognostic biomarkers for Hypoxic-Ischemic Encephalopathy (HIE). Higher number of sharp waves within the first 2 hours from a hypoxic insult is shown to be significantly correlated to subcortical neuronal survival in caudate nucleus of striatum. Cerebral therapeutic hypothermia is also shown to be optimally neuroprotective only if initiated as soon as possible during a short window of opportunity within the first 2-3 hours of HI insult, called the latent phase. Therefore there is an urgent necessity for reliable automated algorithms to robustly identify such biomarkers to help early diagnosis of HIE, in real time at birth, before the optimal window of opportunity for treatment is missed.We have previously introduced successful automated signal processing strategies based on the fusion of wavelet and fuzzy techniques, for real-time identification and quantification of sharp waves along a profoundly suppressed EEG/ECoG background, post HI-insult, during the latent phase of sheep models. This work, in particular, for the first time represents a novel online fusion strategy based on the combination of a deep Convolutional Neural Network (CNN) in conjunction with Wavelet Scalogram (WS) for the real-time identification and classification of micro-scale sharp wave biomarkers within the 1024Hz high resolution ECoG recordings as well as the down-sampled 256Hz signals, from in utero preterm fetal sheep. The WS-CNN classifier highlights ability in the identification of HI sharp waves with remarkable high accuracies of 95.34% for 1024Hz and 94.62% for 256Hz data tested over one hour HI ECoG within the most important interval during the first 2 hours of the latent phase, where experiments have suggested hypothermia is optimally effective.

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

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.

Applications of advanced signal processing and machine learning in the neonatal hypoxic-ischemic electroencephalogram

Perinatal hypoxic-ischemic-encephalopathy significantly contributes to neonatal death and life-long disability such as cerebral palsy. Advances in signal processing and machine learning have provided the research community with an opportunity to develop automated real-time identification techniques to detect the signs of hypoxic-ischemic-encephalopathy in larger electroencephalography/amplitude-integrated electroencephalography data sets more easily. This review details the recent achievements, performed by a number of prominent research groups across the world, in the automatic identification and classification of hypoxic-ischemic epileptiform neonatal seizures using advanced signal processing and machine learning techniques. This review also addresses the clinical challenges that current automated techniques face in order to be fully utilized by clinicians, and highlights the importance of upgrading the current clinical bedside sampling frequencies to higher sampling rates in order to provide better hypoxic-ischemic biomarker detection frameworks. Additionally, the article highlights that current clinical automated epileptiform detection strategies for human neonates have been only concerned with seizure detection after the therapeutic latent phase of injury. Whereas recent animal studies have demonstrated that the latent phase of opportunity is critically important for early diagnosis of hypoxic-ischemic-encephalopathy electroencephalography biomarkers and although difficult, detection strategies could utilize biomarkers in the latent phase to also predict the onset of future seizures.

Electroencephalogram studies of hypoxic ischemia in fetal and neonatal animal models

Alongside clinical achievements, experiments conducted on animal models (including primate or non-primate) have been effective in the understanding of various pathophysiological aspects of perinatal hypoxic/ischemic encephalopathy (HIE). Due to the reasonably fair degree of flexibility with experiments, most of the research around HIE in the literature has been largely concerned with the neurodevelopmental outcome or how the frequency and duration of HI seizures could relate to the severity of perinatal brain injury, following HI insult. This survey concentrates on how EEG experimental studies using asphyxiated animal models (in rodents, piglets, sheep and non-human primate monkeys) provide a unique opportunity to examine from the exact time of HI event to help gain insights into HIE where human studies become difficult.