Protecting the Future: Neonatal Seizure Detection with Spatial-Temporal Modeling

A timely detection of seizures for newborn infants with electroencephalogram (EEG) has been a common yet life-saving practice in the Neonatal Intensive Care Unit (NICU). However, it requires great human efforts for real-time monitoring, which calls for automated solutions to neonatal seizure detection. Moreover, the current automated methods focusing on adult epilepsy monitoring often fail due to (i) dynamic seizure onset location in human brains; (ii) different montages on neonates and (iii) huge distribution shift among different subjects. In this paper, we propose a deep learning framework, namely STATENet, to address the exclusive challenges with exquisite designs at the temporal, spatial and model levels. The experiments over the real-world large-scale neonatal EEG dataset illustrate that our framework achieves significantly better seizure detection performance.Comment: Accepted in IEEE International Conference on Systems, Man, and Cybernetics (SMC) 202

Automatic Detection of Epileptic Seizures in Neonatal Intensive Care Units through EEG, ECG and Video Recordings: A Survey

In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely, effective and efficient clinical intervention. The continuous video electroencephalogram (v-EEG) is the gold standard for monitoring neonatal seizures, but it requires specialized equipment and expert staff available 24/24h. The purpose of this study is to present an overview of the main Neonatal Seizure Detection (NSD) systems developed during the last ten years that implement Artificial Intelligence techniques to detect and report the temporal occurrence of neonatal seizures. Expert systems based on the analysis of EEG, ECG and video recordings are investigated, and their usefulness as support tools for the medical staff in detecting and diagnosing neonatal seizures in NICUs is evaluated. EEG-based NSD systems show better performance than systems based on other signals. Recently ECG analysis, particularly the related HRV analysis, seems to be a promising marker of brain damage. Moreover, video analysis could be helpful to identify inconspicuous but pathological movements. This study highlights possible future developments of the NSD systems: a multimodal approach that exploits and combines the results of the EEG, ECG and video approaches and a system able to automatically characterize etiologies might provide additional support to clinicians in seizures diagnosis

Toward a personalized real-time diagnosis in neonatal seizure detection

The problem of creating a personalized seizure detection algorithm for newborns is tackled in this paper. A probabilistic framework for semi-supervised adaptation of a generic patient-independent neonatal seizure detector is proposed. A system that is based on a combination of patient-adaptive (generative) and patient-independent (discriminative) classifiers is designed and evaluated on a large database of unedited continuous multichannel neonatal EEG recordings of over 800 h in duration. It is shown that an improvement in the detection of neonatal seizures over the course of long EEG recordings is achievable with on-the-fly incorporation of patient-specific EEG characteristics. In the clinical setting, the employment of the developed system will maintain a seizure detection rate at 70% while halving the number of false detections per hour, from 0.4 to 0.2 FD/h. This is the first study to propose the use of online adaptation without clinical labels, to build a personalized diagnostic system for the detection of neonatal seizures

Ensemble Learning Using Individual Neonatal Data for Seizure Detection

Objective: Sharing medical data between institutions is difficult in practice due to data protection laws and official procedures within institutions. Therefore, most existing algorithms are trained on relatively small electroencephalogram (EEG) data sets which is likely to be detrimental to prediction accuracy. In this work, we simulate a case when the data can not be shared by splitting the publicly available data set into disjoint sets representing data in individual institutions. Methods and procedures: We propose to train a (local) detector in each institution and aggregate their individual predictions into one final prediction. Four aggregation schemes are compared, namely, the majority vote, the mean, the weighted mean and the Dawid-Skene method. The method was validated on an independent data set using only a subset of EEG channels. Results: The ensemble reaches accuracy comparable to a single detector trained on all the data when sufficient amount of data is available in each institution. Conclusion: The weighted mean aggregation scheme showed best performance, it was only marginally outperformed by the Dawid-Skene method when local detectors approach performance of a single detector trained on all available data. Clinical impact: Ensemble learning allows training of reliable algorithms for neonatal EEG analysis without a need to share the potentially sensitive EEG data between institutions.Peer reviewe

Towards developing a reliable medical device for automated epileptic seizure detection in the ICU

Abstract. Epilepsy is a prevalent neurological disorder that affects millions of people globally, and its diagnosis typically involves laborious manual inspection of electroencephalography (EEG) data. Automated detection of epileptic seizures in EEG signals could potentially improve diagnostic accuracy and reduce diagnosis time, but there should be special attention to the number of false alarms to reduce unnecessary treatments and costs. This research presents a study on the use of machine learning techniques for EEG seizure detection with the aim of investigating the effectiveness of different algorithms in terms of high sensitivity and low false alarm rates for feature extraction, selection, pre-processing, classification, and post-processing in designing a medical device for detecting seizure activity in EEG data. The current state-of-the-art methods which are validated clinically using large amounts of data are introduced. The study focuses on finding potential machine learning methods, considering KNN, SVM, decision trees and, Random forests, and compares their performance on the task of seizure detection using features introduced in the literature. Also using ensemble methods namely, bootstrapping and majority voting techniques we achieved a sensitivity of 0.80 and FAR/h of 2.10, accuracy of 97.1% and specificity of 98.2%. Overall, the findings of this study can be useful for developing more accurate and efficient algorithms for EEG seizure detection medical device, which can contribute to the early diagnosis and treatment of epilepsy in the intensive care unit for critically ill patients

Intelligent monitoring and interpretation of preterm physiological signals using machine learning

Every year, more than one in ten babies are born prematurely. In Ireland of the 70000 babies delivered every year, 4500 are born too early. Premature babies are at a higher risk of complications, which may lead to both short-term and long-term adverse health outcomes. The neonatal population is especially vulnerable and a delay in the identification of medical conditions, as well as delays in the initiating the correct treatment, may be fatal. After birth, preterms are admitted to the neonatal intensive care unit (NICU), where a continuous flow of information in the form of physiological signals is available. Physiological signals can assist clinicians in decision making related to the diagnosis and treatment of various diseases. This information, however, can be highly complex, and usually requires expert analysis which may not be available at all times. The work conducted in this thesis develops a decision support systems for the intelligent monitoring of preterms in the NICU. This will allow for an accurate estimation of the current health status of the preterm neonate as well as the prediction of possible long-term complications. This thesis is comprised of three main work packages (WP), each addressing health complication of preterm on three different stages of life. At the first 12 hours of life the health status is quantified using the clinical risk index for babies (CRIB). This is followed by the assessment of the preterm’s well-being at discharge from the NICU using the clinical course score (CCS). Finally, the long-term neurodevelopmental follow-up is assessed using the Bayley III scales of development at two years. This is schematically represented in Figure 1 along with the main findings and contributions. Low blood pressure (BP) or hypotension is a recognised problem in preterm infants particularly during the first 72 hours of life. Hypotension may cause decreased cerebral perfusion, resulting in deprived oxygen delivery to the brain. Deciding when and whether to treat hypotension relies on our understanding of the relation between BP, oxygenation and brain activity. The electroencephalogram (EEG) is the most commonly used technology to assess the ‘brain health’ of a newborn. The first WP investigates the relationship between short-term dynamics in BP and EEG energy in the preterm on a large dataset of continuous multi-channel unedited EEG recordings in the context of the health status measured by the CRIB score. The obtained results indicate that a higher risk of mortality for the preterm is associated with a lower level of nonlinear interaction between EEG and BP. The level of coupling between these two systems can potentially serve as an additional source of information when deciding whether or not to intervene in the preterm. The electrocardiogram (ECG) is also routinely recorded in preterm infants. Analysis of heart rate variability (HRV) provides a non-invasive assessment of both the sympathetic and parasympathetic control of the heart rate. A novel automated objective decision support tool for the prediction of the short-term outcome (CCS) in preterm neonates who may have low BP is proposed in the second WP. Combining multiple HRV features extracted during hypotensive episodes, the classifier achieved an AUC of 0.97 for the task of short-term outcome prediction, using a leave-one-patient-out performance assessment. The developed system is based on the boosted decision tree classifier and allows for the continuous monitoring of the preterm. The proposed system is validated on a large clinically collected dataset of multimodal recordings from preterm neonates. If the correct treatment is initiated promptly after diagnosis, it can potentially improve the neurodevelopmental outcome of the preterm infant. The third WP presents a pilot study investigating the predictive capability of the early EEG recorded at discharge from the NICU with respect to the 2-year neurodevelopmental outcome using machine learning techniques. Two methods are used: 1) classical feature-based classifier, and 2) end-to-end deep learning. This is a fundamental study in this area, especially in the context of applying end-to-end learning to the preterm EEG for the problem of long-term outcome prediction. It is shown that for the available labelled dataset of 37 preterm neonates, the classical feature-based approach outperformed the end-to-end deep learning technique. A discussion of the obtained result as well as a section highlighting the possible limitations and areas that need to be investigated in the future are provided

Hilbert-Huang Transform: biosignal analysis and practical implementation

Any system, however trivial, is subjected to data analysis on the signals it produces. Over the last 50 years the influx of new techniques and expansions of older ones have allowed a number of new applications, in a variety of fields, to be analysed and to some degree understood. One of the industries that is benefiting from this growth is the medical field and has been further progressed with the growth of interdisciplinary collaboration. From a signal processing perspective, the challenge comes from the complex and sometimes chaotic nature of the signals that we measure from the body, such as those from the brain and to some degree the heart. In this work we will make a contribution to dealing with such systems, in the form of a recent time-frequency data analysis method, the Hilbert-Huang Transform (HHT), and extensions to it. This thesis presents an analysis of the state of the art in seizure and heart arrhythmia detection and prediction methods. We then present a novel real-time implementation of the algorithm both in software and hardware and the motivations for doing so. First, we present our software implementation, encompassing realtime capabilities and identifying elements that need to be considered for practical use. We then translated this software into hardware to aid real-time implementation and integration. With these implementations in place we apply the HHT method to the topic of epilepsy (seizures) and additionally make contributions to heart arrhythmias and neonate brain dynamics. We use the HHT and some additional algorithms to quantify features associated with each application for detection and prediction. We also quantify significance of activity in such a way as to merge prediction and detection into one framework. Finally, we assess the real-time capabilities of our methods for practical use as a biosignal analysis tool

Optimiser le réchauffement chez le nouveau-né asphyxié soumis à l'hypothermie thérapeutique

L'encéphalopathie hypoxique ischémique néonatale (EHI) reste la cause principale de mortalité chez le nouveau-né à terme. Un tiers des survivants vont développer des séquelles neurologiques, dont la paralysie cérébrale (PC), l'épilepsie et un retard intellectuel. Afin d'améliorer leur pronostic, ces nouveau-nés sont soumis à l'hypothermie thérapeutique (HT) qui débute au plus tard 6 heures après la naissance, pour une durée totale de 72 heures, suivie d'un réchauffement graduel (0.5°C/h). Il a été démontré que cette thérapie à effet neuroprotecteur diminue considérablement l'étendue des lésions cérébrales et la fréquence des séquelles neurologiques. Or, des études animales suggèrent que l'hypothermie sans sédation avec opioïdes n'est pas bénéfique. Selon les observations qui ont été réalisées, les porcelets traités avec la thérapie, mais sans l’administration d’analgésique ont manifesté des signes d’instabilités et de tremblements exagérés. On ignorait jusqu’à présent dans quelle mesure ces résultats tirés des expérimentations animales pouvaient être généralisables au nouveau-né. Ainsi, mon projet de maîtrise vise à mieux comprendre les facteurs qui risquent de compromettre les effets bénéfiques de la thérapie de refroidissement, dans le but d’optimiser la neuroprotection et d’améliorer le développement des nourrissons atteints d’EHI. Nous avons comme objectif principal d’évaluer l’association entre les doses d’opioïdes consommées pendant l’HT, le degrée de tremblement, et l’évolution de l’index de discontinuité à l’EEG au fil des 72h de l’HT, du réchauffement et jusqu’à 12 heures post-HT. Pour répondre à l’objectif, nous avons conduit une étude chez 21 nouveau-nés avec EHI soumis à l’HT, et dont les principaux résultats ont montré des associations significatives entre les fortes doses d’opioïdes administrés à l’enfant (r = - 0.493, p = 0.023), les frissons réduits pendant l’HT (r = 0.513, p = 0.017) et l’amélioration du rythme cérébrale d’EEG. Ces résultats sont décrits de manière plus approfondie dans le Chapitre 2 qui présente la version de l’article soumis à la revue Journal of Pediatrics, et le Chapitre 3 qui présente un retour sur la littérature à la lumière de nos trouvailles. Quant au Chapitre 4, nous y élaborons les possibilités de perspectives futures et les retombées cliniques de nos résultats. À long terme, nous espérons que nos travaux permettront l’ouverture d’une nouvelle piste d’amélioration de la neuroprotection, en favorisant systématiquement une meilleure prise en charge de la douleur et du stress induit par le refroidissement.Neonatal hypoxic-ischemic encephalopathy (HIE) remains the leading cause of death and mortality in the term infant. A third of the survivors will develop neurological sequelae including cerebral palsy (CP), epilepsy and mental retardation. In order to improve their prognosis, these newborns undergo therapeutic hypothermia (TH), which begins no later than 6 hours after birth, maintained for a total duration of 72 hours and followed by gradual rewarming (0.5°C/h). This neuroprotective therapy has been shown to significantly decrease the extent of brain injury and the frequency of neurological sequelae. Results from animal studies revealed that ongoing hypothermia without proper anesthesia is not beneficial. Based on the observations that have been reported, piglets treated with TH with no analgesics have shown signs of instability and excessive tremors. Until now, the extent to which these results from animal experiments could be generalized to the newborn remained unknown. Thus, the purpose of my master’s project was to better understand the clinical factors that may compromise the beneficial effects of TH, in an attempt to optimize neuroprotection and improve the neurological outcome of HIE infants. Our main objective was to assess the associations between opioid doses consumed during TH, shivering recorded during TH, and the evolution of EEG discontinuity index over the course of TH, rewarming and up to 12 hours post-TH. To meet the objective, we conducted a study in 21 newborns with HIE undergoing TH, and the results have shown significant associations between high doses of opioid administered (r = - 0.493, p = 0.023), reduced shivering stress (r = 0.513, p = 0.017) and improved EEG background activity. The key findings of the study are described in more detail in Chapter 2, which presents the original manuscript submitted for publication to the “Journal of Pediatrics”, and Chapter 3, which presents a review of the literature in light of our results. In Chapter 4, we discuss future perspectives and the clinical significance of our results. At last, we hope that our study will open up new avenues for improving neuroprotection, by systematically promoting a better management of pain and cooling-induced stress