Monitoring and diagnosing neonatal seizures by video signal processing

In this thesis we consider the use of well-known statistical methods to early diagnose, through wire-free low-cost video processing, the potential presence of seizures. For this purpose several approaches, have been proposed: periodicity-based, classification-based and clustering-based approaches

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

Monitoring and Diagnosing Neonatal Seizures by Video Signal Processing

In this thesis we consider the use of well-known statistical methods to early diagnose, through wire-free low-cost video processing, the potential presence of seizures. For this purpose several approaches, have been proposed: periodicity-based, classification-based and clustering-based approaches

Self-Reporting Technologies for Supporting Epilepsy Treatment

Epilepsy diagnosis and treatment relies heavily on patient self-reporting for informing clinical decision-making. These self-reports are traditionally collected from handwritten patient journals and tend to be either incomplete or inaccurate. Recent mobile and wearable health tracking developments stand to dramatically impact clinical practice through supporting patient and caregiver data collection activities. However, the specific types and characteristics of the data that clinicians need for patient care are not well known. In this study, we conducted interviews, a literature review, an expert panel, and online surveys to assess the availability and quality of patient-reported data that is useful but reported as being unavailable, difficult for patients to collect, or unreliable during epilepsy diagnosis and treatment, respectively. The results highlight important yet underexplored data collection and design opportunities for supporting the diagnosis, treatment, and self-management of epilepsy and expose notable gaps between clinical data needs and current patient practices

Doctor of Philosophy

dissertationPerinatal hypoxic-ischemic (PHI) encephalopathy afflicts roughly 1-2 in every 1000 live births, predisposing affected infants to a higher probability of developing epilepsy, cerebral palsy, and other neurological disorders. In many forms of acquired epilepsy, including PHI, there is a seizure-free period of time between the injury and the onset of the first spontaneous recurrent seizure (SRS) termed the latent period. In animal models of PHI, we aim to better understand the mechanisms that lead to an epileptic network that occur during this latent period. Due to limitations in performing electrophysiological experiments in immature animals, this time period remains under-studied in the pediatric population. We start our study at the cellular level using immunohistochemistry and whole-cell patch clamp methods before moving to the whole brain level with magnetic resonance imaging and the electroencephalogram (EEG) to examine anatomical and physiological changes that precede the development of epilepsy. We find that immediately after injury, early cell loss results in a reduction in the amount of excitatory and inhibitory synaptic input to pyramidal cells within the peri-infarct region. However, this reduction is short term, as there is a rapid recovery in the synaptic inputs 2 weeks later without any identifiable increase in the number of cells. As the brain continues to develop, the cellular loss that occurs early on leads to atrophy, and sometimes complete loss of the cortex, hippocampus, and thalamus. Even with major cell loss, power spectral analysis of the EEG identified no obvious reduction or increase in the power of any of the various cortical rhythms (delta, theta, alpha, beta, and gamma). However, EEG analysis did reveal the earliest known time point at which seizures occur in this animal model, as well as a previously undescribed short-duration convulsive seizure. Our findings suggest that the mechanisms responsible for the development of SRSs begin immediately after injury and result in a variable and progressive latent period

Amplitude-integrated EEG assists in detecting cerebral dysfunction in the newborn

Background: Amplitude-integrated encephalography (aEEG) in term-born encephalopathic infants has been shown to be predictive of later neurodevelopmental outcomes, but little is known about the mediating cerebral pathology. In addition, the aEEG is commonly used to monitor electrographic seizures in the newborn, an important manifestation of cerebral pathology, but there is limited data on it’s efficacy for this purpose. It’s clinical application in the preterm infant remains to be explored. Aim: The central aim of this thesis is to prove the hypothesis that the aEEG assists in detecting cerebral dysfunction in the newborn. Methods: 1) In a cohort of term-born infants with encephalopathy and/or seizures digital aEEG background measures of the lower and upper aEEG margins were related to a numeric MRI abnormality score. 2) In at-risk term newborns, the accuracy of two-channel digital aEEG monitoring was compared with continuous concurrent conventional EEG for seizure detection. 3) In preterm infants (gestation at birth < 30 weeks) aEEG measures of lower and upper margin collected in the first week of life were compared in infants with substantial cerebral abnormality to infants without. Results: 1) For all infants in the term cohort, the severity of abnormality of aEEG background was strongly related to severity of abnormality seen on cerebral MRI. 2) Using the aEEG pattern with the raw EEG signal, 76% of electrographic seizures were correctly identified in the term infants. 3) In the preterm cohort, the lower and upper aEEG amplitude margins increased significantly during the first week of life. In the presence of substantial cerebral abnormality, these margins were significantly depressed. Seizures were noted in the smaller and sicker, infants. Conclusion: The central hypothesis of this thesis, that the aEEG assists in detecting cerebral dysfunction in the newborn was proved

Epilepsy

Epilepsy is the most common neurological disorder globally, affecting approximately 50 million people of all ages. It is one of the oldest diseases described in literature from remote ancient civilizations 2000-3000 years ago. Despite its long history and wide spread, epilepsy is still surrounded by myth and prejudice, which can only be overcome with great difficulty. The term epilepsy is derived from the Greek verb epilambanein, which by itself means to be seized and to be overwhelmed by surprise or attack. Therefore, epilepsy is a condition of getting over, seized, or attacked. The twelve very interesting chapters of this book cover various aspects of epileptology from the history and milestones of epilepsy as a disease entity, to the most recent advances in understanding and diagnosing epilepsy