Analyzing autonomic activity in neonatal seizures

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 54-55).Recent studies suggest that seizures in the newborn occur more often than previously appreciated. The effect of neonatal seizures remain unclear, however. Do seizures in the newborn cause brain injury, are they a consequence of brain injury, or are they benign? Seizures in the newborn tend to occur without overt clinical correlates, such as convulsions, so their diagnosis requires electroencephalography (EEG). In this thesis, we investigate whether seizure activity is associated with changes in the discharge pattern of the autonomic nervous system, which could be picked up in heart rate (HR) or heart-rate variability (HRV) analysis. More fundamentally, we seek to investigate whether seizures in the neonate are confined to the cerebral cortex or whether they might originate from or propagate to deeper brain structures. Prior studies have provided some evidence that neonatal seizures can result in HR and HRV changes. From these past studies, there seems to be a heart-brain connection, however, this connection is currently poorly understood. Our long term goal is to understand the connection between electro-cortical activity, electro-cardiac activity, and brain injury in newborns with seizures. In this study, we analyzed the EEG and the electrocardiogram (ECG) signals in fourteen newborns with neonatal stroke and three newborns with hypoxemic-ischemic encephalopathy. Furthermore, we used information from magnetic resonance imaging and magnetic resonance spectroscopy reports to identify injury location in these full-term newborns. Our results indicate that some babies show strong changes in HR and HRV during seizure episodes while others tend to respond very weakly. Due to the small sample size of our patient population, no consistent picture emerged whether the location of injury might be responsible for this response pattern. We also explored a spectrogram-based method to determine the occurrence of seizure (on a lead-by-lead basis) and to determine seizure propagation from one region of the cortex to another.by Priya Ramaswamy.M.Eng

Neonatal seizure detection based on single-channel EEG: instrumentation and algorithms

Seizure activity in the perinatal period, which constitutes the most common neurological emergency in the neonate, can cause brain disorders later in life or even death depending on their severity. This issue remains unsolved to date, despite the several attempts in tackling it using numerous methods. Therefore, a method is still needed that can enable neonatal cerebral activity monitoring to identify those at risk. Currently, electroencephalography (EEG) and amplitude-integrated EEG (aEEG) have been exploited for the identification of seizures in neonates, however both lack automation. EEG and aEEG are mainly visually analysed, requiring a specific skill set and as a result the presence of an expert on a 24/7 basis, which is not feasible. Additionally, EEG devices employed in neonatal intensive care units (NICU) are mainly designed around adults, meaning that their design specifications are not neonate specific, including their size due to multi-channel requirement in adults - adults minimum requirement is ≥ 32 channels, while gold standard in neonatal is equal to 10; they are bulky and occupy significant space in NICU. This thesis addresses the challenge of reliably, efficiently and effectively detecting seizures in the neonatal brain in a fully automated manner. Two novel instruments and two novel neonatal seizure detection algorithms (SDAs) are presented. The first instrument, named PANACEA, is a high-performance, wireless, wearable and portable multi-instrument, able to record neonatal EEG, as well as a plethora of (bio)signals. This device despite its high-performance characteristics and ability to record EEG, is mostly suggested to be used for the concurrent monitoring of other vital biosignals, such as electrocardiogram (ECG) and respiration, which provide vital information about a neonate's medical condition. The two aforementioned biosignals constitute two of the most important artefacts in the EEG and their concurrent acquisition benefit the SDA by providing information to an artefact removal algorithm. The second instrument, called neoEEG Board, is an ultra-low noise, wireless, portable and high precision neonatal EEG recording instrument. It is able to detect and record minute signals (< 10 nVp) enabling cerebral activity monitoring even from lower layers in the cortex. The neoEEG Board accommodates 8 inputs each one equipped with a patent-pending tunable filter topology, which allows passband formation based on the application. Both the PANACEA and the neoEEG Board are able to host low- to middle-complexity SDAs and they can operate continuously for at least 8 hours on 3-AA batteries. Along with PANACEA and the neoEEG Board, two novel neonatal SDAs have been developed. The first one, termed G prime-smoothed (G ́_s), is an on-line, automated, patient-specific, single-feature and single-channel EEG based SDA. The G ́_s SDA, is enabled by the invention of a novel feature, termed G prime (G ́) and can be characterised as an energy operator. The trace that the G ́_s creates, can also be used as a visualisation tool because of its distinct change at a presence of a seizure. Finally, the second SDA is machine learning (ML)-based and uses numerous features and a support vector machine (SVM) classifier. It can be characterised as automated, on-line and patient-independent, and similarly to G ́_s it makes use of a single-channel EEG. The proposed neonatal SDA introduces the use of the Hilbert-Huang transforms (HHT) in the field of neonatal seizure detection. The HHT analyses the non-linear and non-stationary EEG signal providing information for the signal as it evolves. Through the use of HHT novel features, such as the per intrinsic mode function (IMF) (0-3 Hz) sub-band power, were also employed. Detection rates of this novel neonatal SDA is comparable to multi-channel SDAs.Open Acces

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

Sleep State Trend (SST), a bedside measure of neonatal sleep state fluctuations based on single EEG channels

Objective To develop and validate an automated method for bedside monitoring of sleep state fluctuations in neonatal intensive care units. Methods A deep learning-based algorithm was designed and trained using 53 EEG recordings from a long-term (a)EEG monitoring in 30 near-term neonates. The results were validated using an external dataset from 30 polysomnography recordings. In addition to training and validating a single EEG channel quiet sleep, we constructed Sleep State Trend (SST), a bedside-ready means for visualising classifier outputs. Results The accuracy of quiet sleep detection in the training data was 90%, and the accuracy was comparable (85-86 %) in all bipolar derivations available from the 4-electrode recordings. The algorithm generalised well to an external dataset, showing 81% overall accuracy despite different signal derivations. SST allowed an intuitive, clear visualisation of the classifier output. Conclusions Fluctuations in sleep states can be detected at high fidelity from a single EEG channel, and the results can be visualised as a transparent and intuitive trend in the bedside monitors. Significance The Sleep State Trend (SST) may provide caregivers a real-time view of sleep state fluctuations and its cyclicity.Peer reviewe

Comparação do uso do EEG convencional, EEG de amplitude integrada e EEG geodésico em neonatos prematuros : uma revisão sistemática

Métodos para avaliar a atividade cortical em neonatos têm grande importância na Medicina moderna, pois permitem a observação e avaliação de diversos aspectos clínicos, garantindo que a equipe de saúde tenha conhecimento sobre possíveis medidas de intervenção que possam ser necessárias no tratamento de recém-nascidos. Objetivo: Esta revisão sistemática tem como objetivo comparar as principais tecnologias disponíveis para a avaliação das funções cerebrais em neonatos: eletroencefalograma convencional (EEG), eletroencefalograma de amplitude integrada (aEEG) e eletroencefalograma da rede do sensor geodésico. Métodos: Os artigos foram selecionados em periódicos nacionais e internacionais, incluídos nas bases de dados eletrônicas Web of Science, LILACS, SciELO e Medline. Resultados: Foram encontrados 39 artigos de interesse entre 155 artigos. As análises indicaram que, em relação ao ambiente clínico, o uso associativo de EEG convencional e aEEG é altamente recomendado, pois permite a combinação de funções, facilitando, por exemplo, que um maior número de convulsões sub-clínicas seja detectado. Por outro lado, o uso do eletroencefalograma da rede do sensor geodésico seria de grande valor, uma vez que permite que uma grande quantidade de dados seja analisada. Conclusão: Essa análise pode ser útil em estudos e pesquisas relacionados a doenças e sintomas, como convulsões, um desafio atual para a neuromonitorização neonatal, bem como aspectos de desenvolvimento neurológico e estudos funcionais. No entanto, apesar de muitos avanços tecnológicos, a eletroencefalografia em recém-nascidos prematuros ainda é um desafio em todo o mundo e requer pesquisas e esforços mais robustos para a melhor assistência clínica neste estágio extremamente precoce da vida.The use of methods to evaluate cortical activity in neonates has great importance in modern medicine, as it allows the observation and evaluation of several clinical aspects, which guarantees that the health team has knowledge about possible intervention measures that may be necessary in the treatment of newborns. Objective: This systematic review aimed to compare the main technologies available for the evaluation of brain functions in neonates, among them: the conventional electroencephalogram (EEG), the amplitude-integrated electroencephalogram (aEEG) and the geodesic sensor net EEG. Methods: A search was conducted forarticles from national and international periodicals included in the Web of Science, LILACS, SciELO and Medline electronic databases. Results: The search found 39 among 155 articles of interest and the analyses indicated that, in the clinical environment, the use of both conventional EEG and aEEG is highly recommended, as the combination of their functions allows, for example, a greater number of subclinical seizures to be detected. Conversely, the use of a geodesic sensor net EEG could be of great value, as it allows a large amount of data to be analyzed. Conclusion: This analysis may be useful in studies and research related to diseases and symptoms, such as seizures, a current challenge for neonatal neuromonitoring, as well as aspects of neurological development and functional studies. However, despite many advances in technology, electroencephalography in preterm neonates remains a challenge worldwide and still requires more robust research and efforts towards the best clinical assistance in this extremely early stage of life

Implantable Asynchronous Epilectic Seizure Detector

RÉSUMÉ Plusieurs algorithmes de détection à faible consommation ont été proposés pour le traitement de l'épilepsie focale. La gestion de l'énergie dans ces microsystèmes est une question importante qui dépend principalement de la charge et de la décharge des capacités parasites des transistors et des courants de court-circuit pendant les commutations. Dans ce mémoire, un détecteur asynchrone de crise pour le traitement de l'épilepsie focale est présenté. Ce système fait partie d'un dispositif implantable intégré pour stopper la propagation de la crise. L'objectif de ce travail est de réduire la dissipation de puissance en évitant les transitions inutiles de signaux grâce à la technique du « clock tree » ; en conséquence, les transistors ne changent pas d'état transitoire dans ce mode d'économie d'énergie (période de surveillance des EEG intracrâniens), sauf si un événement anormal est détecté. Le dispositif intégré proposé comporte un bio-amplificateur en amont (front-end) à faible bruit, un processeur de signal numérique et un détecteur. Un délai variable et quatre détecteurs de fenêtres de tensions variables en parallèles sont utilisés pour extraire de l’information sur le déclenchement des crises. La sensibilité du détecteur est améliorée en optimisant les paramètres variables en fonction des activités de foyers épileptiques de chaque patient lors du début des crises. Le détecteur de crises asynchrone proposé a été implémenté premièrement en tant que prototype sur un circuit imprimé circulaire, ensuite nous l’avons intégré sur une seule puce dans la technologie standard CMOS 0.13μm. La puce fabriquée a été validée in vitro en utilisant un total de 34 enregistrements EEG intracrâniens avec la durée moyenne de chaque enregistrement de 1 min. Parmi ces jeux de données, 15 d’entre eux correspondaient à des enregistrements de crises, tandis que les 19 autres provenaient d’enregistrements variables de patients tels que de brèves crises électriques, des mouvements du corps et des variations durant le sommeil. Le système proposé a réalisé une performance de détection précise avec une sensibilité de 100% et 100% de spécificité pour ces 34 signaux icEEG enregistrés. Le délai de détection moyen était de 13,7 s après le début de la crise, bien avant l'apparition des manifestations cliniques, et une consommation d'énergie de 9 µW a été obtenue à partir d'essais expérimentaux.----------ABSTRACT Several power efficient detection algorithms have been proposed for treatment of focal epilepsy. Power management in these microsystems is an important issue which is mainly dependent on charging and discharging of the parasitic capacitances in transistors and short-circuit currents during switching. In this thesis, an asynchronous seizure detector for treatment of the focal epilepsy is presented. This system is part of an implantable integrated device to block the seizure progression. The objective of this work is reducing the power dissipation by avoiding the unnecessary signal transition and clock tree; as a result, transistors do not change their transient state in power saving mode (icEEG monitoring period) unless an abnormal event detected. The proposed integrated device contains a low noise front-end bioamplifier, a digital signal processor and a detector. A variable time frame and four concurrent variable voltage window detectors are used to extract seizure onset information. The sensitivity of the detector is enhanced by optimizing the variable parameters based on specific electrographic seizure onset activities of each patient. The proposed asynchronous seizure detector was first implemented as a prototype on a PCB and then integrated in standard 0.13 μm CMOS process. The fabricated chip was validated offline using a total of 34 intracranial EEG recordings with the average time duration of 1 min. 15 of these datasets corresponded to seizure activities while the remaining 19 signals were related to variable patient activities such as brief electrical seizures, body movement, and sleep patterns. The proposed system achieved an accurate detection performance with 100% sensitivity and 100 % specificity for these 34 recorded icEEG signals. The average detection delay was 13.7 s after seizure onset, well before the onset of the clinical manifestations. Finally, power consumption of the chip is 9 µW obtained from experimental tests