Relevant bibliographies by topics / Neonatal Seizure Detector

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Academic literature on the topic 'Neonatal Seizure Detector'

Author: Grafiati
Published: 14 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Neonatal Seizure Detector"

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Chen, Hongyu, Zaihao Wang, Chunmei Lu, et al. "Neonatal Seizure Detection Using a Wearable Multi-Sensor System." Bioengineering 10, no. 6 (2023): 658. http://dx.doi.org/10.3390/bioengineering10060658.

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Neonatal seizure is an important clinical symptom of brain dysfunction, which is more common in infancy than in childhood. At present, video electroencephalogram (VEEG) technology is widely used in clinical practice. However, video electroencephalogram technology has several disadvantages. For example, the wires connecting the medical instruments may interfere with the infant’s movement and the gel patch electrode or disk electrode commonly used to monitor EEG may cause skin allergies or even tears. For the above reasons, we developed a wearable multi-sensor platform for newborns to collect ph
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Ansari, Amir H., Perumpillichira J. Cherian, Alexander Caicedo, Gunnar Naulaers, Maarten De Vos, and Sabine Van Huffel. "Neonatal Seizure Detection Using Deep Convolutional Neural Networks." International Journal of Neural Systems 29, no. 04 (2019): 1850011. http://dx.doi.org/10.1142/s0129065718500119.

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Identifying a core set of features is one of the most important steps in the development of an automated seizure detector. In most of the published studies describing features and seizure classifiers, the features were hand-engineered, which may not be optimal. The main goal of the present paper is using deep convolutional neural networks (CNNs) and random forest to automatically optimize feature selection and classification. The input of the proposed classifier is raw multi-channel EEG and the output is the class label: seizure/nonseizure. By training this network, the required features are o
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Borovac, Ana, Thomas Philip Runarsson, Gardar Thorvardsson, and Steinn Gudmundsson. "Neonatal seizure detection algorithms: The effect of channel count." Current Directions in Biomedical Engineering 8, no. 2 (2022): 604–7. http://dx.doi.org/10.1515/cdbme-2022-1154.

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Abstract The number of electrodes used to acquire neonatal EEG signals varies between institutions. Therefore, tools for automatic EEG analysis, such as neonatal seizure detection algorithms, need to be able to handle different electrode montages in order to find widespread use. The aim of this study was to analyse the effect of montage on neonatal seizure detector performance. A full 18-channel montage was compared to reduced 3- and 8-channel montages using a convolutional neural network for seizure detection. Sensitivity decreased by 10 - 18 % for the reduced montages while specificity was m
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Duckworth, Eleanor, Daniyal Motan, Kitty Howse, Stewart Boyd, Ronit Pressler, and Maria Chalia. "Diagnostic Accuracy of the Persyst Automated Seizure Detector in the Neonatal Population." Journal of Integrative Neuroscience 23, no. 8 (2024): 150. http://dx.doi.org/10.31083/j.jin2308150.

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Temko, Andriy, William Marnane, Geraldine Boylan, and Gordon Lightbody. "A Data-Driven Energy Based Estimator of EEG Channel Importance for Improved Patient-Adaptive Neonatal Seizure Detector." IFAC Proceedings Volumes 44, no. 1 (2011): 13770–75. http://dx.doi.org/10.3182/20110828-6-it-1002.03457.

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Buraniqi, Ersida, Arnold J. Sansevere, Kush Kapur, Ann M. Bergin, Phillip L. Pearl, and Tobias Loddenkemper. "Electrographic Seizures in Preterm Neonates in the Neonatal Intensive Care Unit." Journal of Child Neurology 32, no. 10 (2017): 880–85. http://dx.doi.org/10.1177/0883073817713918.

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Objective: Characterize clinical and electroencephalography (EEG) characteristics of preterm neonates undergoing continuous EEG in the neonatal intensive care unit. Methods: Retrospective study of preterm neonates born less than 37 weeks’ gestational age undergoing continuous EEG in the neonatal intensive care unit at Boston Children’s Hospital over a 2-year period. Results: Fifty-two preterms (46% male) had a mean gestational age of 32.8 weeks (standard deviation = 4.17). Seizures were detected in 12/52 (23%), with EEG seizures detected in 4/12 (33%). The median time from EEG to the first sei
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Durgesh, Kannam, and Sandeep Reddy Chinthireddy. "A Study of Clinical and Biochemical Profile of Neonatal Seizures." International Journal of Toxicological and Pharmacological Research 12, no. 11 (2022): 1–7. https://doi.org/10.5281/zenodo.11420695.

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<strong>Background:</strong>&nbsp;One of the most prevalent and recognizable clinical signs of a neurological system in failure is neonatal seizures. Neonatal seizures are the outcome of the developing nervous system&rsquo;s reactions to various shocks and can cause significant neonatal death and morbidity as well as childhood physical and cognitive deficits. Preterm infants are more likely to experience newborn seizures than term infants, and metabolic disorders are a frequent cause of neonatal seizures.&nbsp;<strong>Methods:</strong>&nbsp;Based on the inclusion and exclusion criteria a total
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Buttle, Sarah Grace, Brigitte Lemyre, Erick Sell, et al. "Combined Conventional and Amplitude-Integrated EEG Monitoring in Neonates: A Prospective Study." Journal of Child Neurology 34, no. 6 (2019): 313–20. http://dx.doi.org/10.1177/0883073819829256.

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Background/Objective: Seizure monitoring via amplitude-integrated EEG is standard of care in many neonatal intensive care units; however, conventional EEG is the gold standard for seizure detection. We compared the diagnostic yield of amplitude-integrated EEG interpreted at the bedside, amplitude-integrated EEG interpreted by an expert, and conventional EEG. Methods: Neonates requiring seizure monitoring received amplitude-integrated EEG and conventional EEG in parallel. Clinical events and amplitude-integrated EEG were interpreted at bedside. Subsequently, amplitude-integrated EEG and convent
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Abbasi, Hamid, Joanne O. Davidson, Simerdeep K. Dhillon, et al. "Deep Learning for Generalized EEG Seizure Detection after Hypoxia–Ischemia—Preclinical Validation." Bioengineering 11, no. 3 (2024): 217. http://dx.doi.org/10.3390/bioengineering11030217.

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Brain maturity and many clinical treatments such as therapeutic hypothermia (TH) can significantly influence the morphology of neonatal EEG seizures after hypoxia–ischemia (HI), and so there is a need for generalized automatic seizure identification. This study validates efficacy of advanced deep-learning pattern classifiers based on a convolutional neural network (CNN) for seizure detection after HI in fetal sheep and determines the effects of maturation and brain cooling on their accuracy. The cohorts included HI–normothermia term (n = 7), HI–hypothermia term (n = 14), sham–normothermia term
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Buttle, SG, B. Lemyre, E. Sell, et al. "P.056 Combined conventional and amplitude-integrated EEG monitoring in neonates: a prospective study." Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques 46, s1 (2019): S29. http://dx.doi.org/10.1017/cjn.2019.156.

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Background: Seizure monitoring via amplitude-integrated EEG (aEEG) is standard of care in many NICUs; however, conventional EEG (cEEG) is the gold standard for seizure detection. We compared the diagnostic yield of aEEG interpreted at the bedside, aEEG interpreted by an expert, and cEEG. Methods: Neonates received aEEG and cEEG in parallel. Clinical events and aEEG were interpreted at bedside and subsequently independently analyzed by experienced neonatology and neurology readers. Sensitivity and specificity of bedside aEEG as compared to expert aEEG interpretation and cEEG were evaluated. Res
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Dissertations / Theses on the topic "Neonatal Seizure Detector"

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Frassineti, Lorenzo. "Development of multimodal systems for monitoring paediatric brain disorders." Doctoral thesis, Università di Siena, 2023. https://hdl.handle.net/11365/1227514.

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In the last years, artificial intelligence (AI) methods are extensively applied in several fields, including healthcare, with several applications to support diagnostic approaches or treatments. The research activities carried on during my PhD work have been devoted to the development of AI methods to support neonatologists and paediatric neurologists in the detection, characterization, and monitoring of brain disorders in paediatric subjects. Specifically, the PhD work was focused on the development of multimodal systems for: neonatal and absence seizure detection; quantitative characterizati
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Thomas, Cameron W. "Altering time compression algorithms of amplitude-integrated electroencephalography display improves neonatal seizure detection." University of Cincinnati / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1367926003.

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Rankine, Luke. "Newborn EEG seizure detection using adaptive time-frequency signal processing." Thesis, Queensland University of Technology, 2006. https://eprints.qut.edu.au/16200/1/Luke_Rankine_Thesis.pdf.

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Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible me
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Rankine, Luke. "Newborn EEG seizure detection using adaptive time-frequency signal processing." Queensland University of Technology, 2006. http://eprints.qut.edu.au/16200/.

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Dysfunction in the central nervous system of the neonate is often first identified through seizures. The diffculty in detecting clinical seizures, which involves the observation of physical manifestations characteristic to newborn seizure, has placed greater emphasis on the detection of newborn electroencephalographic (EEG) seizure. The high incidence of newborn seizure has resulted in considerable mortality and morbidity rates in the neonate. Accurate and rapid diagnosis of neonatal seizure is essential for proper treatment and therapy. This has impelled researchers to investigate possible me
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Books on the topic "Neonatal Seizure Detector"

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Jacquemyn, Yves, and Anneke Kwee. Antenatal and intrapartum fetal evaluation. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198713333.003.0006.

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Antenatal and intrapartum fetal monitoring aim to identify the beginning of the process of fetal hypoxia before irreversible fetal damage has taken place. Fetal movement counting by the mother has not been reported to be of any benefit. The biophysical profile score, incorporating ultrasound and fetal heart rate monitoring, has not been proven to reduce perinatal mortality in randomized trials. Doppler ultrasound allows the exploration of the perfusion of different fetal organ systems and provides data on possible hypoxia and fetal anaemia. Maternal uterine artery Doppler can be used to select
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Book chapters on the topic "Neonatal Seizure Detector"

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Tapani, Karoliina T., Sampsa Vanhatalo, and Nathan J. Stevenson. "Incorporating spike correlations into an SVM-based neonatal seizure detector." In EMBEC & NBC 2017. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5122-7_81.

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El-Dib, Mohamed, and Linda S. de Vries. "Neurophysiological Monitoring." In Neonatal Brain Injury. Springer Nature Switzerland, 2024. https://doi.org/10.1007/978-3-031-55972-3_16.

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AbstractEEG (electroencephalogram) is utilized to detect cortical cerebral electric activity. In neonates, both conventional EEG (cEEG) and a simplified form known as amplitude-integrated EEG (aEEG) hold significant value. Prolonged continuous EEG is recommended in high-risk scenarios and has been associated with improved seizure identification and outcomes. While aEEG aids in seizure detection, cEEG remains the gold standard for diagnosing seizures in neonates. aEEG is employed as a bedside tool for selecting infants with neonatal encephalopathy for therapeutic hypothermia, while both aEEG an
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Thomas, Eoin M., Andrey Temko, Gordon Lightbody, William P. Marnane, and Geraldine B. Boylan. "Advances in Automated Neonatal Seizure Detection." In New Advances in Intelligent Signal Processing. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-11739-8_5.

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Abirami, S., John Thomas, Rajamanickam Yuvaraj, and Jac Fredo Agastinose Ronickom. "A Comparative Study on EEG Features for Neonatal Seizure Detection." In Biomedical Signals Based Computer-Aided Diagnosis for Neurological Disorders. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97845-7_3.

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Bjorkman, S. Tracey. "Origin and Detection of Neonatal Seizures: Animal and Clinical Studies." In Neuromethods. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3014-2_17.

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Chen Hongyu, Xue Mengru, Oetomo Sidarto Bambang, and Chen Wei. "Neonatal Seizure Detection with Wearable Sensor System." In Ambient Intelligence and Smart Environments. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-690-3-560.

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Seizures in neonates, the most common sign of neurological dysfunction, require immediate medical attention. A new system for the detection of neonatal seizures using the wearable sensor is presented. The system including smart vest and system software aims for providing reliable continuous monitoring as well as a comfortable clinical environment for neonatal seizure and giving an alarm when seizure occurs on the basis of Electrocardiogram (ECG) and motion sensing techniques. We present the concept of the neonatal vest that enables ECG measurement by textile and motion measurement by Inertial
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Yawno, Tamara, and Rod Hunt. "Recent Developments in Neonatal Seizure Interventions." In Current Treatments for Epilepsy [Working Title]. IntechOpen, 2025. https://doi.org/10.5772/intechopen.1008914.

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Neonatal seizures, a leading neurological disorder, occur in 1 to 3 per 1000 live births, with higher incidence in premature infants. Despite advancements in neonatal care reducing mortality, long-term morbidity—such as cerebral palsy, developmental delays, and post-neonatal epilepsy—remains a significant concern. Neonatal seizures are predominantly symptomatic of acute brain injury, with common causes including hypoxic-ischemic encephalopathy (HIE) in term infants and intraventricular hemorrhage in preterm infants, along with CNS infections, metabolic disorders, and brain malformations. Recen
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Gao, Guangmiao, Jie Liu, Qi Yuan, and Weiwei Nie. "Neonatal Seizure Detection Combining Sparse Representation and Deep Learning." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2023. http://dx.doi.org/10.3233/faia230846.

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Neonatal seizures are a common emergency in neonatal intensive care unit (NICU). Neonatal epilepsy detection is generally the doctor through the naked eye reading electroencephalogram (EEG) to judge, this way is more subjective easy to produce differences. In this paper, a novel algorithm combining sparse representation and deep learning for automatic detection of neonatal seizures is proposed. After the EEG data is preprocessed, the features are extracted and sent to the Deep Sparse Representation Classification (DSRC) network. The performance of the algorithm was evaluated by sensitivity (se
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De Vos, M., P. J. Cherian, W. Deburchgraeve, et al. "Automated Neonatal Brain Monitoring." In Neonatal Monitoring Technologies. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0975-4.ch011.

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Monitoring the electroencephalogram (EEG) in sick newborn babies in the neonatal intensive care units (NICU) gives important information about brain function. Seizures are frequently seen in the EEG of the sick neonate, and usually denote serious underlying brain dysfunction. Current clinical practice assumes that neonatal seizures have to be treated to prevent further injury to the brain. Recording of amplitude integrated EEG (aEEG) or the full EEG supports treatment decisions as well as prognostication has become standard practice in many NICUs. aEEG has become popular in recent years due to
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Azzopardi, Denis. "Introduction to the CFM and the Clinical Applications." In Neonatal Monitoring Technologies. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0975-4.ch010.

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The cerebral function monitor is a device for trend monitoring of changes in the amplitude of the electroencephalogram, typically recorded from 1-2 pairs of electrodes. Initially developed and introduced to monitor cerebral activity in encephalopathic adult patients or during anaesthesia it is now most widely used in newborns with encephalopathy to assess the severity of encephalopathy and for prognosis. The time to recovery from a moderately/severely abnormal amplitude integrated electroencephalogram trace to a normal trace is strongly predictive of subsequent neurological outcome following n
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Conference papers on the topic "Neonatal Seizure Detector"

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Gálvez, Sergio Morant, Mark O'Sullivan, Brian Henry Walsh, and Alison O'Shea. "Modelling Individual Experts in Neonatal Seizure Detection Algorithm Development." In 2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI). IEEE, 2024. https://doi.org/10.1109/bhi62660.2024.10913754.

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Udayantha, Dinuka Sandun, Kavindu Weerasinghe, Nima Wickramasinghe, et al. "Using Explainable AI for EEG-based Reduced Montage Neonatal Seizure Detection." In 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2024. https://doi.org/10.1109/smc54092.2024.10831030.

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Pang, Bingheng, Zhuoxuan Liang, Wei Li, Xiangxu Meng, Chenhao Wang, and Yilin Ren. "Brain Waves Unleashed: Illuminating Neonatal Seizure Detection via Multi-scale Hierarchical Modeling." In 2024 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2024. http://dx.doi.org/10.1109/icme57554.2024.10687908.

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"IMPROVEMENT AND VALIDATION OF AN AUTOMATED NEONATAL SEIZURE DETECTOR." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003127700310037.

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Frassineti, Lorenzo, Claudia Manfredi, Benedetta Olmi, and Antonio Lanata. "A Generalized Linear Model for an ECG-based Neonatal Seizure Detector." In 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2021. http://dx.doi.org/10.1109/embc46164.2021.9630841.

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Ansari, A. H., V. Matic, M. De Vos, G. Naulaers, P. J. Cherian, and S. Van Huffel. "Improvement of an automated neonatal seizure detector using a post-processing technique." In 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2015. http://dx.doi.org/10.1109/embc.2015.7319724.

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Temko, Andriy, William Marnane, Geraldine Boylan, John M. O'Toole, and Gordon Lightbody. "Neonatal EEG audification for seizure detection." In 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2014. http://dx.doi.org/10.1109/embc.2014.6944612.

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"NEONATAL SEIZURE DETECTION USING BLIND ADAPTIVE FUSION." In International Conference on Bio-inspired Systems and Signal Processing. SciTePress - Science and and Technology Publications, 2011. http://dx.doi.org/10.5220/0003294903650371.

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Khlif, M. S., M. Mesbah, B. Boashash, and P. Colditz. "Detection of neonatal seizure using multiple filters." In 2010 10th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA). IEEE, 2010. http://dx.doi.org/10.1109/isspa.2010.5605469.

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Greene, Barry R., Richard B. Reilly, Geraldine Boylan, Philip de Chazal, and Sean Connolly. "Multi-channel EEG based Neonatal Seizure Detection." In Conference Proceedings. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, 2006. http://dx.doi.org/10.1109/iembs.2006.260461.

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