Relevant bibliographies by topics / Neonatal Seizure Detector

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  2. Dissertations / Theses
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Academic literature on the topic 'Neonatal Seizure Detector'

Author: Grafiati
Published: 14 March 2023

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Journal articles on the topic "Neonatal Seizure Detector"

1

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 (May 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 optimized, while fitting a nonlinear classifier on the features. After training the network with EEG recordings of 26 neonates, five end layers performing the classification were replaced with a random forest classifier in order to improve the performance. This resulted in a false alarm rate of 0.9 per hour and seizure detection rate of 77% using a test set of EEG recordings of 22 neonates that also included dubious seizures. The newly proposed CNN classifier outperformed three data-driven feature-based approaches and performed similar to a previously developed heuristic method.
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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 (August 1, 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 mostly unaffected. Electrode artefacts and artefacts associated with biological rhythms caused incorrect classification of nonseizure activity in some cases, but these artefacts were filtered out in the 3-channel montage. Other types of artefacts had little effect. Reduced montages result in some reduction in classifier accuracy, but the performance may still be acceptable. Recording artefacts had a limited effect on detection accuracy.
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Rao, Marpi Suryaprasada, Gavara Chinna Rao, Ayesha Sultana, and Putrevu Jagannadha Karthik. "Clinical, etiological, biochemical, microbiological and neurosonogram factors in related with neonatal seizures in Visakhapatnam, India." International Journal of Contemporary Pediatrics 4, no. 2 (February 22, 2017): 568. http://dx.doi.org/10.18203/2349-3291.ijcp20170711.

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Background: Neonatal seizure is a common neurological problem in the neonatal period. Detection of seizure, its etiology, and clinical types is important for guiding therapy. A varied number of conditions are capable of causing seizures in the neonatal period. The aim was to study biochemical, microbiological and, nurosonogram factors related with neonatal seizures in Visakhapatanam, India.Methods: The study was carried out in the Department of Paediatrics, Andhra medical college, King George Hospital, Visakhapatnam, Andhra Pradesh. The study was done to assess the biochemical changes, nurosonogram factors and microbilogical organisms implicated in neonatal seizures.Results: The present study is descriptive in nature where clinical spectrum of neonatal seizures in neonates was studied. 1500 neonates were admitted in NICU during the study period, among them 200 (13.3%) developed neonatal seizures. Etiology in majority of the cases of neonatal seizures was hypoxic ischemic encephalopathy (45%) followed in frequency by intracranial haemorrhage (14%), meningitis (12%), hypoglycaemia (11%), hypocalcaemia (4%) and others (14%). The most common organism implicated in neonatal seizures was Escherichia coli (36%), followed by Klebsiella (30%), staphylococcus aureus (19%), Streptococci agalactiae (7%) and unknown (8%). Meningitis accounted for 12% of neonatal seizures. Most common biochemical abnormalities noted were hypoglycemia, hypocalcaemia and hyponatremia.Conclusions: Biochemical abnormalities may significantly contribute to seizure activity and possibly correction of these abnormalities may play a significant role in seizure control. A biochemical work up is necessary for all cases of neonatal seizures. Appropriate treatment with antibiotics is essential. Examination of cerebrospinal fluid is essential work up in cases of neonatal seizures. Neurosonogram had good potential in predicting neurological outcome in neonates with perinatal asphyxia. Neurosonogram should be incorporated in the routine evaluation of seizures.
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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 (January 2011): 13770–75. http://dx.doi.org/10.3182/20110828-6-it-1002.03457.

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Buttle, Sarah Grace, Brigitte Lemyre, Erick Sell, Stephanie Redpath, Srinivas Bulusu, Richard J. Webster, and Daniela Pohl. "Combined Conventional and Amplitude-Integrated EEG Monitoring in Neonates: A Prospective Study." Journal of Child Neurology 34, no. 6 (February 14, 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 conventional EEG were independently analyzed by experienced neonatology and neurology readers. Sensitivity and specificity of bedside amplitude-integrated EEG as compared to expert amplitude-integrated EEG interpretation and conventional EEG were evaluated. Results: Thirteen neonates were monitored for an average duration of 33 hours (range 15-94, SD 25). Fourteen seizure-like events were detected by clinical observation, and 12 others by bedside amplitude-integrated EEG analysis. One of the clinical, and none of the bedside amplitude-integrated EEG events were confirmed as seizures on conventional EEG. Post hoc expert amplitude-integrated EEG interpretation revealed eight suspected seizures, all different from the ones detected by the bedside amplitude-integrated EEG team, of which one was confirmed via conventional EEG. Eight seizures were recorded on conventional EEG. Expert amplitude-integrated EEG interpretation had a sensitivity of 13% with 46% specificity for individual seizure detection, and a sensitivity of 50% with 46% specificity for detecting patients with seizures. Conclusion: Real-world bedside amplitude-integrated EEG monitoring failed to detect all seizures evidenced via conventional EEG, while misclassifying other events as seizures. Even post hoc expert amplitude-integrated EEG interpretation provided limited sensitivity and specificity. Considering the poor sensitivity and specificity of bedside amplitude-integrated EEG interpretation, combined monitoring may provide limited clinical benefit.
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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 (July 9, 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 seizure was 0.5 hours (interquartile range 0.24-4). Factors associated with seizures were male gender (odds ratio = 4.65 [95% confidence interval = 1.02-21.24], P = .047) and lack of EEG state change (odds ratio = 0.043 [95% confidence interval = 0.005-0.377], P = .04). Conclusion: Twenty-three percent of preterms undergoing continuous EEG had EEG seizures or electrographic seizures with no clear clinical correlate. This confirms recent American Clinical Neurophysiology Society guidelines suggesting that preterm neonates are at high risk for seizures.
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Olmi, Benedetta, Claudia Manfredi, Lorenzo Frassineti, Carlo Dani, Silvia Lori, Giovanna Bertini, Cesarina Cossu, Maria Bastianelli, Simonetta Gabbanini, and Antonio Lanatà. "Heart Rate Variability Analysis for Seizure Detection in Neonatal Intensive Care Units." Bioengineering 9, no. 4 (April 7, 2022): 165. http://dx.doi.org/10.3390/bioengineering9040165.

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In Neonatal Intensive Care Units (NICUs), the early detection of neonatal seizures is of utmost importance for a timely clinical intervention. Over the years, several neonatal seizure detection systems were proposed to detect neonatal seizures automatically and speed up seizure diagnosis, most based on the EEG signal analysis. Recently, research has focused on other possible seizure markers, such as electrocardiography (ECG). This work proposes an ECG-based NSD system to investigate the usefulness of heart rate variability (HRV) analysis to detect neonatal seizures in the NICUs. HRV analysis is performed considering time-domain, frequency-domain, entropy and multiscale entropy features. The performance is evaluated on a dataset of ECG signals from 51 full-term babies, 29 seizure-free. The proposed system gives results comparable to those reported in the literature: Area Under the Receiver Operating Characteristic Curve = 62%, Sensitivity = 47%, Specificity = 67%. Moreover, the system’s performance is evaluated in a real clinical environment, inevitably affected by several artefacts. To the best of our knowledge, our study proposes for the first time a multi-feature ECG-based NSD system that also offers a comparative analysis between babies suffering from seizures and seizure-free ones.
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Howard, Rachel, Runci Li, Kelly Harvey-Jones, Vinita Verma, Frédéric Lange, Geraldine Boylan, Ilias Tachtsidis, and Subhabrata Mitra. "Optical Monitoring in Neonatal Seizures." Cells 11, no. 16 (August 21, 2022): 2602. http://dx.doi.org/10.3390/cells11162602.

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Background: Neonatal seizures remain a significant cause of morbidity and mortality worldwide. The past decade has resulted in substantial progress in seizure detection and understanding the impact seizures have on the developing brain. Optical monitoring such as cerebral near-infrared spectroscopy (NIRS) and broadband NIRS can provide non-invasive continuous real-time monitoring of the changes in brain metabolism and haemodynamics. Aim: To perform a systematic review of optical biomarkers to identify changes in cerebral haemodynamics and metabolism during the pre-ictal, ictal, and post-ictal phases of neonatal seizures. Method: A systematic search was performed in eight databases. The search combined the three broad categories: (neonates) AND (NIRS) AND (seizures) using the stepwise approach following PRISMA guidance. Results: Fifteen papers described the haemodynamic and/or metabolic changes observed with NIRS during neonatal seizures. No randomised controlled trials were identified during the search. Studies reported various changes occurring in the pre-ictal, ictal, and post-ictal phases of seizures. Conclusion: Clear changes in cerebral haemodynamics and metabolism were noted during the pre-ictal, ictal, and post-ictal phases of seizures in neonates. Further studies are necessary to determine whether NIRS-based methods can be used at the cot-side to provide clear pathophysiological data in real-time during neonatal seizures.
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Gacio, Sebastián. "Amplitude-integrated electroencephalography for neonatal seizure detection. An electrophysiological point of view." Arquivos de Neuro-Psiquiatria 77, no. 2 (February 2019): 122–30. http://dx.doi.org/10.1590/0004-282x20180150.

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ABSTRACT Seizures in the newborn are associated with high morbidity and mortality, making their detection and treatment critical. Seizure activity in neonates is often clinically obscured, such that detection of seizures is particularly challenging. Amplitude-integrated EEG is a technique for simplified EEG monitoring that has found an increasing clinical application in neonatal intensive care. Its main value lies in the relative simplicity of interpretation, allowing nonspecialist members of the care team to engage in real-time detection of electrographic seizures. Nevertheless, to avoiding misdiagnosing rhythmic artifacts as seizures, it is necessary to recognize the electrophysiological ictal pattern in the conventional EEG trace available in current devices. The aim of this paper is to discuss the electrophysiological basis of the differentiation of epileptic seizures and extracranial artifacts to avoid misdiagnosis with amplitude-integrated EEG devices.
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Tapani, Karoliina T., Sampsa Vanhatalo, and Nathan J. Stevenson. "Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection." International Journal of Neural Systems 29, no. 04 (May 2019): 1850030. http://dx.doi.org/10.1142/s0129065718500302.

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The aim of this study was to develop methods for detecting the nonstationary periodic characteristics of neonatal electroencephalographic (EEG) seizures by adapting estimates of the correlation both in the time (spike correlation; SC) and time–frequency domain (time–frequency correlation; TFC). These measures were incorporated into a seizure detection algorithm (SDA) based on a support vector machine to detect periods of seizure and nonseizure. The performance of these nonstationary correlation measures was evaluated using EEG recordings from 79 term neonates annotated by three human experts. The proposed measures were highly discriminative for seizure detection (median [Formula: see text]: 0.933 IQR: 0.821–0.975, median [Formula: see text]: 0.883 IQR: 0.707–0.931). The resultant SDA applied to multi-channel recordings had a median AUC of 0.988 (IQR: 0.931–0.998) when compared to consensus annotations, outperformed two state-of-the-art SDAs [Formula: see text] and was noninferior to the human expert for 73/79 of neonates.
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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 characterization of the speech phenotype for some genetic syndromes; prediction of the neurodevelopmental scales in newborns with sepsis. In the first part of this PhD work, absence seizure detectors have been developed both for online and offline applications based on Electroencephalographic (EEG) signals and sonification algorithms. Following the encouraging results obtained for absence seizures, first attempts were made to validate EEG-based Neonatal Seizure Detectors (NSDs), a still tricky and time-consuming issue in the clinical practice. Moreover, Heart rate variability (HRV) analysis was proposed as an alternative approach for the detection of neonatal seizures. Experimental results confirmed the involvement of the Autonomic Nervous System during or close to neonatal seizures. The comparison between EEG-based NSDs and HRV ones confirmed that the best approach to detect neonatal seizures is still the EEG. However, when EEG techniques are not available, the use of HRV-based NSDs could be a promising alternative. In the second part of this PhD work, quantitative acoustical analysis has been applied to the definition of the speech phenotype for four genetic syndromes: Down, Noonan, Costello and Smith-Magenis. Preliminary results confirm that acoustical measures could add helpful information for several syndromes with well-known language/voice impairments. Being completely non-invasive, acoustical analysis and AI methods might significantly contribute to the clinical assessment of such pathologies, also after surgical, pharmacological or logopaedic treatments and for long-term monitoring of the acoustical characteristics of the voice of these subjects. The last part of this PhD thesis exploits the possibility of forecasting neurodevelopmental scores in preterm newborns with and without sepsis. Using AI regression models, reliable results at different time steps of the follow-up were obtained, both with EEG and HRV features. The BAYLEY-III test was used to compute the scores in three different domains: cognitive, language and motor. Results suggest that both EEG and HRV quantitative analysis could be helpful for the clinical staff, identifying the newborns at risk of neurodevelopmental delays. Summing up, this PhD thesis shows how AI methods could be a valid support to clinicians in neurological paediatrics. Several experimental results are presented, showing possible applications and factual integration between AI techniques and clinical knowledge and needs, providing novel solutions and tools to support the clinical staff in the detection and characterization of brain diseases in infants and children.
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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 methods for the automatic detection of newborn EEG seizure. This thesis is focused on the development of algorithms for the automatic detection of newborn EEG seizure using adaptive time-frequency signal processing. The assessment of newborn EEG seizure detection algorithms requires large datasets of nonseizure and seizure EEG which are not always readily available and often hard to acquire. This has led to the proposition of realistic models of newborn EEG which can be used to create large datasets for the evaluation and comparison of newborn EEG seizure detection algorithms. In this thesis, we develop two simulation methods which produce synthetic newborn EEG background and seizure. The simulation methods use nonlinear and time-frequency signal processing techniques to allow for the demonstrated nonlinear and nonstationary characteristics of the newborn EEG. Atomic decomposition techniques incorporating redundant time-frequency dictionaries are exciting new signal processing methods which deliver adaptive signal representations or approximations. In this thesis we have investigated two prominent atomic decomposition techniques, matching pursuit and basis pursuit, for their possible use in an automatic seizure detection algorithm. In our investigation, it was shown that matching pursuit generally provided the sparsest (i.e. most compact) approximation for various real and synthetic signals over a wide range of signal approximation levels. For this reason, we chose MP as our preferred atomic decomposition technique for this thesis. A new measure, referred to as structural complexity, which quantifes the level or degree of correlation between signal structures and the decomposition dictionary was proposed. Using the change in structural complexity, a generic method of detecting changes in signal structure was proposed. This detection methodology was then applied to the newborn EEG for the detection of state transition (i.e. nonseizure to seizure state) in the EEG signal. To optimize the seizure detection process, we developed a time-frequency dictionary that is coherent with the newborn EEG seizure state based on the time-frequency analysis of the newborn EEG seizure. It was shown that using the new coherent time-frequency dictionary and the change in structural complexity, we can detect the transition from nonseizure to seizure states in synthetic and real newborn EEG. Repetitive spiking in the EEG is a classic feature of newborn EEG seizure. Therefore, the automatic detection of spikes can be fundamental in the detection of newborn EEG seizure. The capacity of two adaptive time-frequency signal processing techniques to detect spikes was investigated. It was shown that a relationship between the EEG epoch length and the number of repetitive spikes governs the ability of both matching pursuit and adaptive spectrogram in detecting repetitive spikes. However, it was demonstrated that the law was less restrictive forth eadaptive spectrogram and it was shown to outperform matching pursuit in detecting repetitive spikes. The method of adapting the window length associated with the adaptive spectrogram used in this thesis was the maximum correlation criterion. It was observed that for the time instants where signal spikes occurred, the optimal window lengths selected by the maximum correlation criterion were small. Therefore, spike detection directly from the adaptive window optimization method was demonstrated and also shown to outperform matching pursuit. An automatic newborn EEG seizure detection algorithm was proposed based on the detection of repetitive spikes using the adaptive window optimization method. The algorithm shows excellent performance with real EEG data. A comparison of the proposed algorithm with four well documented newborn EEG seizure detection algorithms is provided. The results of the comparison show that the proposed algorithm has significantly better performance than the existing algorithms (i.e. Our proposed algorithm achieved a good detection rate (GDR) of 94% and false detection rate (FDR) of 2.3% compared with the leading algorithm which only produced a GDR of 62% and FDR of 16%). In summary, the novel contribution of this thesis to the fields of time-frequency signal processing and biomedical engineering is the successful development and application of sophisticated algorithms based on adaptive time-frequency signal processing techniques to the solution of automatic newborn EEG seizure detection.
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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 methods for the automatic detection of newborn EEG seizure. This thesis is focused on the development of algorithms for the automatic detection of newborn EEG seizure using adaptive time-frequency signal processing. The assessment of newborn EEG seizure detection algorithms requires large datasets of nonseizure and seizure EEG which are not always readily available and often hard to acquire. This has led to the proposition of realistic models of newborn EEG which can be used to create large datasets for the evaluation and comparison of newborn EEG seizure detection algorithms. In this thesis, we develop two simulation methods which produce synthetic newborn EEG background and seizure. The simulation methods use nonlinear and time-frequency signal processing techniques to allow for the demonstrated nonlinear and nonstationary characteristics of the newborn EEG. Atomic decomposition techniques incorporating redundant time-frequency dictionaries are exciting new signal processing methods which deliver adaptive signal representations or approximations. In this thesis we have investigated two prominent atomic decomposition techniques, matching pursuit and basis pursuit, for their possible use in an automatic seizure detection algorithm. In our investigation, it was shown that matching pursuit generally provided the sparsest (i.e. most compact) approximation for various real and synthetic signals over a wide range of signal approximation levels. For this reason, we chose MP as our preferred atomic decomposition technique for this thesis. A new measure, referred to as structural complexity, which quantifes the level or degree of correlation between signal structures and the decomposition dictionary was proposed. Using the change in structural complexity, a generic method of detecting changes in signal structure was proposed. This detection methodology was then applied to the newborn EEG for the detection of state transition (i.e. nonseizure to seizure state) in the EEG signal. To optimize the seizure detection process, we developed a time-frequency dictionary that is coherent with the newborn EEG seizure state based on the time-frequency analysis of the newborn EEG seizure. It was shown that using the new coherent time-frequency dictionary and the change in structural complexity, we can detect the transition from nonseizure to seizure states in synthetic and real newborn EEG. Repetitive spiking in the EEG is a classic feature of newborn EEG seizure. Therefore, the automatic detection of spikes can be fundamental in the detection of newborn EEG seizure. The capacity of two adaptive time-frequency signal processing techniques to detect spikes was investigated. It was shown that a relationship between the EEG epoch length and the number of repetitive spikes governs the ability of both matching pursuit and adaptive spectrogram in detecting repetitive spikes. However, it was demonstrated that the law was less restrictive forth eadaptive spectrogram and it was shown to outperform matching pursuit in detecting repetitive spikes. The method of adapting the window length associated with the adaptive spectrogram used in this thesis was the maximum correlation criterion. It was observed that for the time instants where signal spikes occurred, the optimal window lengths selected by the maximum correlation criterion were small. Therefore, spike detection directly from the adaptive window optimization method was demonstrated and also shown to outperform matching pursuit. An automatic newborn EEG seizure detection algorithm was proposed based on the detection of repetitive spikes using the adaptive window optimization method. The algorithm shows excellent performance with real EEG data. A comparison of the proposed algorithm with four well documented newborn EEG seizure detection algorithms is provided. The results of the comparison show that the proposed algorithm has significantly better performance than the existing algorithms (i.e. Our proposed algorithm achieved a good detection rate (GDR) of 94% and false detection rate (FDR) of 2.3% compared with the leading algorithm which only produced a GDR of 62% and FDR of 16%). In summary, the novel contribution of this thesis to the fields of time-frequency signal processing and biomedical engineering is the successful development and application of sophisticated algorithms based on adaptive time-frequency signal processing techniques to the solution of automatic newborn EEG seizure detection.
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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 women with a high risk for intrauterine growth restriction and pre-eclampsia but does not directly provide information on fetal status. Umbilical artery Doppler has been shown to reduce perinatal mortality significantly in high-risk pregnancies (but not in low-risk women). Adding middle cerebral artery Doppler to umbilical artery Doppler does not increase accuracy for detecting adverse perinatal outcome. Ductus venosus Doppler demonstrates moderate value in diagnosing fetal compromise; it is not known whether its use adds any value to umbilical artery Doppler alone. Cardiotocography (CTG) reflects the interaction between the fetal brain and peripheral cardiovascular system. Prelabour routine use of CTG in low-risk pregnancies has not been proven to improve outcome; computerized CTG significantly reduces perinatal mortality in high-risk pregnancies. Monitoring the fetus during labour with intermittent auscultation has not been compared to no monitoring at all; when compared with CTG no difference in perinatal mortality or cerebral palsy has been noted. CTG does lower neonatal seizures and is accompanied by a statistically non-significant rise in caesarean delivery. Fetal blood sampling to detect fetal pH and base deficit lowers caesarean delivery rate and neonatal convulsions when used in adjunct to CTG. Determination of fetal scalp lactate has not been shown to have an effect on neonatal outcome or on the rate of instrumental deliveries but is less often hampered by technical failure than fetal scalp pH. Analysis of the ST segment of the fetal ECG (STAN®) in combination with CTG during labour results in fewer vaginal operative deliveries, less need for neonatal intensive care, and less use of fetal blood sampling during labour, without a change in fetal metabolic acidosis when compared to CTG alone.
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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, 322–25. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-5122-7_81.

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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, 93–113. Berlin, Heidelberg: 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, 43–64. Cham: 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, 343–58. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3014-2_17.

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De Vos, M., P. J. Cherian, W. Deburchgraeve, R. M. Swarte, P. Govaert, S. Van Huffel, and G. H. Visser. "Automated Neonatal Brain Monitoring." In Neonatal Monitoring Technologies, 244–61. 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 its user friendliness. A full EEG offers a more reliable window to study the ongoing activity in the newborn brain with high temporal and relatively good spatial resolution. However, the expertise required to register and interpret EEG is not available around the clock in the NICUs. For this purpose, automated monitoring devices have been developed, to assist neonatologists at the bedside and neurophysiologists in reviewing large amounts of monitoring data. The main topic of this chapter is automated detection of neonatal seizures and its possible impact in clinical practice. Three different detection approaches are reviewed: model-based, heuristic and classifier-based. Also a futuristic view on automated EEG analysis systems will be given.
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Azzopardi, Denis. "Introduction to the CFM and the Clinical Applications." In Neonatal Monitoring Technologies, 222–43. 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 neonatal hypoxic ischaemic encephalopathy, including in newborns receiving neuroprotective treatment with prolonged moderate hypothermia. The cerebral function monitor is also used for seizure detection and to monitor response to anticonvulsant therapies. Amplitude integrated electroencephalography compares well with standard electroencephalography when used to assess the severity of neonatal encephalopathy but a standard electroencephalogram is still required to provide additional important information about changes in frequency, and in the synchrony and distribution and other characteristics of cerebral cortical activity. The role of the amplitude integrated electroencephalogram to identify brain injury in preterm infants remains to be determined.
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Conference papers on the topic "Neonatal Seizure Detector"

1

"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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7

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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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.2006.4398496.

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Ansari, A. H., P. J. Cherian, A. Caicedo, M. De Vos, G. Naulaers, and S. Van Huffel. "Improved neonatal seizure detection using adaptive learning." In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2017. http://dx.doi.org/10.1109/embc.2017.8037441.

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Faul, S. "A novel automatic neonatal seizure detection system." In IEE Irish Signals and Systems Conference 2005. IEE, 2005. http://dx.doi.org/10.1049/cp:20050340.

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