Defining the gap between electrographic seizure burden, clinical expression and staff recognition of neonatal seizures

Murray, Deirdre M.; Boylan, Geraldine B.; Ali, Imran I.; Ryan, C. Anthony; Murphy, Brendan P.; Connolly, Seán

doi:10.1136/adc.2005.086314

Cited by 389 publications

(297 citation statements)

References 22 publications

(27 reference statements)

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“…The EEG can be used to monitor sick newborn patients who are admitted to the neonatal intensive care unit (NICU) and are at risk of developing neonatal seizures (Murray et al, 2008). These neonatal seizures are clinically defined as paroxysmal alterations in neurological function, i.e.…”

Section: Neonatal Seizure Detectionmentioning

confidence: 99%

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

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Type of publicationDoctoral thesis Rights AbstractThe electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of-use and mobility are important.Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours.However, as typical EEG signals are of the order of microvolts (µV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance.This thesis therefore, contributes to the further improvement of automated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science.The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning i classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG.Subsequently, additional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head-movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement artefact detection.The artefact detecti...

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Section: Neonatal Seizure Detectionmentioning

confidence: 99%

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

O’Regan¹

2018

Preprint

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“…In one study comparing clinical observations and video electroencephalography (EEG), two-thirds of the clinical manifestations were unrecognized or misinterpreted by experienced neonatal staff. 13,14 Standard video EEG has been considered ideal for the accurate diagnosis of neonatal seizures, but is not practicable in most neonatal units because of a lack of availability of the necessary equipment, and also because clinicians are not trained to interpret neonatal EEG. The use of amplitude-integrated electroencephalography (aEEG) has become standard practice in most neonatal units in developed countries, and it is part of the standard protocol in HIE management.…”

Section: Diagnosis (Of Clinical and Electrographic Seizures)mentioning

confidence: 99%

Neonatal seizures in hypoxic–ischaemic encephalopathy – risks and benefits of anticonvulsant therapy

Shetty

2015

Develop Med Child Neuro

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The risk of seizures is at its highest during the neonatal period, and the most common cause of neonatal seizures is hypoxic–ischaemic encephalopathy (HIE). This enhanced vulnerability is caused by an imbalance in the expression of receptors for excitatory and inhibitory neurotransmission, which is age dependent. There has been progress in detecting the electrophysiological abnormalities associated with seizures using amplitude‐integrated electroencephalography (aEEG). Data from animal studies indicate a variety of risk factors for seizures, but there are limited clinical data looking at the long‐term neurodevelopmental consequences of seizures alone. Neonatal seizures are also associated with increased risk of further epileptic seizures; however, it is less clear whether or not this results from an underlying pathology, and whether or not seizures confer additional risk. Phenobarbital and phenytoin are still the first‐line antiepileptic drugs (AEDs) used to treat neonatal seizures, although they are effective in only one‐third of affected infants. Furthermore, based on findings from animal studies, there are concerns regarding the risks associated with using these AEDs. Clinicians face a difficult challenge because, although seizures can be easily identified using aEEG, treatment options are limited, and there are uncertainties regarding treatment outcomes. There is a need to obtain long‐term follow‐up data, comparing groups of infants treated with or without current therapies. If these analyses indicate a definite benefit of treating neonatal seizures, then novel therapeutic approaches should be developed.

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“…It is therefore critical to recognise neonatal seizures in their early stages to allow timely medical intervention. Clinical assessment of seizures in the neonates is difficult and unreliable as many neonatal seizures occur either in the absence of any clinical signs or accompanied by only subtle ones [8,9]. The clinical diagnosis is further hampered by the frequent administration of sedative or paralytic agents to the newborn patients in neonatal intensive care units.…”

Section: Introductionmentioning

confidence: 99%

Instantaneous frequency based newborn EEG seizure characterisation

Mesbah

O’Toole

Colditz

et al. 2012

EURASIP J. Adv. Signal Process.

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The electroencephalogram (EEG), used to noninvasively monitor brain activity, remains the most reliable tool in the diagnosis of neonatal seizures. Due to their nonstationary and multi-component nature, newborn EEG seizures are better represented in the joint time-frequency domain than in either the time domain or the frequency domain. Characterising newborn EEG seizure nonstationarities helps to better understand their time-varying nature and, therefore, allow developing efficient signal processing methods for both modelling and seizure detection and classification. In this article, we used the instantaneous frequency (IF) extracted from a time-frequency distribution to characterise newborn EEG seizures. We fitted four frequency modulated (FM) models to the extracted IFs, namely a linear FM, a piecewise-linear FM, a sinusoidal FM, and a hyperbolic FM. Using a database of 30-s EEG seizure epochs acquired from 35 newborns, we were able to show that, depending on EEG channel, the sinusoidal and piecewise-linear FM models best fitted 80-98% of seizure epochs. To further characterise the EEG seizures, we calculated the mean frequency and frequency span of the extracted IFs. We showed that in the majority of the cases (>95%), the mean frequency resides in the 0.6-3 Hz band with a frequency span of 0.2-1 Hz. In terms of the frequency of occurrence of the four seizure models, the statistical analysis showed that there is no significant difference (p = 0.332) between the two hemispheres. The results also indicate that there is no significant differences between the two hemispheres in terms of the mean frequency (p = 0.186) and the frequency span (p = 0.302).

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Defining the gap between electrographic seizure burden, clinical expression and staff recognition of neonatal seizures

Cited by 389 publications

References 22 publications

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

Artefact Detection and Removal Algorithms for EEG Diagnostic Systems

Neonatal seizures in hypoxic–ischaemic encephalopathy – risks and benefits of anticonvulsant therapy

Instantaneous frequency based newborn EEG seizure characterisation

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