Brain dysmaturity index for automatic detection of high-risk infants

Holthausen, Klaus; Breidbach, Olaf; Scheidt, B; Frenzel, J

doi:10.1016/s0887-8994(99)00154-x

Cited by 32 publications

(13 citation statements)

References 11 publications

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“…The preferred methodological approach was the fast-Fourier transform analysis, studied initially with fullterm neonates (Eiselt et al, 2001;Field et al, 2002;Ktonas et al, 1995;Lehtonen et al, 1998;Witte et al, 1997), followed by more recent reports in preterm infants (Eisalt et al, 1997;Holthausen et al, 2000;Kuhle et al, 2001;Myers et al, 1997;Sawaguchi et al, 1996;Schramm et al, 2000;Vanhatalo et al, 2002). Similar calculations, based primarily on assumptions of stationarity, were also described for specific neonatal and infant risk groups for sudden infant death syndrome (Schechtman et al, 1995), apnea (Schramm et al, 2000), hyperbilirubinemia (Gurses et al, 2002), white matter necrosis (Inder et al, 2003), and asphyxia (Hellström-Westas, 1992), applying power analyses to one particular physiologic behavior with little attention to the multiple neuronal networks that contemporaneously express state transitions.…”

Section: Automated Neonatal State Detection Strategiesmentioning

confidence: 99%

Automated State Analyses: Proposed Applications to Neonatal Neurointensive Care

Scher¹,

Turnbull²,

Loparo³

et al. 2005

Journal of Clinical Neurophysiology

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The two principal challenges of neonatal physiologic monitoring device are: (1) the development of computational strategies that consider the rudimentary forms of neonatal sleep state especially for preterm infants and (2) any physiologic monitoring device for clinical applications in a neonatal intensive care setting must be small, portable, and user-friendly. Our multicenter neonatal sleep consortium has acquired more than 1,100 multihour EEG-sleep recordings on over 370 neonates, ranging from 24 to 44 weeks gestation. Each recording was visually-scored for state, arousals, movements, and rapid eye movements, which were used as templates when applying spectral analyses. The authors have defined a brain dysmaturity index to represent functional brain reorganization as the prenate matures to a full-term age; delayed or accelerated physiologic behaviors have been described for the preterm cohort when compared to the full-term group at the same postmenstrual age. Seven EEG-sleep measures comprise this index: quiet sleep percentage, sleep cycle length, rapid eye movements, arousals, spectral beta EEG energies, spectral EEG correlations, and a spectral measure of respiratory regularity. Linear and nonlinear computational algorithms are being developed to automate the computation of the dysmaturity index and to identify new feature types that correlate with dysmaturity. Automated neonatal sleep monitoring system can potentially improve neonatal neurointensive care by facilitating analyses of pervasive neonatal brain disorders expressed primarily as altered sleep state organization, and help predict altered developmental trajectories of children at higher risk for neurologic sequelae.

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Section: Automated Neonatal State Detection Strategiesmentioning

confidence: 99%

Automated State Analyses: Proposed Applications to Neonatal Neurointensive Care

Scher¹,

Turnbull²,

Loparo³

et al. 2005

Journal of Clinical Neurophysiology

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show abstract

“…Novel approaches to data analysis reveal new information about the behaviour of the neonatal EEG, and it may be possible to identify unique EEG biomarkers. A combination of signal processing and the more traditional visual analysis techniques will undoubtedly shed light onto our understanding of the developing brain for years to come [36,[100][101][102][103][104] . In addition, it is now possible to combine other physiological data and complimentary tools with EEG to provide a more comprehensive overview of neonatal brain health [105][106][107] .…”

Section: Resultsmentioning

confidence: 99%

EEG - A Valuable Biomarker of Brain Injury in Preterm Infants

Pavlidis¹,

Lloyd²,

Boylan³

2017

Dev Neurosci

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This review focuses on the role of electroencephalography (EEG) in monitoring abnormalities of preterm brain function. EEG features of the most common developmental brain injuries in preterm infants, including intraventricular haemorrhage, periventricular leukomalacia, and perinatal asphyxia, are described. We outline the most common EEG biomarkers associated with these injuries, namely seizures, positive rolandic sharp waves, EEG suppression/increased interburst intervals, mechanical delta brush activity, and other deformed EEG waveforms, asymmetries, and asynchronies. The increasing survival rate of preterm infants, in particular those that are very and extremely preterm, has led to a growing demand for a specific and shared characterization of the patterns related to adverse outcome in this unique population. This review includes abundant high-quality images of the EEG patterns seen in premature infants and will provide a valuable resource for everyone working in developmental neuroscience.

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“…Experts attempt to assess brain maturity by visual analysis of age-related patterns in electroencephalograms (EEG) recorded from sleeping newborns (Tharp 1990;Holthausen et al 2000). The analysis can take hours of expert work to confidently interpret sleep EEG, as the age-related patterns widely vary during sleep hours as well as between patients, and there are no regular rules for interpretation of these patterns (Cooper et al 2003).…”

Section: Introductionmentioning

confidence: 99%

Classification of newborn EEG maturity with Bayesian averaging over decision trees

Schetinin

Jakaite

2012

Expert Systems with Applications

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-EEG experts can assess a newborn's brain maturity by visual analysis of age-related patterns in sleep EEG. It is highly desirable to make the results of assessment most accurate and reliable. However, the expert analysis is limited in capability to provide the estimate of uncertainty in assessments. Bayesian inference has been shown providing the most accurate estimates of uncertainty by using Markov Chain Monte Carlo (MCMC) integration over the posterior distribution. The use of MCMC enables to approximate the desired distribution by sampling the areas of interests in which the density of distribution is high. In practice, the posterior distribution can be multimodal, and so that the existing MCMC techniques cannot provide the proportional sampling from the areas of interest. The lack of prior information makes MCMC integration more difficult when a model parameter space is large and cannot be explored in detail within a reasonable time. In particular, the lack of information about EEG feature importance can affect the results of Bayesian assessment of EEG maturity. In this paper we explore how the posterior information about EEG feature importance can be used to reduce a negative influence of disproportional sampling on the results of Bayesian assessment. We found that the MCMC integration tends to oversample the areas in which a model parameter space includes one or more features, the importance of which counted in terms of their posterior use is low. Using this finding, we proposed to cure the results of MCMC integration and then described the results of testing the proposed method on a set of sleep EEG recordings.

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Brain dysmaturity index for automatic detection of high-risk infants

Cited by 32 publications

References 11 publications

Automated State Analyses: Proposed Applications to Neonatal Neurointensive Care

Automated State Analyses: Proposed Applications to Neonatal Neurointensive Care

EEG - A Valuable Biomarker of Brain Injury in Preterm Infants

Classification of newborn EEG maturity with Bayesian averaging over decision trees

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