Clinical relevance of age-dependent EEG signatures in the detection of neonates at high risk for apnea

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

doi:10.1016/s0304-3940(99)00397-3

Cited by 19 publications

(21 citation statements)

References 10 publications

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“…Fast-Fourier transform was initially applied to studies of full-term neonates [108][109][110][111][112] followed by more recent reports in preterm infants [113][114][115][116][117][118][119][120][121][122] . These algorithms were also used to describe specific neonatal and infant risk groups, such as sudden infant death syndrome [123] , apnea [117,124] , hyperbilirubinemia [125] , white matter necrosis [126] , and asphyxia [127] . These studies applied power analyses to only one specific physiologic behavior.…”

Section: Computer-assisted Analyses Of Neonatal Eeg/sleep Represent Amentioning

confidence: 99%

Neonatal EEG/Sleep State Analyses: A Complex Phenotype of Developmental Neural Plasticity

Scher

Loparo

2009

Dev Neurosci

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Computer analyses of EEG/sleep states can be used as physiologic biomarkers of developmental neural plasticity. Frequency- and time-dependent signal processing strategies of cerebral and noncerebral measures can help test current theories of neuronal network maturation in terms of segregation and integration of short-distance versus long-distance neuronal connections throughout the neuroaxis. Specific phenotypic expressions of adaptive or maladaptive neuronal connectivity are proposed based on comparisons of whole-brain EEG/sleep resting states between preterm and full-term cohorts when developmental outcome measures are applied. Combined use of neurophysiological datasets with neuroimaging and genetic methodologies define endophenotypes that will more accurately diagnose children at risk for developmental disorders, as well as design appropriate neuroprotective interventions for the individual’s age and disease progress.

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Section: Computer-assisted Analyses Of Neonatal Eeg/sleep Represent Amentioning

confidence: 99%

Neonatal EEG/Sleep State Analyses: A Complex Phenotype of Developmental Neural Plasticity

Scher

Loparo

2009

Dev Neurosci

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“…Analytical descriptions of the evolution of neuronal networks allow us to characterize learning rules and complexity function by those parameters that could be formed out in describing internal system characteristics. At first, that is only a formal trick, which allows the omission of any external reference functions in the characterization of internal system dynamics (Holthausen and Breidbach 1999). Consequently, however, optimizations can be defined referring only to internal system characteristics.…”

Section: Network Semanticsmentioning

confidence: 99%

Neurosemantics, neurons and system theory

Breidbach

2007

Theory Biosci.

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Following the concept of internal representations, signal processing in a neuronal system has to be evaluated exclusively based on internal system characteristics. Thus, this approach omits the external observer as a control function for sensory integration. Instead, the configuration of the system and its computational performance are the effects of endogenous factors. Such self-referential operation is due to a strictly local computation in a network and, thereby, computations follow a set of rules that constitute the emergent behaviour of the system. These rules can be shown to correspond to a "logic" that is intrinsic to the system, an idea which provides the basis for neurosemantics.

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“…Abnormal brain development in newborns is a lifethreatening factor, and early diagnosis of such development is important for saving health and lives of newborns [1], [2]. Clinical assessment of the brain development is mainly carried out by analyzing the maturity-related features in sleep electroencephalograms (EEGs).…”

Section: Introductionmentioning

confidence: 99%

“…The use of such EEG features has been shown promising for assessment of EEG maturity [1], [15]. To cope with the non-stationarity of EEG the spectral analysis has been carried out within short, up to 10s, intervals [3].…”

Section: Introductionmentioning

confidence: 99%

Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity

Jakaite

Schetinin

Schult

2011

2011 24th International Symposium on Computer-Based Medical Systems (CBMS)

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Clinical relevance of age-dependent EEG signatures in the detection of neonates at high risk for apnea

Cited by 19 publications

References 10 publications

Neonatal EEG/Sleep State Analyses: A Complex Phenotype of Developmental Neural Plasticity

Neonatal EEG/Sleep State Analyses: A Complex Phenotype of Developmental Neural Plasticity

Neurosemantics, neurons and system theory

Feature extraction from electroencephalograms for Bayesian assessment of newborn brain maturity

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