Cerebral Function Monitoring: A New Scoring System for the Evaluation of Brain Maturation in Neonates

Burdjalov, Vladimir F.; Baumgart, Stephen; Spitzer, Alan R.

doi:10.1542/peds.112.4.855

Cited by 217 publications

(224 citation statements)

References 24 publications

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“…These observations may explain the only moderate extent of agreement for determination of cycles among two observers examining the aEEG for this study. These considerations have led to tier classification of cycle appearance, including mature, imminent or immature, interrupted and absence of cycles (13,26). Our results indicate that many infants at 34-wk gestation have cycles with an immature or interrupted appearance.…”

Section: Discussionmentioning

confidence: 73%

“…This limits gestational age related changes in aEEG (12)(13)(14) and focuses on the lower gestational age of late preterm infants who are known to have a higher frequency of morbidities which may require intensive care compared with infants at Articles Sommers et al 35-36 wk (9,15-17). Thus, accurate gestational age was critical and infants were enrolled only if a first trimester sonogram was available (18).…”

Section: Discussionmentioning

confidence: 99%

“…Cycles represent the repetitive changes between continuous and discontinuous EEG activity as described by Kidokoro et al (19). Cycle type was prospectively defined based on published criteria (12)(13)(14) and are described below (Figure 4). Cycles were visually identified and Analyze was used in cases of uncertainty to establish cycle criteria.…”

Section: Amplitude Integrated Eeg Acquisition and Analysismentioning

confidence: 99%

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Neurological maturation of late preterm infants at 34 wk assessed by amplitude integrated electroencephalogram

et al. 2013

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Background: This study tested if measures of central nervous system (CNS) immaturity reflected by amplitude integrated electroencephalogram (aEEG) and associated clinical morbidities are determinants of length of hospitalization among late preterm infants born at 34 wk. Methods: This was a prospective cohort study of infants with a gestational age of 34 wk 0-6 d who had a single aEEG recording acquired over 6 h in a neonatal intensive care unit within 72 h of birth (n = 80). Infants were followed for predefined morbidities (classified as CNS or non-CNS) and length of hospitalization (determined by the clinical care team). aEEG variables were correlated with length of hospitalization. results: Eighty infants were enrolled and 75 aEEG recordings were analyzed. The average length of hospitalization was 10.4 ± 7.2 d (range 3-46 d). The total number of cycles recorded in the first 72 h following birth were inversely correlated with the length of hospitalization (r 2 = 0.44, P < 0.001). Kaplan-Meier curves indicated that morbidities consistent with neurological immaturity were associated with a longer length of hospitalization (P < 0.001). conclusion: Neurological maturation as indicated by aEEG and specific clinical morbidities is an important determinant of length of hospitalization among late-preterm infants.

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Section: Discussionmentioning

confidence: 73%

Section: Discussionmentioning

confidence: 99%

Section: Amplitude Integrated Eeg Acquisition and Analysismentioning

confidence: 99%

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Neurological maturation of late preterm infants at 34 wk assessed by amplitude integrated electroencephalogram

et al. 2013

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“…There are more frequent 'bursts', and the raw tracing of the EEG signal may show periods of relatively low voltage with sudden but infrequent bursts of high activity. Burdjalov et al 40 developed a quantifiable scoring system for brain maturation based on aEEG. Using four distinct variablesFcontinuity, cyclicity, bandwidth and lower border amplitudeFscores from 0 to 13 were obtained and compared with gestational ages (GAs).…”

Section: Introductionmentioning

confidence: 99%

Using amplitude-integrated EEG in neonatal intensive care

Tao

Mathur

2010

J Perinatol

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The implementation of amplitude-integrated electroencephalography (aEEG) has enhanced the neurological monitoring of critically ill infants. Limited channel leads are applied to the patient and data are displayed in a semilogarithmic, time-compressed scale. Several classifications are currently in use to describe patient tracings, incorporating voltage criteria, pattern recognition, cyclicity, and the presence or absence of seizures. In term neonates, aEEG has been used to determine the prognosis and treatment for those affected by hypoxic-ischemic encephalopathy, seizures, meningitis and even congenital heart disease. Its application as inclusion criteria for therapeutic hypothermia remains controversial. In preterm infants, normative values and patterns corresponding to gestational age are being established. As these standards emerge, the predictive value of aEEG increases, especially in the setting of preterm brain injury and intraventricular hemorrhage. The sensitivity and specificity of aEEG are enhanced by the display of a simultaneous raw EEG, which aids interpretation. Caution must be taken when using and interpreting this tool in conjunction with certain medications and in the setting of less experienced staff. Continuing efforts at developing software that can aid seizure detection and background classification will enhance the bedside utility of this tool.

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“…Unfortunately, the amplitude of the aEEG margins, which serves as an important discriminating index, usually is measured semisubjectively with the naked eye from the voltage scale on printed paper [6,7]. In most cases, a line is drawn manually through the upper and lower margins of the aEEG tracing, with half of the voltage peaks (for the upper margin) or troughs (for the lower margin) below the line and half above [3,[8][9][10][11][12][13]. Therefore, potential bias may be introduced in the assessed amplitudes.…”

Section: Introductionmentioning

confidence: 99%

Calculation of compact amplitude-integrated EEG tracing and upper and lower margins using raw EEG data

Zhang¹,

Ding²

2013

Health

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Amplitude-integrated EEG (aEEG) is a popular method for monitoring cerebral function. Although various commercial aEEG recorders have been produced, a detailed aEEG algorithm currently is not available. The upper and lower margins in the aEEG tracing are the discriminating features for data inspection and tracing classification. However, most aEEG devices require that these margins be measured semi-subjectively. This paper proposes a step-by-step signal-processing method to calculate a compact aEEG tracing and the upper/lower margin using raw EEG data. The high accuracy of the algorithm was verified by comparison with a recognized commercial aEEG device based on a representative testing dataset composed of 72 aEEG data. The introduced digital algorithm achieved compact aEEG tracing with a small data size. Moreover, the algorithm precisely represented the upper and lower margins in the tracing for objective data interpretation. The described method should facilitate aEEG signal processing and further establish the clinical and experimental application of aEEG methods.

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Cerebral Function Monitoring: A New Scoring System for the Evaluation of Brain Maturation in Neonates

Cited by 217 publications

References 24 publications

Neurological maturation of late preterm infants at 34 wk assessed by amplitude integrated electroencephalogram

Neurological maturation of late preterm infants at 34 wk assessed by amplitude integrated electroencephalogram

Using amplitude-integrated EEG in neonatal intensive care

Calculation of compact amplitude-integrated EEG tracing and upper and lower margins using raw EEG data

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