Spectral Edge Frequency of the EEG in Healthy Neonates and Variation with Behavioural State

Bell, Angela H.; McClure, B G; McCullagh, Peter; McClelland, R. J.

doi:10.1159/000243390

Cited by 30 publications

(21 citation statements)

References 7 publications

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“…In contrast to the majority of articles (22,23,27,(32)(33)(34)(35)(36), we studied maturational changes of spectral power analysis quantitatively and automated on whole 4-h recordings, including several sleep-wake cycles. From a previous article using the same subjects, we observed periodic variation in background activity with alternating periods of discontinuity and continuity, suggestive for two to four sleep-wake cycles per 4-h recording (19).…”

Section: Discussionmentioning

confidence: 99%

Maturational Changes in Automated EEG Spectral Power Analysis in Preterm Infants

et al. 2011

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Our study aimed at automated power spectral analysis of the EEG in preterm infants to identify changes of spectral measures with maturation. Weekly (10 -20 montage) 4-h EEG recordings were performed in 18 preterm infants with GA Ͻ32 wk and normal neurological follow-up at 2 y, resulting in 79 recordings studied from 27 ϩ4 to 36 ϩ3 wk of postmenstrual age (PMA, GA ϩ postnatal age). Automated spectral analysis was performed on 4-h EEG recordings. The frequency spectrum was divided in delta 1 (0.5-1 Hz), delta 2 (1-4 Hz), theta (4 -8 Hz), alpha (8 -13 Hz), and beta (13-30 Hz) band. Absolute and relative power of each frequency band and spectral edge frequency were calculated. Maturational changes in spectral measures were observed most clearly in the centrotemporal channels. With advancing PMA, absolute powers of delta 1 to 2 and theta decreased. With advancing PMA, relative power of delta 1 decreased and relative powers of alpha and beta increased, respectively. In conclusion, with maturation, spectral analysis of the EEG showed a significant shift from the lower to the higher frequencies. Computer analysis of EEG will allow an objective and reproducible analysis for long-term prognosis and/or stratification of clinical treatment. (Pediatr Res 70: 529-534, 2011) A dvances in the care of very preterm infants have led to an increased survival (1). However, a considerable number of these infants experience neurological deficits later in life, even in the absence of neuroimaging abnormalities (2,3). The exact etiology of these developmental deficits remains to be clarified, but it is suggested that medical, environmental, and iatrogenic conditions may interfere with white matter development of the vulnerable preterm brain (4). Therefore, brain function monitoring in preterm infants during their stay in the NICU may be valuable in detecting conditions that interfere with brain development (5). It is a challenge to develop effective monitoring and therapeutic strategies to protect the preterm brain.The EEG is regarded as the gold standard in the assessment of cerebral function. Assessing changes in EEG are useful in the prediction of long-term outcome (6). Although the acute and chronic EEG changes are mainly nonspecific regarding type of damage, they correlate with later neurological and cognitive function (7). In preterm infants developing white matter damage, acute EEG findings include decreased continuity, lower amplitude of background activity, and epileptic seizure activity (8). The chronic EEG changes associated with white matter injury and abnormal neurological development include delayed maturation and disorganized pattern with the presence of abundant positive Rolandic sharp waves (9,10). In addition, EEG patterns of preterm infants change with postmenstrual age (PMA) (11,12). In the very preterm infant, the EEG background activity is characterized by discontinuity, instability, and fragmentation (13). The greater the prematurity, the more marked are these EEG aspects. These characteristics make the ...

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Section: Discussionmentioning

confidence: 99%

Maturational Changes in Automated EEG Spectral Power Analysis in Preterm Infants

et al. 2011

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“…The relative power of a band is the ratio of the absolute power of that band to the total power of all bands and expressed as a percentage. Spectral edge frequency is the frequency that delimits 95% of the total spectral power between 0.5 and 30 Hz (12). Asymmetry was calculated as the ratio of the total absolute power of the recordings from the left side (Fp1, C3, and O1) to the total absolute power of the recordings from the right side (Fp2, C4, and O2).…”

Section: Methodsmentioning

confidence: 99%

“…Bell et al (3) demonstrated a significant negative correlation between the absolute power of the signal below 1 Hz (the ␦ band) and gestational age in 60 infants between 29 and 42 wk gestation. The same authors also reported a significant positive correlation between the spectral edge frequency (the frequency below which 95% of the power resides) between 0.3 and 30 Hz and gestational age in 51 infants between 29 and 41 wk gestation (12). Others have studied sleep patterns and brain maturation using spectral energies in infants between 28 and 32 wk gestation (13)(14)(15).…”

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confidence: 90%

Spectral Analysis of Electroencephalography in Premature Newborn Infants: Normal Ranges

et al. 2005

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Continuous EEG monitoring has not been used widely in neonatal intensive care, especially in the care of extremely premature infants, probably in part because of a lack of a reliable quantitative method. The purpose of this study was to quantify the EEG of the very premature infants just after birth by using spectral analysis and to describe the characteristics of the spectral signal when infants were clinically stable. Digital EEG recordings were performed on 53 infants who were Յ30 wk gestation for 75 min each day during the first 4 d after birth. Artefact was rejected manually after visual inspection of trace. The EEG was analyzed by manual measurement of interburst interval and automatically by spectral analysis using Fast Fourier Transformation. Spectral analysis generated the normal ranges of the relative power of the ␦ (0.5-3.5 Hz), (4 -7.5 Hz), ␣ (8 -12.5 Hz), and ␤ (13-30 Hz) frequency bands, spectral edge frequency, and symmetry. The median (range) relative power of the ␦ band increased significantly from 68% (62-76%) on day 1 to 81% (72-89%) on day 4 (p ϭ 0.001). The interburst intervals became progressively shorter between days 1 [14s (10 -25)] and 3 [8s (6 -12)]; there were no significant differences between days 3 and 4. The relative power of the ␦ band seemed to be the most useful and repeatable spectral measurement for continuous long-term monitoring. However, automatic artefact rejection software needs to be developed before continuous quantitative EEG monitoring can be used in the neonatal intensive care environment. The hemodynamic status of extremely premature infants is particularly labile during the first days after birth. Intensive interventions for such infants are intended to maintain an adequate tissue oxygen supply, particularly to the brain. Because clinical management is aimed at preserving cerebral integrity in these infants, it would be helpful to have some form of continuous neuromonitoring to guide clinical interventions. EEG provides a useful, noninvasive technique for monitoring cerebral electrical activity, and advances in digital technology have made it possible to record high-quality EEG even in an electrically noisy intensive care environment and have done away with the necessity of using large amounts of recording paper. However, interpretation of the EEG is generally considered to be highly specialized, and this is at least one reason that continuous EEG monitoring is not used widely in neonatal intensive care.There are three major issues to be considered for the automatic and reliable analysis of the EEG signal of premature infants and for data to be presented in a manner that is useful to clinicians. First, the EEG of these infants consists predominantly of high-amplitude slow waves (1,2). Bell et al. (3) showed that the relative power of slow waves with frequency Ͻ1 Hz might be as high as 90% in infants at 28 wk gestation. A recent study using DC EEG on infants between 34 and 37 wk gestation found that the spontaneous EEG activity of sleeping preterm infants consiste...

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“…In the fetal baboon, the SEF was demonstrated to be a good discriminator of EEG patterns (8). This EEG parameter was also useful in measuring cerebral maturation in the newborn infant, demonstrating a significant correlation with gestational age (10). This study also demonstrated that the SEF was significantly higher in active sleep compared with quiet sleep.…”

Section: Real-time Spectral Analysis Of the Fetal Eegmentioning

confidence: 53%

“…However, the aEEG employs signal-processing techniques such as band-pass filtering and amplitude and time compression and rectification (26), adequate for screening purposes but insufficient to obtain detailed information regarding amplitude and frequency components embedded in the signal. In contrast, automated scanning of the EEG by spectral analysis as described in this study offers both accuracy and ease of interpretation and, when effectively displayed, provides a sensitive and reliable monitor of cortical activity (5,9,10). The system can also be applied postnatally, so that central neurologic adaptation can be evaluated both in preterm and term newborns.…”

Section: Real-time Spectral Analysis Of the Fetal Eegmentioning

confidence: 99%

Real-Time Spectral Analysis of the Fetal EEG: A New Approach to Monitoring Sleep States and Fetal Condition during Labor

2000

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Adverse perinatal events affecting cerebral functions are a major cause of neonatal mortality, morbidity, and long-term neurologic deficit. Intrapartum fetal EEG, which records fetal brain electrical activity, provides a monitoring modality for evaluating the fetal CNS during labor. In this study, we describe a new approach to such monitoring that is based on real-time spectral analysis of the fetal EEG during labor. Fourteen pregnant women with uncomplicated term pregnancies who went into labor participated in the study. Two suction-cup electrodes were applied to the fetal scalp at the occipitoparietal or parietal region after rupture of membranes. Real-time spectral analysis was used to determine the frequency and amplitude of the fetal EEG signal. The spectral edge frequency (SEF) was calculated as the frequency below which 90% of the power in the power spectrum resides. The average EEG amplitude and the SEF were displayed using the density spectral array technique. Fetal heart rate and intrauterine pressure were also measured. Two fundamental EEG patterns were identified: high-voltage slow activity and lowvoltage fast activity. The SEF was found to be an excellent index of cyclic EEG activity. Fetal heart rate demonstrated increased variability and an elevated baseline during low-voltage fast activity, whereas both parameters decreased during high-voltage slow activity. During episodes of variable decelerations in the fetal heart rate, a decrease in the SEF was observed, accompanied by an increased EEG voltage. The results obtained substantiate the presence of sleep cycles in the human fetus. This kind of cortical activity monitoring may enable rapid alertness to cerebral hypoxia and allow for prompt intervention, thereby decreasing the risk for birth asphyxia and subsequent brain damage. Abbreviations SEF, spectral edge frequency DSA, density spectral array FHR, fetal heart rate IUP, intrauterine pressure HVSA, high-voltage slow activity LVFA, low-voltage fast activity Normal function of the fetal CNS is of paramount importance to the quality of life after birth. Methods for assessing the normality or abnormality of the CNS function before birth are required to perform this sort of fetal evaluation. Fetal EEG, which records fetal brain electrical activity, can be used to measure CNS function. Methods for continuous monitoring of the fetal EEG were previously described, both in mature and premature fetuses (1, 2). Various EEG patterns were recognized, both transient and nontransient, that were found to be associated with intrapartum events and also with neurologic abnormalities at 1 y of age (3, 4). These observations were based on visual analysis of the fetal EEG records, which presented many methodologic as well as interpretative problems, precluding clinical acceptance of this monitoring modality. In recent years, automated methods for EEG scoring and coding for EEG states have been proposed (5-8). Such methods are objective and offer greater consistency of pattern recognition while facilitating processin...

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Spectral Edge Frequency of the EEG in Healthy Neonates and Variation with Behavioural State

Cited by 30 publications

References 7 publications

Maturational Changes in Automated EEG Spectral Power Analysis in Preterm Infants

Maturational Changes in Automated EEG Spectral Power Analysis in Preterm Infants

Spectral Analysis of Electroencephalography in Premature Newborn Infants: Normal Ranges

Real-Time Spectral Analysis of the Fetal EEG: A New Approach to Monitoring Sleep States and Fetal Condition during Labor

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