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Published on: September 6, 2017
Evaluation of maturity of sleep states in preterm infants using conventional and amplitude-integrated
Ye Han1, Na Fu1, Jingjing Liang1
1Department of Pediatrics, Peking University People's Hospital, Beijing, China.
Insights
Conventional electroencephalography (cEEG) and amplitude-integrated electroencephalography (aEEG) can estimate sleep state maturity in preterm infants. cEEG demonstrated higher accuracy than aEEG in determining postmenstrual age (PMA) for sleep maturity.
Area of Science:
- Neonatal neurology
- Sleep neurophysiology
- Developmental neuroscience
Background:
- Assessing neurodevelopmental maturity in preterm infants is crucial.
- Sleep state maturity is a key indicator of neurological development.
- Conventional electroencephalography (cEEG) and amplitude-integrated electroencephalography (aEEG) are commonly used for monitoring infant brain activity.
Purpose of the Study:
- To evaluate the accuracy of cEEG and aEEG in estimating sleep state maturity in neurologically unimpaired preterm infants.
- To compare the performance of cEEG versus aEEG for this estimation.
Main Methods:
- Fifty-one preterm infants underwent simultaneous cEEG-polygraphy and aEEG monitoring.
- Sleep state maturity was assessed by estimating postmenstrual age (PMA) from cEEG patterns and aEEG cycling scores.
- Estimations were performed blindly and compared to chronological PMA (cPMA).
Main Results:
- Both cEEG and aEEG showed a significant correlation between estimated PMA (ePMA) and cPMA.
- The estimation gap (ePMA - cPMA) was within ±2 weeks for both methods.
- cEEG achieved a higher accuracy (96% within ±1 week) compared to aEEG (88% within ±1 week).
Conclusions:
- Sleep state maturity can be reliably estimated using both cEEG and aEEG in preterm infants.
- Both methods provide estimations within two weeks of the actual cPMA.
- cEEG offers superior accuracy for assessing sleep state maturity compared to aEEG.
Objective:
To determine whether sleep state maturity can be estimated accurately using conventional electroencephalography (cEEG) or amplitude-integrated electroencephalography (aEEG) features concerning sleep in neurologically unimpaired preterm infants.
Methods:
A total of 51 preterm infants were monitored with cEEG-polygraphy and simultaneous aEEG. Sleep state maturity of EEG corresponded to specific postmenstrual age (PMA). PMA on cEEG was blindly estimated according to cEEG patterns (indicated as background continuity, frequencies, and voltages) as well as developmental markers in specific states. PMA on aEEG was blindly estimated based on the cycling score (cycling representing sleep state transitions) according to a pre-established scoring system.
Results:
A total of 51 EEGs recorded between 32 and 37 weeks PMA were analysed. A significant relationship between estimated PMA (ePMA) and actual chronological PMA (cPMA) was shown by linear regression both on cEEG (r = 0.93, β = 0.98, 95% confidence interval (CI) 0.87-1.09, p < 0.001) and aEEG (r = 0.85, β = 0.83, 95% CI 0.69-0.98, p < 0.001). The estimation gap (defined as ePMA minus cPMA) was between -2 and +2 weeks both on cEEG and aEEG. The percentage of estimation gap between -1 and +1 weeks was 96% for cEEG, which was higher than the estimate of 88% for aEEG.
Conclusion:
Estimated maturity of sleep state was well correlated with cPMA both on cEEG and aEEG. PMA corresponding to state maturity could be estimated within two weeks of actual cPMA using either of these two tools. However, cEEG had higher accuracy compared with aEEG in the evaluation of sleep state maturity.
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