Early Development of Sleep-wake Cycles in Premature Infants and Its Impact on Neurodevelopmental Outcome

Summary

Due to the development of neonatal intensive care the number of surviving premature infants increased significantly. The immature brain undergoes a fair amount of external stimuli, which have a great impact on later cognitive development. Increasingly data show, that a delayed emergence of sleep-wake-cycling in newborns can be the first sign of brain injury. Studies have shown that clearly defined sleep states can be identified from 31-32 weeks of gestation onwards. But a few studies show, that also extremely premature infants already show cyclical variations of the background pattern within amplitude-integrated EEG (aEEG= a time-compressed, simplified EEG) and conventional EEG. This might resemble early sleep-wake-states and their presence correlates to the integrity of the central nervous system, although no clearly defined "sleep states" according to the classical definition can be identified. Complex EEG analysis needs the use of automated methods to exclude personal bias and to ensure gestational age specific data analysis. The newly developed NLEO algorithm was specially designed for EEG analysis of premature infants. Conventional EEG within this study will be analyzed visually and with the automated algorithm. In our research project we will study the emergence of Sleep-wake-cycling in extremely premature infants and its impact on their neurodevelopmental outcome prospectively. The different sleep and wake states will be derived from analysis of the conventional Video-EEG, aEEG and polysomnographic measurements. Visual analysis will include assessment of amplitudes and frequencies as well as the latencies and durations of EEG-Bursts and Interburst intervals. The automated NLEO-algorithm will be firstly used for comparison with above described visual analysis and secondly to find regions of interest involved in the organization of these early sleep states. The aim of this study is first to understand and analyze in detail the emergence of sleep-wake cycling including its disturbances in premature infants and to compare automated NLEO algorithm to conventional visual analysis methods. Secondly to correlate neurodevelopmental outcome to the emergence of sleep-wake-cycling.

Conditions

• Sleep Disorders, Circadian Rhythm

Interventions

OTHER

aEEG and conventional EEG measurement

aEEG and conventional EEG measurement including video-polysomnography

Sponsors & Collaborators

• Austrian Science Fund (FWF)

  collaborator OTHER

• Medical University of Vienna

  lead OTHER

Principal Investigators

• Katrin Klebermass-Schrehof, MD · Medical University of Vienna

Study Design

Allocation

NA

Purpose

SUPPORTIVE_CARE

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

23 Weeks

Max Age

29 Weeks

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2012-02-29

Primary Completion

2014-12-31

Completion

2016-12-31

Countries

• Austria

Study Locations