WU-NEAT: A clinically validated, open-source MATLAB toolbox for limited-channel neonatal EEG analysis

Area of Science:

• Neonatal neurology
• Biomedical engineering
• Signal processing

Background:

• Limited-channel neonatal EEG research is constrained by a lack of accessible analytical tools.
• The Washington University-Neonatal EEG Analysis Toolbox (WU-NEAT) was developed to address this gap.
• WU-NEAT is an open-source, clinically validated package for MATLAB, offering commonly used EEG analysis tools.

Purpose of the Study:

• To introduce and validate the WU-NEAT toolbox for neonatal EEG analysis.
• To provide open-source access to essential EEG analytic algorithms.
• To facilitate collaborative research in neonatal EEG.

Main Methods:

• The toolbox includes algorithms for amplitude-integrated EEG (aEEG) and spectral edge frequency (SEF).
• aEEG algorithm validation involved 14 preterm/term infant recordings assessed by three experienced reviewers for background pattern classification, assessing inter/intra-rater reliability.
• SEF calculations were compared against a reference algorithm using Pearson's correlation coefficient.

Main Results:

• The aEEG algorithm demonstrated high reliability, with 100% intra-rater and 98% inter-rater reliability.
• The SEF calculations showed a strong correlation between WU-NEAT and the reference algorithm (mean±SD Pearson's r = 0.96±0.04).

Conclusions:

• WU-NEAT provides a clinically validated method for generating aEEG and calculating SEF from neonatal EEG data.
• The open-source nature of WU-NEAT promotes widespread adoption of device-independent, future-proof analytic algorithms.
• This accessibility is expected to significantly advance collaborative research in neonatal EEG analysis.