  Goal: Limited-channel EEG research in neonates is hindered by lack of open,
accessible analytic tools. To overcome this limitation, we have created the
Washington University- Neonatal EEG Analysis Toolbox (WU-NEAT), containing two
of the most commonly used tools, provided in an open-source,
clinically-validated package running within MATLAB. Methods: The first
algorithm is the amplitude-integrated EEG (aEEG), which is generated by
filtering, rectifying and time-compressing the original EEG recording, with
subsequent semi-logarithmic display. The second algorithm is the spectral edge
frequency (SEF), calculated as the critical frequency below which a user-
defined proportion of the EEG spectral power is located. The aEEG algorithm was
validated by three experienced reviewers. Reviewers evaluated aEEG recordings
of fourteen preterm/term infants, displayed twice in random order, once using a
reference algorithm and again using the WU-NEAT aEEG algorithm. Using standard
methodology, reviewers assigned a background pattern classification.
Inter/intra-rater reliability was assessed. For the SEF, calculations were made
using the same fourteen recordings, first with the reference and then with the
WU-NEAT algorithm. Results were compared using Pearson's correlation
coefficient. Results: For the aEEG algorithm, intra- and inter-rater
reliability was 100% and 98%, respectively. For the SEF, the mean (SD) Pearson
correlation coefficient between algorithms was 0.96 (0.04). Conclusion: We have
demonstrated a clinically-validated toolbox for generating the aEEG as well as
calculating the SEF from EEG data. Open-source access will enable widespread
use of common analytic algorithms which are device-independent and not subject
to obsolescence, thereby facilitating future collaborative research in neonatal
EEG.
