  Neonatal seizure detection algorithms (SDA) are approaching the benchmark of
human expert annotation. Measures of algorithm generalizability and
non-inferiority as well as measures of clinical efficacy are needed to assess
the full scope of neonatal SDA performance. We validated our neonatal SDA on an
independent data set of 28 neonates. Generalizability was tested by comparing
the performance of the original training set (cross-validation) to its
performance on the validation set. Non-inferiority was tested by assessing
inter-observer agreement between combinations of SDA and two human expert
annotations. Clinical efficacy was tested by comparing how the SDA and human
experts quantified seizure burden and identified clinically significant periods
of seizure activity in the EEG. Algorithm performance was consistent between
training and validation sets with no significant worsening in AUC (p>0.05, n
=28). SDA output was inferior to the annotation of the human expert, however,
re-training with an increased diversity of data resulted in non-inferior
performance ($\Delta\kappa$=0.077, 95% CI: -0.002-0.232, n=18). The SDA
assessment of seizure burden had an accuracy ranging from 89-93%, and 87% for
identifying periods of clinical interest. The proposed SDA is approaching human
equivalence and provides a clinically relevant interpretation of the EEG.
