Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy

TL;DR

This study validates an SVM-based neonatal seizure detection algorithm, demonstrating its generalizability, non-inferiority to human experts, and clinical relevance in seizure burden assessment.

Contribution

It introduces a validated neonatal SDA that approaches human expert performance and demonstrates clinical utility with improved training methods.

Findings

Algorithm performance remained consistent across datasets.

Re-training improved non-inferiority to human annotations.

Seizure burden assessment accuracy was 89-93%.

Abstract

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.