Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy
Loading...
Access rights
openAccess
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
View publication in the Research portal
View/Open full text file from the Research portal
Other link related to publication
Date
2022-06
Major/Subject
Mcode
Degree programme
Language
en
Pages
10
1-10
1-10
Series
COMPUTERS IN BIOLOGY AND MEDICINE, Volume 145
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 (Δκ = 0.077, 95% CI: −0.002-0.232, n = 18). The SDA assessment of seizure burden had an accuracy ranging from 89 to 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.Description
| openaire: EC/H2020/813483/EU//INFANS | openaire: EC/H2020/656131/EU//APE Funding Information: Funding for this research was provided by the Finnish Cultural Foundation ( 00181077 ), Finnish Pediatric Foundation, the Finnish Academy ( 313242 , 288220 , 321235 ), Aivosäätiö, Neuroscience Center at University of Helsinki , Helsinki University Hospital, Helsinki University Hospital Research Funds, HUS and University of Helsinki Researcher position, Sigrid Juselius Foundation and European Union's Horizon 2020 Research and Innovation Programme ( H2020-MCSA-ITN-813483 and H2020-MCSA-IF-656131 ). The study sponsors had no involvement in the study design, nor in the collection, analysis or interpretation of data. Additionally we acknowledge the computational resources provided by the Aalto Science-IT project. Publisher Copyright: © 2022 The Authors
Keywords
EEG monitoring, neonatal EEG, Neonatal intensive care unit, Seizure, Support vector machine
Other note
Citation
Tapani , K T , Nevalainen , P , Vanhatalo , S & Stevenson , N J 2022 , ' Validating an SVM-based neonatal seizure detection algorithm for generalizability, non-inferiority and clinical efficacy ' , Computers in Biology and Medicine , vol. 145 , 105399 , pp. 1-10 . https://doi.org/10.1016/j.compbiomed.2022.105399
