Weighted Performance Metrics for Automatic Neonatal Seizure Detection Using Multiscored EEG Data

Amir Hossein Ansari, Perumpillichira Joseph Cherian, Alexander Caicedo Dorado, Katrien Jansen, Anneleen Dereymaeker, Leen De Wispelaere, Charlotte Dielman, Jan Vervisch, Paul Govaert, Maarten De Vos, Gunnar Naulaers, Sabine Van Huffel

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

Abstract

In neonatal intensive care units, there is a need for around the clock monitoring of electroencephalogram (EEG), especially for recognizing seizures. An automated seizure detector with an acceptable performance can partly fill this need. In order to develop a detector, an extensive dataset labeled by experts is needed. However, accurately defining neonatal seizures on EEG is a challenge, especially when seizure discharges do not meet exact definitions of repetitiveness or evolution in amplitude and frequency. When several readers score seizures independently, disagreement can be high. Commonly used metrics such as good detection rate (GDR) and false alarm rate (FAR) derived from data scored by multiple raters have their limitations. Therefore, new metrics are needed to measure the performance with respect to the different labels. In this paper, instead of defining the labels by consensus or majority voting, popular metrics including GDR, FAR, positive predictive value, sensitivity, specificity, and selectivity are modified such that they can take different scores into account. To this end, 353 hours of EEG data containing seizures from 81 neonates were visually scored by a clinical neurophysiologist, and then processed by an automated seizure detector. The scored seizures were mixed with false detections of an automated seizure detector and were relabeled by three independent EEG readers. Then, all labels were used in the proposed performance metrics and the result was compared with the majority voting technique and showed higher accuracy and robustness for the proposed metrics. Results were confirmed using a bootstrapping test.

Original languageEnglish (US)
Pages (from-to)1114-1123
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume22
Issue number4
DOIs
StatePublished - Jul 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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