Neonatal EEG seizure detection using a new signal structural complexity measure based on matching pursuit decomposition with nonstationary dictionary

Mohamed Salah Khlif, Mostefa Mesbah*, Paul B. Colditz, Boualem Boashash

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background and objective: In newborns, it is often difficult to accurately differentiate between seizure and non-seizure based solely on clinical manifestations. This highlights the importance of electroencephalogram (EEG) in the recognition and management of neonatal seizures. This paper proposes an effective algorithm for the detection of neonatal seizure using multichannel EEG. Methods: Neonatal EEG changes morphology as it alternates between seizure and non-seizure states. A new signal complexity measure based on matching pursuit (MP) decomposition is proposed and used to detect transitions between these two states. The new measure, referred to as weighted structural complexity (WSC), was used for the detection of seizures in 30 newborn EEG records. Multiple IIR filters and an MP-based filter were designed and used to remove artifacts from the EEG data. Geometrical correlation between the EEG data channels was applied to reduce the number of false detections caused by remnant artifacts. The seizure detector's performance was assessed using several epoch-based (e.g., accuracy) and event-based (GDR = good detection rate and FD/h = false detections per hour) metrics. Results: Compared to the neurologist marking, the proposed detector was able to detect EEG seizures with 94% accuracy, 90.9% GDR, and 0.14 FD/h (95% CI: [0.06, 0.34]). Conclusions: The high performance of the MP-based detector may have significant implications for the accurate diagnosis of neonatal seizures and the appropriate use of anticonvulsants and ongoing clinical assessment and care of the newborn.

Original languageEnglish
Article number107014
JournalComputer Methods and Programs in Biomedicine
Volume224
DOIs
Publication statusPublished - Sep 2022

Keywords

  • Artifact removal
  • EEG
  • Matching pursuit
  • Newborn seizure
  • Time-frequency analysis

ASJC Scopus subject areas

  • Software
  • Health Informatics
  • Computer Science Applications

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