Neonatal EEG seizure detection using a new signal structural complexity measure based on matching pursuit decomposition with nonstationary dictionary. (September 2022)

• Record Type: Journal Article
• Authors: Khlif, Mohamed Salah; Mesbah, Mostefa; Colditz, Paul B.; Boashash, Boualem
• Is Part Of: Computer methods and programs in biomedicine. Volume 224(2022)
• Publication Date: 2022-09
• Subjects: Newborn seizure -- EEG -- Matching pursuit -- Time-frequency analysis -- Artifact removal; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107014
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Definition of a new signal complexity measure to discriminate between neonatal EEG seizure and non-seizure. Design of a coherent dictionary for neonatal EEG signal decomposition. Definition of data-dependent threshold for the classification of neonatal EEG. Design of effective artifact removal techniques. 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.