Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection

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http://hdl.handle.net/10138/311546

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Tapani , K T , Vanhatalo , S & Stevenson , N J 2019 , ' Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection ' , International Journal of Neural Systems , vol. 29 , no. 4 , 1850030 . https://doi.org/10.1142/S0129065718500302

Title: Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection
Author: Tapani, Karoliina T.; Vanhatalo, Sampsa; Stevenson, Nathan J.
Contributor: University of Helsinki, Department of Neurosciences
University of Helsinki, Kliinisen neurofysiologian yksikkö
University of Helsinki, Clinicum
Date: 2019-05
Language: eng
Number of pages: 15
Belongs to series: International Journal of Neural Systems
ISSN: 0129-0657
URI: http://hdl.handle.net/10138/311546
Abstract: The aim of this study was to develop methods for detecting the nonstationary periodic characteristics of neonatal electroencephalographic (EEG) seizures by adapting estimates of the correlation both in the time (spike correlation; SC) and time-frequency domain (time-frequency correlation; TFC). These measures were incorporated into a seizure detection algorithm (SDA) based on a support vector machine to detect periods of seizure and nonseizure. The performance of these nonstationary correlation measures was evaluated using EEG recordings from 79 term neonates annotated by three human experts. The proposed measures were highly discriminative for seizure detection (median AUC(SC): 0.933 IQR: 0.821-0.975, median AUC(TFC): 0.883 IQR: 0.707-0.931). The resultant SDA applied to multi-channel recordings had a median AUC of 0.988 (IQR: 0.931-0.998) when compared to consensus annotations, outperformed two state-of-the-art SDAs (p <0.001) and was noninferior to the human expert for 73/79 of neonates.
Subject: Electroencephalography
support vector machines
time-frequency distributions
neonatal seizure detection
nonstationary signal processing
STATISTICAL TURING TEST
NEWBORN EEG
BRAIN-INJURY
FREQUENCY
MODEL
ALGORITHM
AGREEMENT
INFANTS
SYSTEM
SERIES
3112 Neurosciences
3124 Neurology and psychiatry
113 Computer and information sciences
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