Time-Varying EEG Correlations Improve Automated Neonatal Seizure Detection

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2019-05-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
15
Series
International Journal of Neural Systems, Volume 29, issue 4
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 AUCSC: 0.933 IQR: 0.821-0.975, median AUCTFC: 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.
Description
| openaire: EC/H2020/656131/EU//APE
Keywords
Electroencephalography, neonatal seizure detection, nonstationary signal processing, support vector machines, time-frequency distributions
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Citation
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