2018
DOI: 10.1109/jbhi.2017.2750769
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Weighted Performance Metrics for Automatic Neonatal Seizure Detection Using Multiscored EEG Data

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. … Show more

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Cited by 16 publications
(17 citation statements)
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“…Another interesting aspect would involve including more human scorers and consequently applying more principled ways of combining their knowledge (e.g. see [29]).…”
Section: Discussionmentioning
confidence: 99%
“…Another interesting aspect would involve including more human scorers and consequently applying more principled ways of combining their knowledge (e.g. see [29]).…”
Section: Discussionmentioning
confidence: 99%
“…Furthermore, in Table 3, the epoch-based AUC, sensitivity, and specificity, as well as event-based good detection rate (GDR) and false alarm rate per hour (FAR), are reported. 62,63 Since the output of the heuristic algorithm is not continuous, Hermite spline interpolation was used to calculate the AUC. 64 For other metrics, in order to make the comparison simpler, the threshold of the CNN-RF was chosen where the sensitivities of CNN-RF and heuristic methods are equal (the horizontal dashed lines in Fig.…”
Section: Resultsmentioning
confidence: 99%
“…Fusion of various techniques such as autocorrelation, wavelet decomposition, and nonlinear energy operator (NLEO) has been shown to also improve the overall sensitivity of spike train seizures detection in more than 217 h of recordings from term neonates [34]. Very recent studies also show that heuristic algorithms can be developed to identify spike trains when the maximums of nonlinear energy components of the signal are compared to the background EEG activity, resulting in an overall good detection rate (GDR) of 95%, tested over 353 h recordings from 81 infants [35]. More recent work has demonstrated that a combinational scheme based on the convolutional neural networks (CNN) and random forest can help to automatically identify neonatal seizures in human babies with 77% overall accuracy [36].…”
Section: Related Workmentioning
confidence: 99%