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. 2017 Sep 11:5:2800414.
doi: 10.1109/JTEHM.2017.2737992. eCollection 2017.

Toward a Personalized Real-Time Diagnosis in Neonatal Seizure Detection

Andriy Temko  1 , Achintya Kr Sarkar  2 , Geraldine B Boylan  3 , Sean Mathieson  4 , William P Marnane  1 , Gordon Lightbody  1
Affiliations

Toward a Personalized Real-Time Diagnosis in Neonatal Seizure Detection

Andriy Temko et al. IEEE J Transl Eng Health Med. .

Abstract

The problem of creating a personalized seizure detection algorithm for newborns is tackled in this paper. A probabilistic framework for semi-supervised adaptation of a generic patient-independent neonatal seizure detector is proposed. A system that is based on a combination of patient-adaptive (generative) and patient-independent (discriminative) classifiers is designed and evaluated on a large database of unedited continuous multichannel neonatal EEG recordings of over 800 h in duration. It is shown that an improvement in the detection of neonatal seizures over the course of long EEG recordings is achievable with on-the-fly incorporation of patient-specific EEG characteristics. In the clinical setting, the employment of the developed system will maintain a seizure detection rate at 70% while halving the number of false detections per hour, from 0.4 to 0.2 FD/h. This is the first study to propose the use of online adaptation without clinical labels, to build a personalized diagnostic system for the detection of neonatal seizures.

Keywords: Neonatal; detection; online adaptation; seizure.

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Figures

FIGURE 1.
FIGURE 1.
From generic to personalized neonatal seizure detector. The adaptation of the generic model can be performed on the fly. The optional clinical feedback in the testing stage can be used to purify the hypothesised decisions.
FIGURE 2.
FIGURE 2.
The ensemble of patient-adaptive GMM and patient-independent SVM SDAs.
FIGURE 3.
FIGURE 3.
(a) Four different weight functions; here x = PSVM for providing weights for updates to seizure class GMM, x = (1-PSVM) for updating non-seizure class GMM. (b) An example of a sampled sigmoid weighting using 5 clusters.
FIGURE 4.
FIGURE 4.
Performance of a SDA – measured as the area under the ROC curve.
FIGURE 5.
FIGURE 5.
Performance of the baseline SVM and adaptive fusion system measured as AUC (top) and AUC90 (bottom) as a function of time.
FIGURE 6.
FIGURE 6.
Relative improvement in AUC90 between the PA-FUSION and PI-FUSION systems. ‘*’ indicates statistical significance at á set to 1%.
FIGURE 7.
FIGURE 7.
Real-time functioning of the developed personalised SDA algorithm. A) Probabilistic output of patient-adaptive and patient-independent SDAs with superimposed ground truth. B) 30s of seizure activity detected with higher probability with adaptive fusion system. C) 30s of non-seizure activity detected with lower probability.
FIGURE 8.
FIGURE 8.
Neonatal seizure detection system diagram.
See this image and copyright information in PMC

References

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