Toward a personalized real-time diagnosis in neonatal seizure detection
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Published Version
Date
2017-09-11
Authors
Temko, Andriy
Sarkar, Achintya K. R.
Boylan, Geraldine B.
Mathieson, Sean
Marnane, William P.
Lightbody, Gordon
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers
Published Version
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.
Description
Keywords
Neonatal , Seizure , Detection , Online adaptation
Citation
Temko, A., Sarkar, A. K., Boylan, G. B., Mathieson, S., Marnane, W. P. and Lightbody, G. (2017) 'Toward a personalized real-time diagnosis in neonatal seizure detection', IEEE Journal of Translational Engineering in Health and Medicine, 5, 2800414 (14pp). doi: 10.1109/JTEHM.2017.2737992