Peer-Reviewed Journal Details
Mandatory Fields
Temko, A;Sarkar, AK;Boylan, GB;Mathieson, S;Marnane, WP;Lightbody, G
2017
January
Ieee Journal Of Translational Engineering In Health And Medicine-Jtehm
Toward a Personalized Real-Time Diagnosis in Neonatal Seizure Detection
Validated
WOS: 3 ()
Optional Fields
DETECTION ALGORITHM EEG CLASSIFIER LIKELIHOOD KNOWLEDGE MEDICINE INFANTS CURVES MODELS SYSTEM
5
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.
PISCATAWAY
2168-2372
10.1109/JTEHM.2017.2737992
Grant Details