A Multi-stage System for the Automated Detection of Epileptic Seizures in Neonatal EEG

This paper describes the design and test results of a 3-stage automated system for neonatal EEG seizure detection. Stage I of the system is the initial detection stage, and identifies overlapping 5-s segments of suspected seizure activity in each EEG channel. In Stage II, the detected segments from Stage I are spatiotemporally clustered to produce multi-channel candidate seizures. In Stage III, the candidate seizures are processed further using measures of quality and context-based rules to eliminate false candidates. False candidates due to artifacts and commonly occurring EEG background patterns such as bifrontal delta activity are also rejected. Seizures at least 10 s in duration are considered for reporting results. The testing data consisted of recordings of 28 seizure subjects (34 hrs of data) and 48 non-seizure subjects (87 hrs of data) obtained in the neonatal intensive care unit. The data were not edited to remove artifacts and were identical in every way to data normally processed visually. The system was able to detect seizures of widely varying morphology with an average detection sensitivity of Corresponding Author. John R. Glover, N308 Engineering Bldg. 1, University of Houston, Houston, TX 77204-4005. Phone: 713-743-4430, FAX: 713-743-4444. glover@uh.edu. NIH Public Access Author Manuscript J Clin Neurophysiol. Author manuscript; available in PMC 2010 August 1. Published in final edited form as: J Clin Neurophysiol. 2009 August ; 26(4): 218–226. doi:10.1097/WNP.0b013e3181b2f29d. N IH PA Athor M anscript N IH PA Athor M anscript N IH PA Athor M anscript almost 80% and a subject sensitivity of 96%, in comparison to a team of clinical neurophysiologists who had scored the same recordings. The average false detection rate obtained in non-seizure subjects was 0.74 per hr.