A computer-aided detection of EEG seizures in infants: a singular-spectrum approach and performance comparison.
This paper presents a scalp electroencephalogram (EEG) seizure detection scheme based on singular spectrum analysis (SSA) and Rissanen minimum description length (MDL) model-order selection (SSA-MDL). Preprocessing of the signals allows for the drastic reduction of the number of false alarms. Statistical performance comparison with seizure detection schemes of Gotman et al. and Liu et al. is performed on both synthetic data and real EEG seizures. Monte Carlo simulations based on synthetic infant EEG seizure data reveals some detection drawbacks on a large variety of seizure waveforms. Detection using both Monte Carlo and four real infant scalp EEG signals shows the superiority of the SSA-MDL method with an average good detection rate of >93% and false detection rate <4%.
Celka
Patrick
P
Signal Processing Research Centre, Queensland University of Technology, Brisbane, Australia. patrick.celka@csem.ch
Colditz
Paul
P
D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D007223:Infant; D008959:Models, Neurological; D009010:Monte Carlo Method; D012640:Seizures / Q000175:diagnosis*; D012815:Signal Processing, Computer-Assisted; D013057:Spectrum Analysis; D013269:Stochastic Processes