title: Automated peak decomposition of evoked potential signals using Wavelet Transform singularity detection.
abstract: Averaged evoked potential (EP) signals are automatically decomposed into sets of peaks that may be characterised using sparse Wavelet Transform coefficients. The Wavelet Transform singularity detection technique is employed to convert Electroencephalograph (EEG) data into sets of singularities. A peak detection algorithm matches singularity pairs into sets of peaks. A single EEG epoch is then represented by a separable set of peaks. The approximation parameters classify the size and shape of the peak.
authors.affiliation: Royal Melbourne Institute of Technology (RMIT) University, Melbourne, VIC, Australia. s3029091@student.rmit.edu.au
authors.forename: Conor G
authors.initials: CG
authors.lastname: McCooey
authors.forename: Dinesh
authors.initials: D
authors.lastname: Kumar
mesh_terms: D001331:Automation; D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods; D001330:Electronic Data Processing; D004867:Equipment Design; D005071:Evoked Potentials; D005583:Fourier Analysis; D006801:Humans; D015233:Models, Statistical; D008991:Monitoring, Physiologic / Q000295:instrumentation* / Q000379:methods; D012044:Regression Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted