title: Feature extraction in time-frequency signal analysis by means of matched wavelets as a feature generator.
abstract: The goal of presented work was to compare the usage of standard basic wave let function like e.g. bio-orthogonal or dbn with the optimized wavelet created to the best match analyzing ECG signals in the context of P-wave and atrial fibrillation detection. A library of clinical expert evaluated typical atrial fibrillation evolutions was created as a database for optimal matched wavelet construction. Whole data set consisting of 40 cases with long term ECG recording s were divided into learning and verifying set for the multilayer perceptron neural network used as a classifier structure. Compared with other wavelet filters, the matched wavelet was able to improve classifier performance for a given ECG signals in terms of the Sensitivity and Specificity measures.
authors.affiliation: Silesian University of Technology, Institute of Electronics, 16 Akademicka St Gliwice, Poland. pkostka@ polsl.pl
authors.forename: Pawel S
authors.initials: PS
authors.lastname: Kostka
authors.forename: Ewaryst J
authors.initials: EJ
authors.lastname: Tkacz
mesh_terms: D000465:Algorithms; D001281:Atrial Fibrillation / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D058067:Wavelet Analysis