title: Detection of ECG waveforms by neural networks.
abstract: In this study, ECG waveform detection was performed by using artificial neural networks (ANNs). Initially, the R peak of the QRS complex is detected, and then feature vectors are formed by using the amplitudes of the significant frequency components of the DFT spectrum. Grow and Learn (GAL) and Kohonen networks are comparatively investigated to detect four different ECG waveforms. The comparative performance results of GAL and Kohonen networks are reported.
authors.affiliation: Istanbul Technical University, Electrical & Electronics Engineering Department, Turkey.
authors.forename: Z
authors.initials: Z
authors.lastname: Dokur
authors.forename: T
authors.initials: T
authors.lastname: Olmez
authors.forename: E
authors.initials: E
authors.lastname: Yazgan
authors.forename: O K
authors.initials: OK
authors.lastname: Ersoy
mesh_terms: D002037:Bundle-Branch Block / Q000175:diagnosis; D004562:Electrocardiography; D006801:Humans; D016571:Neural Networks, Computer; D018879:Ventricular Premature Complexes / Q000175:diagnosis