title: Neural network models for biosignal analysis.
abstract: Signal analysis provides important clues for diagnosis of disease in many arenas, particularly in cardiology. While each result may give good diagnostic information, a comprehensive decision model requires the combination of results. In the work described here, a neural network model is used to combine various features obtained through signal analysis. The original model is based on electrocardiogram (ECG analysis). This model is extended in two ways: the neural network model is expanded to include other clinical parameters in addition to the ECG and the method is generalized to include other biosignals.
authors.affiliation: California Univ., San Francisco, CA 93701, USA. mcohen@fresno.ucsf.edu
authors.forename: Maurice E
authors.initials: ME
authors.lastname: Cohen
authors.forename: Donna L
authors.initials: DL
authors.lastname: Hudson
mesh_terms: D000818:Animals; D001145:Arrhythmias, Cardiac / Q000175:diagnosis; D001698:Biomedical Engineering; D003936:Diagnosis, Computer-Assisted; D004562:Electrocardiography / Q000706:statistics & numerical data*; D015716:Electrocardiography, Ambulatory / Q000706:statistics & numerical data; D004569:Electroencephalography / Q000706:statistics & numerical data; D006439:Hemodynamics; D006801:Humans; D016571:Neural Networks, Computer; D017711:Nonlinear Dynamics; D012815:Signal Processing, Computer-Assisted