title: Robustness of support vector machine-based classification of heart rate signals.
abstract: In this study, we discuss the use of support vector machine (SVM) learning to classify heart rate signals. Each signal is represented by an attribute vector containing a set of statistical measures for the respective signal. At first, the SVM classifier is trained by data (attribute vectors) with known ground truth. Then, the classifier learnt parameters can be used for the categorization of new signals not belonging to the training set. We have experimented with both real and artificial signals and the SVM classifier performs very well even with signals exhibiting very low signal to noise ratio which is not the case for other standard methods proposed by the literature.
authors.affiliation: Dept. of Comput. Sci., Ioannina Univ., PO Box 1186, 45110 Ioannina, Greece.
authors.forename: Argyro
authors.initials: A
authors.lastname: Kampouraki
authors.forename: Christophoros
authors.initials: C
authors.lastname: Nikou
authors.forename: George
authors.initials: G
authors.lastname: Manis
mesh_terms: D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology*; D001185:Artificial Intelligence; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity