Robustness of support vector machine-based classification of heart rate signals

Argyro Kampouraki; Christophoros Nikou; George Manis

doi:10.1109/IEMBS.2006.260550

Robustness of support vector machine-based classification of heart rate signals

Conf Proc IEEE Eng Med Biol Soc. 2006:2006:2159-62. doi: 10.1109/IEMBS.2006.260550.

Authors

Argyro Kampouraki¹, Christophoros Nikou, George Manis

Affiliation

¹ Dept. of Comput. Sci., Ioannina Univ., PO Box 1186, 45110 Ioannina, Greece.

PMID: 17945696
DOI: 10.1109/IEMBS.2006.260550

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.

Publication types

Evaluation Study

MeSH terms

Algorithms
Arrhythmias, Cardiac / diagnosis*
Arrhythmias, Cardiac / physiopathology*
Artificial Intelligence*
Diagnosis, Computer-Assisted / methods*
Electrocardiography / methods*
Heart Rate*
Humans
Pattern Recognition, Automated / methods*
Reproducibility of Results
Sensitivity and Specificity