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Analysis of electrocardiographic changes in partial epileptic patients by combining eigenvector methods and support vector machines
Author(s) -
Übeyli Elif Derya
Publication year - 2009
Publication title -
expert systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.365
H-Index - 38
eISSN - 1468-0394
pISSN - 0266-4720
DOI - 10.1111/j.1468-0394.2009.00478.x
Subject(s) - support vector machine , computer science , pattern recognition (psychology) , eigenvalues and eigenvectors , artificial intelligence , feature extraction , electroencephalography , data mining , medicine , physics , quantum mechanics , psychiatry
Abstract: In the present study, the diagnostic accuracy of support vector machines (SVMs) on electrocardiogram (ECG) signals is evaluated. Two types of ECG beats (normal and partial epilepsy) were obtained from the Physiobank database. Decision making was performed in two stages: feature extraction by eigenvector methods and classification using the SVM trained on the extracted features. The present research demonstrates that the power levels of the power spectral densities obtained by eigenvector methods are features which represent the ECG signals well and SVMs trained on these features achieve high classification accuracies.