Support vector black-box interpretation in ventricular arrhythmia discrimination

A new discrimination algorithm, based on the analysis of ventricular electrogram (EGM) onset, was proposed in order to discriminate between supraventricular and ventricular tachycardias (SVTs and VTs) in implantable cardioverter defibrillators (ICDs). Due to the absence of a detailed statistical mod...

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Bibliographic Details
Published in:IEEE engineering in medicine and biology magazine 2002-01, Vol.21 (1), p.27-35
Main Authors: Rojo-Alvarez, J.L., Arenal-Maiz, A., Artes-Rodriguez, A.
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Backpropagation algorithms
Bundle of His - physiopathology
Cardiology
Covariance matrix
Diagnosis, Differential
Electrocardiography - methods
Electrocardiography - statistics & numerical data
Evaluation Studies as Topic
Feedback
Kernel
Machine learning
Mathematical model
Models, Cardiovascular
Models, Statistical
Neural Networks (Computer)
Pattern Recognition, Automated
Sensitivity and Specificity
Strontium
Studies
Support vector machine classification
Support vector machines
Tachycardia, Supraventricular - diagnosis
Tachycardia, Supraventricular - physiopathology
Tachycardia, Ventricular - diagnosis
Tachycardia, Ventricular - physiopathology
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Summary:A new discrimination algorithm, based on the analysis of ventricular electrogram (EGM) onset, was proposed in order to discriminate between supraventricular and ventricular tachycardias (SVTs and VTs) in implantable cardioverter defibrillators (ICDs). Due to the absence of a detailed statistical model for the ventricular activation, this algorithm was based on a support vector method (SVM) learning machine plus bootstrap resampling to avoid overfitting. This SVM classifier was trained with available arrhythmia episodes, so that it can be viewed as containing a statistical model for the differential diagnosis. However, the black-box model character of any learning machine presents problems in a clinical environment. A solution is the extraction of the statistical information enclosed in the black-box model. The SVM could be appropriate for this purpose, given that the support vectors represent the critical samples for the classification task. In this article we propose two SVM-oriented analyses and their use in building two new differential diagnosis algorithms based on the ventricular EGM onset criterion.
ISSN:0739-5175
1937-4186
DOI:10.1109/51.993191