A support vector machine for predicting spontaneous termination of paroxysmal atrial fibrillation episodes

JD Diaz, C Gonzalez, OJ Escalona

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)
4 Downloads (Pure)

Abstract

The aim of this work is to predict the spontaneous termination of atrial fibrillation (AF) episodes. The database includes three record groups: non-terminating AF (N), AF that terminates one minute after recording end (S), and AF that terminates immediately after recording end (T). A first goal consisted on separating N from T group records (event 1), and a second, for separating S from T records (event 2). A Support Vector Machine was used for the classification problem. For event 1, four indexes were extracted: the atrial fibrillatory frequency (AFF) and the mean, standard deviation, and approximate entropy of RR intervals. For event 2, the AFF, the energy of the 3-7 Hz and 7-11 Hz bands, from the ten and five final seconds of the records, were used. The groups were divided in two sets: learning and test. For event 1, a 100% in learning, and 86.66% in test set were correctly classified. For the event 2, we classified 100% in the learning, and 80% in the test set.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages949-952
Number of pages4
Volume33
ISBN (Print)978-1-4244-2532-7
Publication statusPublished - 15 Dec 2006
EventComputers in Cardiology 2006, Valencia, Spain - Valencia, Spain
Duration: 15 Dec 2006 → …

Conference

ConferenceComputers in Cardiology 2006, Valencia, Spain
Period15/12/06 → …

Keywords

  • Cardiology
  • Learning systems
  • Standards
  • Support vector machines
  • Testing
  • Vectors
  • Approximate entropy
  • Atrial fibrillation
  • Paroxysmal atrial fibrillation
  • R-R interval
  • Standard deviation
  • medical signal processing
  • signal classification.

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