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Application of frequency and sample entropy to discriminate long-term recordings of paroxysmal and persistent atrial fibrillation

Författare:
Publiceringsår: 2010
Språk: Engelska
Sidor: 4558-4561
Publikation/Tidskrift/Serie: Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Dokumenttyp: Konferensbidrag

Sammanfattning

Atrial fibrillation (AF) is the most common arrhythmia in clinical practice. At an early stage of the disease, AF may terminate spontaneously and is then referred to as paroxysmal AF. On the other hand, when external intervention is required for the arrhythmia to terminate, it is referred to as persistent AF. In this work, a method to discriminate between paroxysmal and persistent AF in the long-term ECGs is presented. The dominant frequency as well as the organization of the atrial activity are employed to characterize AF. The dominant atrial frequency (DAF) is estimated using hidden Markov model based frequency tracking, and organization is estimated by the sample entropy of the main atrial wave (MAW) and the first two harmonics, respectively. Long-term variations in DAF and organization from 50 ECG recordings were evaluated, showing that episodes of paroxysmal AF were consistently associated with lower DAF and organization of the MAW and the harmonics, than was persistent AF. Discrimination of paroxysmal and persistent AF resulted in classification rates of 84.1±26.1%, thus suggesting that it possible to discriminate between paroxysmal and persistent AF in patients without previously known AF history.

Disputation

Nyckelord

  • Technology and Engineering
  • diseases
  • electrocardiography
  • hidden Markov models
  • physiological models
  • ECG
  • arrhythmia
  • atrial fibrillation
  • dominant atrial frequency
  • entropy
  • main atrial wave

Övriga

Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
2010-09-04
Buenos Aires, Argentina
Published
Yes
  • Signal Processing
  • ISSN: 1557-170X
  • ISBN: 978-1-4244-4123-5

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