Detection of Occult Paroxysmal Atrial Fibrillation

Med Biol Eng Comput. 2015 Apr;53(4):287-97. doi: 10.1007/s11517-014-1234-y. Epub 2014 Dec 14.

Abstract

This work introduces a novel approach to the detection of brief episodes of paroxysmal atrial fibrillation (PAF). The proposed detector is based on four parameters which characterize RR interval irregularity, P-wave absence, f-wave presence, and noise level, of which the latter three are determined from a signal produced by an echo state network. The parameters are used for fuzzy logic classification where the decisions involve information on prevailing signal quality; no training is required. The performance is evaluated on a large set of test signals with brief episodes of PAF. The results show that episodes with as few as five beats can be reliably detected with an accuracy of 0.88, compared to 0.82 for a detector based on rhythm information only (the coefficient of sample entropy); this difference in accuracy increases when atrial premature beats are present. The results also show that the performance remains essentially unchanged at noise levels up to [Formula: see text] RMS. It is concluded that the combination of information on ventricular activity, atrial activity, and noise leads to substantial improvement when detecting brief episodes of PAF.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Atrial Fibrillation / diagnosis*
  • Atrial Fibrillation / physiopathology
  • Electrocardiography
  • Fuzzy Logic
  • Humans
  • Signal Processing, Computer-Assisted*