Organization tracking of long-term atrial fibrillation recordings: differences between paroxysmal and persistent episodes
Author
Summary, in English
In this work, a method for non-invasive assessment of
AF organization has been applied to discriminating between
paroxysmal and long-term persistent AF episodes.
Following extraction of the atrial activity (AA) signal,
the dominant atrial frequency (DAF) of the AA was computed
based on a hidden Markov model. Finally, the main
atrial wave (MAW) was obtained by bandpass filtering
centered on the DAF, thus providing a time series suitable
for AF organization estimation with sample entropy
(SampEn). The performance of the method was evaluated
on 24-h Holter recordings with long-term changes
in AF organization. The results showed that episodes of
paroxysmal AF (0.06930.0147) were consistently associated
with lower SampEn than episodes with persistent
AF (0.10560.0146). Moreover, 94.2% of 1-min segments
with paroxysmal AF and 88.6% of 1-min segments with
persistent AF could be correctly classified based on Samp-
En information, thus making it possible to classify longterm
recordings of patients without AF history.
AF organization has been applied to discriminating between
paroxysmal and long-term persistent AF episodes.
Following extraction of the atrial activity (AA) signal,
the dominant atrial frequency (DAF) of the AA was computed
based on a hidden Markov model. Finally, the main
atrial wave (MAW) was obtained by bandpass filtering
centered on the DAF, thus providing a time series suitable
for AF organization estimation with sample entropy
(SampEn). The performance of the method was evaluated
on 24-h Holter recordings with long-term changes
in AF organization. The results showed that episodes of
paroxysmal AF (0.06930.0147) were consistently associated
with lower SampEn than episodes with persistent
AF (0.10560.0146). Moreover, 94.2% of 1-min segments
with paroxysmal AF and 88.6% of 1-min segments with
persistent AF could be correctly classified based on Samp-
En information, thus making it possible to classify longterm
recordings of patients without AF history.
Publishing year
2009
Language
English
Pages
509-512
Publication/Series
[Host publication title missing]
Volume
36
Document type
Conference paper
Publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
Topic
- Electrical Engineering, Electronic Engineering, Information Engineering
Conference name
36th Annual Computers in Cardiology Conference, 2009
Conference date
2009-09-13 - 2009-09-16
Conference place
Park City, UT, United States
Status
Published
Research group
- Signal Processing