Abstract
External electrical cardioversion has been used as a therapeutic option to terminate atrial fibrillation (AF) and restore sinus rhythm (SR). However, identifying patients who would benefit from this therapy is still an active area of research. In this study, we develop new time-frequency features to characterize the atrial activity (AA) and to predict the success of electrical cardioversion therapy by identifying the AF patients who will maintain SR in the long term. New features are extracted from the surface AA using a matching pursuit (MP) decomposition with various combinations of wavelet families. The performance of the features is validated using a dataset of AF patients who underwent electrical cardioversion therapy. Results indicate that the developed features are significantly (p-value <0.05) correlated with SR maintenance which suggests that the MP decomposition captures detailed morphological information of AA that may potentially be used to guide the therapy of AF patients.
| Original language | English |
|---|---|
| Title of host publication | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 5498-5501 |
| Number of pages | 4 |
| ISBN (Print) | 9781424479290 |
| DOIs | |
| Publication status | Published - Nov 2 2014 |
| Externally published | Yes |
| Event | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States Duration: Aug 26 2014 → Aug 30 2014 |
Other
| Other | 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 |
|---|---|
| Country | United States |
| City | Chicago |
| Period | 8/26/14 → 8/30/14 |
Fingerprint
ASJC Scopus subject areas
- Health Informatics
- Computer Science Applications
- Biomedical Engineering
Cite this
Developing time-frequency features for prediction of the recurrence of atrial fibrillation after electrical cardioversion therapy. / Sterling, Mark; Huang, David T.; Ghoraani, Behnaz.
2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014. Institute of Electrical and Electronics Engineers Inc., 2014. p. 5498-5501 6944871.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Developing time-frequency features for prediction of the recurrence of atrial fibrillation after electrical cardioversion therapy
AU - Sterling, Mark
AU - Huang, David T.
AU - Ghoraani, Behnaz
PY - 2014/11/2
Y1 - 2014/11/2
N2 - External electrical cardioversion has been used as a therapeutic option to terminate atrial fibrillation (AF) and restore sinus rhythm (SR). However, identifying patients who would benefit from this therapy is still an active area of research. In this study, we develop new time-frequency features to characterize the atrial activity (AA) and to predict the success of electrical cardioversion therapy by identifying the AF patients who will maintain SR in the long term. New features are extracted from the surface AA using a matching pursuit (MP) decomposition with various combinations of wavelet families. The performance of the features is validated using a dataset of AF patients who underwent electrical cardioversion therapy. Results indicate that the developed features are significantly (p-value <0.05) correlated with SR maintenance which suggests that the MP decomposition captures detailed morphological information of AA that may potentially be used to guide the therapy of AF patients.
AB - External electrical cardioversion has been used as a therapeutic option to terminate atrial fibrillation (AF) and restore sinus rhythm (SR). However, identifying patients who would benefit from this therapy is still an active area of research. In this study, we develop new time-frequency features to characterize the atrial activity (AA) and to predict the success of electrical cardioversion therapy by identifying the AF patients who will maintain SR in the long term. New features are extracted from the surface AA using a matching pursuit (MP) decomposition with various combinations of wavelet families. The performance of the features is validated using a dataset of AF patients who underwent electrical cardioversion therapy. Results indicate that the developed features are significantly (p-value <0.05) correlated with SR maintenance which suggests that the MP decomposition captures detailed morphological information of AA that may potentially be used to guide the therapy of AF patients.
UR - http://www.scopus.com/inward/record.url?scp=84929494119&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84929494119&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2014.6944871
DO - 10.1109/EMBC.2014.6944871
M3 - Conference contribution
SN - 9781424479290
SP - 5498
EP - 5501
BT - 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -