Estimation of f-wave Dominant Frequency Using a Voting Scheme
(2022) 2022 Computing in Cardiology, CinC 2022- Abstract
Atrial fibrillation (AF) is the most common heart arrhythmia, characterized by the presence of fibrillatory waves (f-waves) in the ECG. We introduce a voting scheme to estimate the dominant atrial frequency (DAF) of f-waves. Methods: We analysed a subset of Holter recordings obtained from the University of Virginia AF Database. 100 Holter recordings with manually annotated AF events, resulting in a total 363 AF events lasting more than 1 min. The f-waves were extracted using four different template subtraction (TS) algorithms and the DAF was estimated from the first 1-min window of each AF event. A random forest classifier was used. We hypothesized that better extraction of the f-wave meant better AF/non-AF classification using the DAF... (More)
Atrial fibrillation (AF) is the most common heart arrhythmia, characterized by the presence of fibrillatory waves (f-waves) in the ECG. We introduce a voting scheme to estimate the dominant atrial frequency (DAF) of f-waves. Methods: We analysed a subset of Holter recordings obtained from the University of Virginia AF Database. 100 Holter recordings with manually annotated AF events, resulting in a total 363 AF events lasting more than 1 min. The f-waves were extracted using four different template subtraction (TS) algorithms and the DAF was estimated from the first 1-min window of each AF event. A random forest classifier was used. We hypothesized that better extraction of the f-wave meant better AF/non-AF classification using the DAF as the single input feature of the RF model. Results: Performance on the test set, expressed in terms of AF/non-AF classification, was the best when the DAF was computed computed the three best-performing extraction methods. Using these three algorithms in a voting scheme, the classifier obtained AUC=0.60 and the DAFs were mostly spread around 6 Hz, 5.66 (4.83-7.47). Conclusions: This study has two novel contributions: (1) a method for assessing the performance of f-wave extraction algorithms, and (2) a voting scheme for improved DAF estimation.
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- author
- Biton, Shany LU ; Suleiman, Mahmoud ; Moshe, Noam Ben ; Sornmo, Leif LU and Behar, Joachim A.
- organization
- publishing date
- 2022
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Computing in Cardiology, CinC 2022
- publisher
- IEEE Computer Society
- conference name
- 2022 Computing in Cardiology, CinC 2022
- conference location
- Tampere, Finland
- conference dates
- 2022-09-04 - 2022-09-07
- external identifiers
-
- scopus:85152901246
- ISBN
- 9798350300970
- DOI
- 10.22489/CinC.2022.059
- language
- English
- LU publication?
- yes
- id
- 16c2a22a-40b5-4b1d-bed6-b0682f4163a1
- date added to LUP
- 2023-08-15 10:51:10
- date last changed
- 2025-02-09 05:38:33
@inproceedings{16c2a22a-40b5-4b1d-bed6-b0682f4163a1,
abstract = {{<p>Atrial fibrillation (AF) is the most common heart arrhythmia, characterized by the presence of fibrillatory waves (f-waves) in the ECG. We introduce a voting scheme to estimate the dominant atrial frequency (DAF) of f-waves. Methods: We analysed a subset of Holter recordings obtained from the University of Virginia AF Database. 100 Holter recordings with manually annotated AF events, resulting in a total 363 AF events lasting more than 1 min. The f-waves were extracted using four different template subtraction (TS) algorithms and the DAF was estimated from the first 1-min window of each AF event. A random forest classifier was used. We hypothesized that better extraction of the f-wave meant better AF/non-AF classification using the DAF as the single input feature of the RF model. Results: Performance on the test set, expressed in terms of AF/non-AF classification, was the best when the DAF was computed computed the three best-performing extraction methods. Using these three algorithms in a voting scheme, the classifier obtained AUC=0.60 and the DAFs were mostly spread around 6 Hz, 5.66 (4.83-7.47). Conclusions: This study has two novel contributions: (1) a method for assessing the performance of f-wave extraction algorithms, and (2) a voting scheme for improved DAF estimation.</p>}},
author = {{Biton, Shany and Suleiman, Mahmoud and Moshe, Noam Ben and Sornmo, Leif and Behar, Joachim A.}},
booktitle = {{Computing in Cardiology, CinC 2022}},
isbn = {{9798350300970}},
language = {{eng}},
publisher = {{IEEE Computer Society}},
title = {{Estimation of f-wave Dominant Frequency Using a Voting Scheme}},
url = {{http://dx.doi.org/10.22489/CinC.2022.059}},
doi = {{10.22489/CinC.2022.059}},
year = {{2022}},
}