Hierarchical cardiac-rhythm classification based on electrocardiogram morphology
Sopic, Dionisije ; De Giovanni, Elisabetta ; Aminifar, Amir LU
and Atienza, David
(2017)
44th Computing in Cardiology Conference, CinC 2017
In Computing in Cardiology
44.
p.1-4
- Abstract
Atrial Fibrillation (AF) is a type of cardiac arrhythmia that significantly increases the risk of stroke and heart failure. In general, in the case of patients affected by AF, their electrocardiogram (ECG) shows a typical pattern of irregular RR intervals and abnormal P waves. However, discriminating AF from a normal sinus rhythm or from other types of rhythms remains a challenging problem today. Methods: We analyze the database of PhysioNet/Computing in Cardiology Challenge 2017 to validate our heart rhythm classification technique. The database contains short-term ECG recordings, labelled as normal sinus rhythm, AF, other types of rhythm, and noise. We extract different morphology-based features of ECG signals, and we design a... (More)
Atrial Fibrillation (AF) is a type of cardiac arrhythmia that significantly increases the risk of stroke and heart failure. In general, in the case of patients affected by AF, their electrocardiogram (ECG) shows a typical pattern of irregular RR intervals and abnormal P waves. However, discriminating AF from a normal sinus rhythm or from other types of rhythms remains a challenging problem today. Methods: We analyze the database of PhysioNet/Computing in Cardiology Challenge 2017 to validate our heart rhythm classification technique. The database contains short-term ECG recordings, labelled as normal sinus rhythm, AF, other types of rhythm, and noise. We extract different morphology-based features of ECG signals, and we design a multiclass classifier based on error-correcting output codes, along with a random forest classifier for binary decision making. Results: We test the performance of our classifiers based on the F1 score of each class and the average F1 score of all the classes. The final F1 score obtained on the hidden test set of challenge is 80%. Conclusions: Our results show that our classifier is robust and that it is able to discriminate AF from normal sinus, other rhythms, and noise, based on the morphology of the ECG signal.
(Less)
- author
- Sopic, Dionisije
; De Giovanni, Elisabetta
; Aminifar, Amir
LU
and Atienza, David
- publishing date
- 2017
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Computing in Cardiology 2017
- series title
- Computing in Cardiology
- volume
- 44
- pages
- 4 pages
- publisher
- IEEE - Institute of Electrical and Electronics Engineers Inc.
- conference name
- 44th Computing in Cardiology Conference, CinC 2017
- conference location
- Rennes, France
- conference dates
- 2017-09-24 - 2017-09-27
- external identifiers
-
- scopus:85045127717
- ISSN
- 2325-8861
- ISBN
- 978-1-5386-6630-2
- DOI
- 10.22489/CinC.2017.343-119
- language
- English
- LU publication?
- no
- additional info
- Publisher Copyright: © 2017 IEEE Computer Society. All rights reserved.
- id
- cd7927ec-aef4-4cb8-a521-20c0860a9a77
- date added to LUP
- 2024-06-25 13:39:59
- date last changed
- 2025-04-04 14:02:56
@inproceedings{cd7927ec-aef4-4cb8-a521-20c0860a9a77,
abstract = {{<p>Atrial Fibrillation (AF) is a type of cardiac arrhythmia that significantly increases the risk of stroke and heart failure. In general, in the case of patients affected by AF, their electrocardiogram (ECG) shows a typical pattern of irregular RR intervals and abnormal P waves. However, discriminating AF from a normal sinus rhythm or from other types of rhythms remains a challenging problem today. Methods: We analyze the database of PhysioNet/Computing in Cardiology Challenge 2017 to validate our heart rhythm classification technique. The database contains short-term ECG recordings, labelled as normal sinus rhythm, AF, other types of rhythm, and noise. We extract different morphology-based features of ECG signals, and we design a multiclass classifier based on error-correcting output codes, along with a random forest classifier for binary decision making. Results: We test the performance of our classifiers based on the F<sub>1</sub> score of each class and the average F<sub>1</sub> score of all the classes. The final F<sub>1</sub> score obtained on the hidden test set of challenge is 80%. Conclusions: Our results show that our classifier is robust and that it is able to discriminate AF from normal sinus, other rhythms, and noise, based on the morphology of the ECG signal.</p>}},
author = {{Sopic, Dionisije and De Giovanni, Elisabetta and Aminifar, Amir and Atienza, David}},
booktitle = {{Computing in Cardiology 2017}},
isbn = {{978-1-5386-6630-2}},
issn = {{2325-8861}},
language = {{eng}},
pages = {{1--4}},
publisher = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
series = {{Computing in Cardiology}},
title = {{Hierarchical cardiac-rhythm classification based on electrocardiogram morphology}},
url = {{http://dx.doi.org/10.22489/CinC.2017.343-119}},
doi = {{10.22489/CinC.2017.343-119}},
volume = {{44}},
year = {{2017}},
}