QRS Complexes Classification Using Dictionary Learning and Pietra Index
Author | Baali H. |
Author | Djelouat H. |
Author | Amira A. |
Author | Bensaali F. |
Author | Zhai X. |
Available date | 2020-02-05T08:53:35Z |
Publication Date | 2018 |
Publication Name | Proceedings - 2017 IEEE International Conference on Internet of Things, IEEE Green Computing and Communications, IEEE Cyber, Physical and Social Computing, IEEE Smart Data, iThings-GreenCom-CPSCom-SmartData 2017 |
Publication Name | Joint 10th IEEE International Conference on Internet of Things, iThings 2017, 13th IEEE International Conference on Green Computing and Communications, GreenCom 2017, 10th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2017 and the 3rd IEEE International Conference on Smart Data, Smart Data 2017 |
Resource | Scopus |
ISBN | 9.78E+12 |
Abstract | In this study, an algorithm for arrhythmia classification that conforms to the recommended practice by the Association for the Advancement of Medical Instrumentation (AAMI) is presented. Our approach efficiently exploits the inherent sparse representation of the electrocardiogram (ECG) signals. It involves, first, designing a separate dictionary for each Arrhythmia class. To this end, the alternating direction method of multipliers (ADMM) and the K-SVD, a dictionary learning algorithm based on singular value decomposition (SVD) approach, are applied. Sparse representations, based on new designed dictionaries, of each new test QRS complex are then calculated and assigned to the class associated with the largest Pietra Index (PI) afterwards. Our experiments showed promising results with accuracies ranging between 80 % and 100 %. 2017 IEEE. |
Sponsor | ACKNOWLEDGMENT This paper was made possible by National Priorities Research Program (NPRP) grant No. 9-114-2-055 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. |
Language | en |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Subject | ADMM Arrhythmia Classification Dictionary K-SVD Learning Pietra index |
Type | Conference Paper |
Pagination | 203-207 |
Volume Number | 2018-January |
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