Inequality indexes as sparsity measures applied to ventricular ectopic beats detection and its efficient hardware implementation.

Journal article


Baali, Hamza, Zhai, Xiaojun, Djelouat, Hamza, Amira, Abbes and Bensaali, Faycal 2017. Inequality indexes as sparsity measures applied to ventricular ectopic beats detection and its efficient hardware implementation. IEEE Access. https://doi.org/10.1109/ACCESS.2017.2780190
AuthorsBaali, Hamza, Zhai, Xiaojun, Djelouat, Hamza, Amira, Abbes and Bensaali, Faycal
Abstract

Meeting application requirements under a tight power budget is of a primary importance to enable connected health internet of things (IoT) applications. This paper considers using sparse representation and well-defined inequality indexes drawn from the theory of inequality to distinguish ventricular ectopic beats (VEBs) from non-VEBs. Our approach involves designing a separate dictionary for each arrhythmia class using a set of labelled training QRS complexes. Sparse representation, based on the designed dictionaries of each new test QRS complex is then calculated. Following this, its class is predicted using the winner-takes-all principle by selecting the class with the highest inequality index. Our experiments showed promising results ranging between 80% and 100% for the detection of VEBs considering the patient-specific approach, 80% using cross-validation and 70% on unseen data using independent sets for training and testing respectively. An efficient hardware implementation of the alternating direction method of multipliers (ADMM) algorithm is also presented. The results show that the proposed hardware implementation can classify a QRS complex in 69.3 ms that use only 0.934 W energy.

KeywordsInequality indexes; Dictionary learning; Internet of Things; Arrhythmia
Year2017
JournalIEEE Access
PublisherIEEE
ISSN21693536
Digital Object Identifier (DOI)https://doi.org/10.1109/ACCESS.2017.2780190
Web address (URL)http://hdl.handle.net/10545/622053
hdl:10545/622053
Publication dates27 Dec 2017
Publication process dates
Deposited12 Jan 2018, 15:37
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ContributorsQatar University and University of Derby
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