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

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    Authors
    Baali, Hamza
    Zhai, Xiaojun cc
    Djelouat, Hamza
    Amira, Abbes
    Bensaali, Faycal cc
    Affiliation
    Qatar University
    University of Derby
    Issue Date
    2017-12-27
    
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    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.
    Citation
    Baali, H. et al (2017) 'Inequality Indexes as Sparsity Measures Applied to Ventricular Ectopic Beats Detection and its Efficient Hardware Implementation', IEEE Access, DOI: 10.1109/ACCESS.2017.2780190.
    Publisher
    IEEE
    Journal
    IEEE Access
    URI
    http://hdl.handle.net/10545/622053
    DOI
    10.1109/ACCESS.2017.2780190
    Additional Links
    http://ieeexplore.ieee.org/document/8240906/
    Type
    Article
    Language
    en
    ISSN
    21693536
    ae974a485f413a2113503eed53cd6c53
    10.1109/ACCESS.2017.2780190
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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