Inequality Indexes as Sparsity Measures Applied to Ventricular Ectopic Beats Detection and its Efficient Hardware Implementation

Abstract

Meeting application requirements under a tight power budget is of a primary importance to enable connected health internet of things 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 labeled 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. The 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 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.

Keywords

• Inequality indexes
• dictionary learning
• ADMM
• arrhythmia
• classification
• connected health