Improving recovery of ECG signal with deterministic guarantees using split signal for multiple supports of matching pursuit (SS-MSMP) algorithm Israa Shaker Tawfic a, *, Sema Koc Kayhan b a Electric and Electronic Engineering, Ministry of Science and Technology, Baghdad, Iraq b Electric and Electronic Engineering, University of Gaziantep, Gaziantep,Turkey ARTICLE INFO Article history: Received 18 June 2016 Received in revised form 9 September 2016 Accepted 18 October 2016 ABSTRACT Compressed sensing (CS) is a new field used for signal acquisition and design of sensor that made a large drooping in the cost of acquiring sparse signals. In this paper, new algo- rithms are developed to improve the performance of the greedy algorithms. In this paper, a new greedy pursuit algorithm, SS-MSMP (Split Signal for Multiple Support of Matching Pursuit), is introduced and theoretical analyses are given. The SS-MSMP is sug- gested for sparse data acquisition, in order to reconstruct analog and efficient signals via a small set of general measurements. This paper proposes a new fast method which depends on a study of the behavior of the support indices through picking the best estimation of the corrosion between residual and measurement matrix. The term multiple supports originates from an algorithm; in each iteration, the best support indices are picked based on maximum quality created by discovering correlation for a par- ticular length of support. We depend on this new algorithm upon our previous derivative of halting condition that we produce for Least Support Orthogonal Matching Pursuit (LS-OMP) for clear and noisy signal. For better reconstructed results, SS-MSMP algorithm provides the recovery of support set for long signals such as signals used in WBAN. Numerical experiments demonstrate that the new suggested algorithm performs well compared to existing algorithms in terms of many factors used for reconstruction performance. © 2016 Elsevier Ireland Ltd. All rights reserved. Keywords: Compressed Sensing MSMP Algorithm Least Support Orthogonal Matching Pursuit percentage root-mean square difference PRD, Average support - cardinality error ASCE 1. Introduction Signal processing on its core is concerning about an effective algorithm for extracting data of signals. In order to design such algorithms for a specific problem, we must have precise models for the concerned signal. These could be a form of obstetric models, probabilistic models or even deterministic classes. According to these challenges, a low dimensional model has been suggested [1]. Such a low-dimensional model constructs a mathematical structure for capturing important data from those high-dimensional signals as long as most of their * Corresponding author. Ministry of Science and Technology/ Information Technology, Baghdad, Iraq. E-mail address: isshakeralani@yahoo.com (I.S. Tawfic). http://dx.doi.org/10.1016/j.cmpb.2016.10.014 0169-2607/© 2016 Elsevier Ireland Ltd. All rights reserved. computer methods and programs in biomedicine 139 (2017) 39–50 journal homepage: www.intl.elsevierhealth.com/journals/cmpb