## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### CS Implementations-Sensing and Sparsifying Matrices
The ECG signal is segmented into non-overlapping blocks of definite length and sparsification of the signal is performed on each block using Daubechies 5 DWT. The wavelet coefficients are thresholded to improve the performance, keeping only the top magnitude coefficients. CS is applied to the wavelet coefficients of each block and the reconstruction capability of ARMP-PKS is evaluated on MIT-BIH Arrhythmia Database. ARMP-PKS displayed a significant improvement in reconstruction time and SNR. [PERSON] et al. [186] discussed a CS technique with lower-rank limitation for efficient data acquisition and signal recovery for multichannel ECG in WBSN. For effective signal recovery, an analytical method is implemented based on alternating direction method of multipliers (ADMM) to effectively solve the optimization problem. Specifically, two optimization methods are defined \(l_{1}\) norm and nuclear norm for sparsity and the low-rank constraint respectively. Daubechies-4 (db4) wavelets and DCT are used as the temporal and spatial sparsifying bases for reconstruction of MECG signals using MIT-BIH and PTB database. Considering the additive white Gaussian noise (SNR = 10 dB) to all the experiments, the proposed algorithm achieved higher reconstruction accuracy with a smaller number of required transmission packets and lesser computational complexity with low reconstruction error. [PERSON] et al. [187] makes a study on the acquisition of heart sound (HS) signals by CS in WBSN. The main purpose is to find the best sparsifying basis among the wavelet family, the best reconstruction techniques and frame size among the widely used ones. BP was considered as recovery algorithm, setting the frame size to 1024 and it is implemented over 52 different types of wavelet basis. The experimental results conclude that with the reduction of CR, PRD decreases significantly. The results demonstrate that rbio3.1, bior3.1 and bior3.3 had bad recovery performance when CR is greater than 35%. Keeping db2 as sparsifying matrix and the frame size to 1024 for fair comparison, different reconstruction algorithms bring about different signal qualities. PRD for almost every CR region and the best efficiency was obtained by BP. OMP algorithm gave satisfactory result for 35% of CR value. However, when CR is greater than 50%, PRDs and SSIMs of OMP and CoSaMP methods are unsatisfactory, and analysis of frame size concluded that the frame size from 256 to 4096 has little influence on the reconstruction quality. The different frame sizes have undefined differences in terms of PRD and SSIM. Another paper [188] addresses the design of bi orthogonal wavelet filters to increase the sparsity of ECG signal in CS domain in ECG and fetal ECG compression based on double exponential wavelet 2 (dew2). From the observed result it can be noted that the sparse representation in fetal ECGs was better compared to other wavelets where the obtained reconstruction quality was enhanced as there was an improvement in PRD and PSNR. In some dataset dew1 performed better than dew2 as the prior had good sparse representation. [PERSON] et al. [189] investigated on Rakeness-Based CS optimization techniques for improving reconstruction performance of an ECG Signal using three iterative algorithms such as OMP, CoSaMP and Iterative Hard Thresholding (IHT) at decoder and evaluated their performance in terms of energy required for reconstruction. On two separate ARM architectures, three iterative techniques were explored, and certain trade-offs were considered, OMP emerges as the best reconstruction algorithm, both in terms of energy consumption and reconstruction quality. In [190] [PERSON] et al. examined the four commonly used CS recovery algorithms such as CoSaMP, OMP, Expectation Maximum-based block sparse Bayesian learning (BSBL-EM) and Bound-Optimization-based block sparse Bayesian learning (BSBL-BO) to identify the suitable algorithm for real-time application for CS-based ECG signal processing. PRD and Reconstructing time (RT) were chosen as performance metrics.