## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### CS Implementations-Sensing and Sparsifying Matrices
For various values of CR BSBL-BO and BSBL-EM algorithms performed superiorly, specifically BSBL-BO resulted in giving the best PRD while BSBL-EM attained the better RT at various CR. It resulted in saving more hardware resources and reducing the burden of sampling and storing. Article [191] discusses the aim of enhancing the reconstruction quality of ECG signals in CS algorithm. [PERSON] et al. proposed a new method for dictionary matrix optimization. The dimensions of the dictionary matrix were reduced by using a Multiple Orthogonal Matching Pursuit (M-OMP) algorithm in an initial training phase. As a result, the OMP algorithm used in reconstruction estimates the signal coefficients in a reduced domain, which in turn increased the signal's quality and execution time was also reduced. PhysioNet and MIT-BIH Arrhythmia Database were explored for evaluation, and this method achieved greater performance in terms of PRD and also exhibited a better reconstruction quality. The work performed by [PERSON] et al. in [192] discusses about ECG signal corrupted with different noise signals and efficient way of denoising and recovering. They considered two noises baseline wander interference and Gaussian white noise and overcame the noise by using the low-pass filtering method and alternating direction method of multipliers (ADMM) optimization algorithm. This BP-ADMM is based on the conventional BP algorithm, which can reconstruct the ECG signals with denoising. To improve the original variables and dual variables at the same time this scheme adds a secondary penalty term and reduces constraint conditions. By decomposing the objective function in parallel the calculation speed is improved by dual decomposition method, the simulation results conclude that the proposed algorithm gave its best in ECG denoising with higher SNR and lesser mean square error (MSE). [PERSON] et al. [193] proposed low-complexity CS techniques in WBSN for monitoring ECG signals. To simplify the support augmentation and to estimate the efforts in the recovery algorithm, properties of ECG were utilized in the wavelet domain to extend the partially known support set (PKS). OMP algorithm based variable orthogonal multi-matching pursuit (vOMMP) that combines the advantages of OMP and orthogonal multi-matching pursuit (OMMP) is proposed to successfully augment the support set with reliable supports as a first step and as a second phase. The OMMP is used to increase the probability of recovering the missing supports to increase the reconstruction performance. For reducing the computation complexity matrix-inversion-free scheme based on QR decomposition was utilized to implement pseudo-inverse operation. The performance of the proposed scheme was evaluated, complexity analysis and simulation results proved to attain good performance with less complexity. [PERSON] et al. [194] conducted study on adaptive ECG signal processing system adapting the quantity of data transmitted based on the channel status. Partial DCT matrices and LDPC matrices were used as measurement matrices. Later, performances of various recovery algorithms were evaluated using CR, MSE, PRD and SNR. BSBL algorithm gave the best result with low data rate. Wireless body area network (WBAN) is a trending technology which allows examining and collecting patient health data using wearable sensors. WBAN communicates through the Internet and other wireless technologies like Bluetooth, ZigBee, Wireless Sensor Networks (WSNs) etc. Here are a few studies which are carried out using CS on WBSN. This approach by [PERSON] [195] initiated to cut down the energy consumption of WBAN node, a data compression energy-saving strategy was presented. Using CS technique for signal compression, Sparse Representation Classification (SRC) algorithm was selected to recognize the normal and abnormal signal.