## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### CS Implementations-Sensing and Sparsifying Matrices
SRC identifies the nodes that are collecting normal signals and set them to dormant state these normal physiological signals collected by WBAN nodes will not be transmitted and put the nodes to sleep state which reduces the amount of data acquisition and transmission, which in turn reduces the data transmission energy consumption of WBAN node. Similarly, if the abnormal signal is identified it is sent directly to the central base station, and reconstruction of the signal is done through CS reconstruction algorithm. FFT was chosen as transformation matrix, and sparse random matrix was selected as measurement matrix. Signal recognition simulation result shows the effectiveness of proposed model based on the PRD and SNR evaluation index. The minimum \(l_{0}\) algorithm has faster computing speed compared with \(l_{1}\) minimum convex optimization algorithm. Considering both recognition time and data rate, greedy algorithm is a preferable algorithm for the ECG SRC algorithm. This paper [196] by [PERSON] et al. proposes a fast and accurate non-sparse ECG signal recovery algorithm based on BSBL known as BSBL-Alternating direction method of multipliers algorithm (ADMM) for ECG tele-monitoring system. The proposed BSBL-ADMM method can recover non-sparse ECG signals in the time domain with high accuracy and speed. A digital CS based practical wearable ECG tele-monitoring system was built and the experimental outcome demonstrated that the proposed approach can directly recover ECG signals in the time domain without the need of a dictionary matrix with fast speed and acceptable accuracy. The suggested technique is quick and resilient for various ECG datasets because of ADMM. This study [197] by [PERSON] et al. recommended CS algorithms for reconstructing under-sampled and compressed ECG signal. This method involved two steps: ECG signal subsampling and reconstruction. Initially sparsity enhancement was done by sub-sampling the ECG signal and using Cut and Align (CAB) it was mapped onto a two-dimensional (2D) space, later using nonlinear optimization model 2D signal was reconstructed. Compression of original signal was achieved using Gaussian random matrix and recovery of signal is done using four well known algorithms, two from MP that is OMP and CoSaMP and another two from BSBL method such as Bound-Optimization based Block Sparse Bayesian Learning (BO-BSBL) and Expectation-Maximum-based Block Sparse Bayesian Learning (EM-BSBL). Performance evaluation was carried out by PRD and Reconstruction Time (RT) is also considered. The suggested CS technique demonstrated to be capable of reconstructing original signals faithfully with just 30% data acquisition. The obtained results prove that the reconstruction accuracy was better in BSBL methods and for implementation MP methods proved to be very efficient. [PERSON] et al. [198] proposed a work in which a joint CS method is considered for compression and recovery of multi-channel ECG (MECG) signals for tele-monitoring applications. Here two specific sparse recovery algorithms are jointly used from weighted mixed-norm minimization (WMNM) algorithms, one is iterative and referred as Sub-band weighted MNM (SWMNM), and the other is a non-iterative algorithm called Prior weighted MNM (PWMNM). The proposed recovery algorithms exploit multi-scale signal information through a sub band weighting strategy. This weighting strategy incorporates additional information about diagnostically relevant wavelet coefficients in the optimization problem formulation and emphasizes them in the final reconstruction. Results are averaged for PTB database, PRD = 6.60, SNR = 23.67, WEDD = 6.07 and CSE data base, PRD = 16.74, WEDD = 13.96. It has a capability of achieving classification accuracy of 73.2% when MECG signals are jointly reconstructed using only about 10% of compressed measurements. The proposed method was capable of reducing the number of CS measurements required for effective reconstruction. [PERSON] et al.