## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### CS Implementations-Sensing and Sparsifying Matrices
[180] investigates the lossless compression technique for multichannel ECG, which utilizes the adaptive linear prediction for intra channel and inter channel decorrelation to remove redundancy in lossless mode with an adaptive Golomb-Rice codec for entropy coding and achieved an average CR of 2.809 saved average power of 36.5 mW. [PERSON] et al. [181] discusses the innovative wavelet-based compression scheme for ECG signals in e-Health cardiac online diagnostic applications. The compressibility of continuous-time sampled ECG signals has been investigated in this research using 75 ECG signals which are normal and pathologic and 54 different orthogonal and biorthogonal wavelets to determine the best wavelet for ECG compression. After the (level-crossinganalog to digital converter) LC-ADC model sampling, OMP algorithm is used to compress. The effectiveness of both LC-ADC and bior3.1 wavelet decomposition, followed by thresholding, is computed and is compared with conventional ADC. This resulted in 33% bits reduction compared to conventional ADC with PRD ranging from 0.1 to 2.1 percent and an acceptable diagnostic quality. In her research work [182], [PERSON] et al. utilized the correlation between consecutive heartbeats to determine the magnitude of the coefficients of the sparse representation in support of the signal. In this proposed scheme, normalization, sampling and quantization of ECG signal is carried out at the encoder stage, and most of the computational process is done at the decoder side using block sparse Bayesian learning (bSBL) algorithm. Daubechies db4 wavelets were used, and the effectiveness of the proposed bSBL algorithm for reconstruction of original signal is evaluated. The superiority of the proposed scheme was compared with SPIHT algorithm where the proposed method outperformed for low PRD values and reconstruction SNR. When compared with SOMP, MSBL and CoSOMP this method was capable of recovering the ECG signal with lesser measurements. Another article [183] proposes an ECG compression algorithm which relies on residual error coding based on variable-length classified signature, envelope vector sets (VL-CSEVS) and wavelet transform. The suggested approach used an energy-based segmentation technique where a high-energy ECG frame possessing important clinical information as QRS complex was represented with short segments and a low-energy ECG frame with or without clinical information with large segments and tested on the MIT-BIH Arrhythmia and MIT-BIH Compression Test Dataset to get high CR with low reconstruction error, and the average time taken for compression and reconstruction was 0.619 and 0.279 s respectively, retaining the diagnostic information of the reconstructed signal. The authors of [184] [PERSON] et al. explored CS for wireless ECG system with iterative method using WBAN and DWT sparsification. They presented two greedy pursuit techniques named Least Support Orthogonal Matching Pursuit (LS-OMP) and Least Support Denoising-Orthogonal Matching Pursuit (LSD-OMP). The effectiveness of the LS-OMP is tested from Physio Bank ATM records and to get a sparse signal DWT was used. The stopping condition in the algorithm successfully found the correct signal in a smaller number of iterations. Similarly, this was experimented for other record using three level DWT with filter type Symlets8. The transmitted signal was affected with WGN = 10 dB noise, reconstructed signal is achieved with RSNR = 25.0677. Three performance measures were used to check the ability of proposed algorithm and compared with the other three greedy methods such as MP, OMP and (Compressive Sampling Matching Pursuit) CoSaMP. Results showed that there is a significant performance in terms of reconstruction quality and compression rate. [PERSON] et al. [185] proposed an ECG compression method in wavelet domain based on adaptive greedy reduced-set matching pursuit with partially known support (ARMP-PKS).