## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### CS Implementations-Sensing and Sparsifying Matrices  
The signal that has redundant information is said to be compressible and there are lot of compression methods for ECG signals. CS is also a method to compress the signal, but it is performed in the sensing stage where only required number samples or measurements are done on the signal and the original signal is reconstructed from the incomplete linear measurements by exploiting the sparsity of the signal. If the signal is not sparse it can be represented as a sparse signal using a sparsifying matrix. Any prevailing conventional compression techniques can be used as a sparsifier. Let us discuss some sparsifying techniques that have been used for ECG signal compression which is already used in CSA. Let us consider some novel compression techniques that had been used for ECG and explore the method of using it in CS. One such method is Discrete Anamorphic Stretch Transform. In this study, [PERSON] et al. [175] have investigated a technique on compression using the 1D complex Discrete Anamorphic Stretch Transform (DAST). Here, for pre compression of ECG signal 1D DAST with three different kernel functions (Superlinear, Sublinear and Linear) it is proposed. DAST stretches and warps the ECG signal which results in the allocation of huge samples for sharp features, decreased the occupied bandwidth (OBW) and data required for storage. For inverse DAST two tap phase recovery filter is used to recover the phase of the DAST. Their experiment using MIT-BIH arrhythmia database shows that the highest average CR obtained for 3 and 6 level DWT is 3.25 and 2.8 respectively. Authors claim that the pre compression using DAST improves the compression attained using the secondary compression scheme such as Discrete Wavelet Transform (DWT) and (RLE) Run-length encoding. The proposed scheme achieved a higher CR and is suitable for cardiac patient monitoring remotely. [PERSON] et al. proposed another non-invasive technique [176] to determine whether the ectopic foci located in the right or left atrium and also to identify the exact anatomical location of the ectopic foci inside the atrium. 12 lead ECG signal dataset was exploited from Tehran arrhythmia clinic database and estimation and optimization of sparse coefficients was contributed by the use of Gradient Projection for Sparse Reconstruction algorithm (GPSR) that performed well in locating arrhythmias in the right and left atria accurately. Another work [177], addresses two MAXimum Feasible Subsystem which have been implemented and investigated for reconstruction of compressed ECG signals. For signal compression, Random Normalized Matrices (RNM) is used and in recovery of ECG signals it out preformed the state-of-the-art CS sparse recovery algorithms such as Smoothed \(l_{0}\) Norm (\(sl_{0}\)) and BP. [PERSON] addresses [178] the power line interference (PLI) that is present in ECG signal while reconstructing. They utilized the sparsity of signals to execute PLI suppression and as well as to reconstruct the ECG signal. Joint optimization estimation was formulated to simultaneously perform the PLI suppression and ECG recovery in the transform domains. Optimization problem was solved with the help of two popular efficient greedy algorithms such as BP and CoSaMP for ECG reconstruction and PLI suppression using MIT-BIH PTB diagnostic ECG database. In [179], [PERSON] et al. demonstrated the ECG data compression technique based on the tunable Q-wavelet transform (TQWT) which provides modifiable parameters to attain better compression. The performance of the TQWT compression technique was evaluated on various records of MIT-BIH arrhythmia dataset the performance result obtained for 48 first lead ECG records of duration 1 min in terms of CR, PRD, PRD1, QS, QS1 and SNR are 20.61, 4.43, 6.37, 5.88, 3.46, and 55.93 dB respectively and for 48 first lead ECG records of duration 30 min was 21.98, 7.11, 9.23, 4.24, 2.57 and 48.48, respectively. [PERSON] et al.