## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### CS Implementations-Sensing and Sparsifying Matrices
[199] discuss a modified low sampling rate approach based on CS theory with incorporation of BSBL framework for classification of normal and abnormal ECG signals. Sparse reconstruction algorithm was evaluated using SPARCO toolbox. ECG databases were subjected to a random sensing matrix, this random sensing matrix was examined with three different sensing matrix variants concluded that with minimum energy consumption the Binary Toeplitz matrix provided good SNR and better compression performance. The recovery of sparse signals from quantized random measurements is validated through BPBQ (Basis Pursuit De Quantizer) toolbox, and performed better on CR, and PRD with high probability and with reasonable accuracy. [PERSON] et al. [200] proposed a self-training dictionary scheme (STDS) based on an approximated \(l_{0}\) norm constraint method (Al\({}_{0}\)CM), it is designed by making use of an accelerated gradient descent method for ECG signal compression and reconstruction to attain better accurate sparse representation. In the compression stage, the original ECG signal is recorded and compressed by sampling matrix. Then the compressed data is transmitted through IoT platform and received by the data center for reconstruction of original ECG signals. In this work STDS is used for sparse representation. A training step is required for the learned dictionary; training data is taken from the original ECG signal. This method performed superior in terms of the RSNR, for both low and high CR. It provided a better precise estimation of the ECG signal even when CR = 0.2, RNSR was high compared to other methods. [PERSON] et al. [201] proposed ECG Signal Compressive Sampling by Encouraging Second-Order Sparsity Differences and with the aid of using a Dictionary Learning. The regularised least-squares (\(l_{\text{p}}^{\text{2d}}\)-RLS) algorithm, for the reconstruction of ECG signals and dictionary learning algorithm for improving the \(l_{\text{p}}^{\text{2d}}\)-RLS algorithm is proposed in this study, On the second-order difference of the signal, the pseudo-norm is employed to promote sparsity. The \(l_{\text{p}}^{\text{2d}}\)-RLS algorithm is associated with the reduction of a pseudo-norm regularised square error. A sequential variant of the basic conjugate-gradient (BCG) approach is used to perform the optimization. The \(l_{\text{p}}^{\text{2d}}\)-RLS technique is used to reconstruct signals, and the linear least-squares method is used to update the dictionary. Simulationresults shows that the \(\rm I_{P}^{2d}\)-RLS approach improves signal reconstruction performance, and the average computation time was also less. [PERSON] et al. (2020) investigated a new method to find out the sparsity order of the signal by reducing the reconstruction error, the Optimum Sparsity Order Selection (OSOS). Using the dictionary matrices based on Gaussian kernel functions representation of the sparse signal is obtained, the OMP algorithm was used to estimate the active coefficients of the model. By knowing the optimum sparsity order, a sensing matrix which has RIP property was used to achieve compressed ECG signal. The proposed algorithm provided fair compression ratio, parameter error, efficient denoising and good improvement in SNR. The raised Cosine dictionary represented more sparsifying dictionary, it is much more efficient than Gaussian kernel and this method can work with any sparsifying kernel. [PERSON] et al. (2013) investigated the (Restricted Boltzmann machines) RBM-OMP-like algorithm wavelets and \"learned overcomplete dictionaries\" are utilized to sparsify ECG signals. RBM is a type of undirected graphical model made up of layer of binary stochastic hidden units and a layer of stochastic visible units. Daubechies-4 wavelet transform used as the sparsifying transform at decomposition level L = 4. Simulation result revealed that the RBM-OMP-like algorithm performed better when using learned overcomplete dictionaries than wavelets. Another proposal by [PERSON] et al. (2020), discusses CS based efficient beat type dictionary learning.