## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### CS Implementations-Sensing and Sparsifying Matrices
For individual ECG records, the proposed algorithm provides high-quality reconstructed signal without training stage. It incorporates both beat type dictionary and non-uniform random sensing matrix. Based on the morphology of the ECG beats the matching beat type dictionary is employed for recovery of the ECG signals. A cross-correlation based template matching approach is used to determine the type of ECG beat in the recovered signal. The kind of the beat is identified by determining the greatest cross-correlation coefficient. The qualitative and quantitative analysis concluded efficiency by analyzing CR, PRD, PRD1, RMSE, SNR and (fractional distortion measure) FDM. It produced greater CR and the power consumed by the proposed scheme was 11.35 \(\rm\upmu W\) which is very less. The authors [PERSON] et al. (2020) provided the complete description to build wavelet dictionaries with reduced dimensionality for modelling the ECG signals, these dictionaries are created from known wavelet families. Each dictionary is created by taking the models from a wavelet basis and translating them in a smaller step than the wavelet basis itself. There are two parts to each of the suggested dictionaries. A discrete cosine basis denoted as matrix \(D_{c}\), is used in one of the components few elements, and the Wavelet dictionary denoted as matrix \(D_{w}\) is the other component. As a result of the horizontal concatenation of \(D_{c}\) and \(D_{w}\) matrices, the full dictionary D modeling is built as \(\rm D=[D_{c}*D_{w}]\) and it was illustrated to reduce the dimensionality of three records from the Arrhythmia database. The wavelet dictionary is constructed for different scales with translation parameter \(\rm b=1\) and \(\rm b=\
icefrac{{1}}{{4}}\) and approximation was realized. [PERSON] et al. (2020) this paper presents a dynamic CS method for monitoring ECG signal with multiple lead, and transfer of information through IoT networks. The CS algorithm utilizes a sensing matrix that is constructed from a vector obtained by precisely integrating ECG signals from two separate leads. The sensor node obtains a compressed signal for every ECG frame and sends it to the cloud server, together with the vector defining the sensing matrix. As a result, the sensing matrix can be reconstructed in the cloud server, and all of the ECG leads can be recovered, Mexican hat wavelet kernel is used as sparsity matrix and to solve the reconstruction problem this method utilized two minimization algorithms the Multiple Sparse Bayesian Learning (M-SBL) and the Multiple FOCal Underdetermined System Solver (M-FOCUSS). The proposed method obtained low PRD. [PERSON] et al. (2020) exploit a single-lead ECG compression method in which the Q, R and S wave peaks and periods are detected for each heartbeat in preprocessing stage, later this QRS complex is estimated these estimated QRS complex is compared with original ECG signal and the difference signal is considered as error where these error signal is compressed with CS method. DWT sparsifying dictionaries (The bi-orthogonal wavelet filter \"bior4.4\") is adopted for entire process. The results indicate that an average compression ratio of 11:1 with PRD1 = 1.2% are obtained and proved an improved result in compression ratio. [PERSON] et al. [208] presented an alternate method for CS and reconstruction of ECG signal, which offers a high CR. This is achieved by high decimation and requantizing the measurement signal. QRS detector algorithm using Hilbert transform was used to detect exact R wave position for signal segmentation, the reconstruction employed a dynamic ECG model, based on Differential Evolution (DE) algorithm to find the ECG model parameters for signal reconstruction. It had been experimented varying the decimation factor D for different CR values and achieved better results for higher D. The results indicated the similar quality of reconstruction for \(l_{1}\) and \(l_{2}\), based on error function minimization.