## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### Learning Algorithms on ECG
#### 6.2.1 Learning on Reconstructed ECG Signal
The concepts are experimented upon MIT-BIH and PTB datasets where all the experiments were carried out directly on time domain, and the obtained result demonstrated that this method achieved better performance compared to other existing models with respect to reconstruction time and accuracy. The reconstruction accuracy of the ECG signal was evaluated using PRD. The proposed model enhanced the reconstruction accuracy by 2% and the PRD value is equal to 0.39%. The discussions have been tabulated in Table 3 with respect to various applications, CS implementation on ECG signal classification using various Deep learning algorithms. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Application** & **DL Algorithm** & **Database** & **Result** & **Remark** \\ \hline [PERSON] et al. [213] & QRS detection & CNN and LSTM & MIT-BIH AFDB and European ST-T & Average time = 0.1265 s & lower reconstruction error \\ \hline [PERSON] et al. [214] & Normal, LBBB, RBBB and PVC & CNN and SVM & MIT-BIH cardiac arrhythmia & Average accuracy = 99.39\% & low quality signal, achieved high accurate classification \\ \hline [PERSON] et al. [215] & ECG data compression & multi-objective optimization neural network & MIT BIH ECG database & data compression ratio is 1:19, PRD = 12\% and CC = 99\%, & lesser computational time \\ \hline [PERSON] et al. & Arrhythmia & 1-D CNN & MIT-BIH & Average precision = 91.73\% & implemented on FPGA, 91.55\% \& specificity = 98.65\% & \\ \hline [PERSON] et al. [217] & Arrhythmia detection & SVM classifiers & MIT-BIH arrhythmia database & CR up to 9.77 can be classified with precision and sensitivity \textgreater{} 90\% & negative correlation between CR and reconstructed signal \\ \hline [PERSON] et al. [218] & ECG signal classification & CNN & MIT-BIH arrhythmia database & Average accuracy = 98\% & classification stages are combined as single frame \\ \hline [PERSON] et al. [219] & cardiovascular disease detection & CNN (LeNet-5) & \begin{tabular}{c} MIT-BIH arrhythmia \\ arrhythmia \\ \end{tabular} & \begin{tabular}{c} Achieved sensitivity and \\ specificity of 99.4\% \\ 99.9\% \\ \end{tabular} & \begin{tabular}{c} To increase the \\ convergence speed of \\ the learning rate \\ \end{tabular} \\ \hline [PERSON] & 5 types of & \multirow{2}{*}{2D CNN} & MIT-BIH & The average & Pan Tompkins algorithm \\ [PERSON] et al. [220] & Heartbeat classification & & \begin{tabular}{c} Arrhythmia \\ database \\ \end{tabular} & \begin{tabular}{c} The average \\ accuracy for TN4 \\ model is 99.65\% \\ \end{tabular} & \begin{tabular}{c} Pan Tompkins algorithm \\ was used for ECG wave \\ R peak detection \\ \end{tabular} \\ \hline [PERSON] et al. [167] & Automated heart & \multirow{2}{*}{1D CNN} & MIT-BIH & F1 Score Mean & \begin{tabular}{c} Considered three classes \\ database Normal, \\ Atrial premature beat \\ and premature \\ ventricular contraction. \\ \end{tabular} \\ \hline [PERSON]. [PERSON] et al. [221] & Automated ECG & CNN and GaborCNN & \begin{tabular}{c} Fantasia and St. \\ Petersburg \\ databases \\ \end{tabular} & \begin{tabular}{c} Average success \\ rate CNN = 99.55\% \\ GaborCNN = \\ 98.74\% \\ \end{tabular} & \begin{tabular}{c} CAD, MI and CHF heart \\ diseases were \\ considered for \\ classification \\ \end{tabular} \\ \hline [PERSON] et al. [222] & \begin{tabular}{c} ECG heart \\ Signal \\ classification \\ \end{tabular} & CNN and RNN & MIT-BIH dataset & Accuracy = 95.90\% & cardiac health application \\ \hline [PERSON] et al. [223] & arrhythmia detection & \begin{tabular}{c} CNN and \\ inverted residual \\ block (IRB) \\ \end{tabular} & \
\begin{tabular}{c} MIT-BIH \\ arrhythmia \\ \end{tabular} &
\begin{tabular}{c} classification \\ accuracy was \\ 100\%, \\ \end{tabular} & clinical applications. \\ \hline [PERSON] & cardiac & \multirow{2}{*}{2D CNN} & MIT-BIH dataset & overall accuracy = 99.13\% & Real-time arrhythmia detection Application \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Models based on the convolutional neural network and hybrid models.