## 6 Detailed Review of CS Based ECG IoT Framework and Analysis
### CS Realtime Implementations and IoT Framework
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Author** & **Application** & **Algorithm** & **Database** & **Result** & **Remark** \\ \hline \multirow{2}{*}{\begin{tabular}{c} [PERSON] \\ et al. [230] \\ \end{tabular} } & \multirow{2}{*}{biometric user identification} & \multirow{2}{*}{CA-CA} & ECG-ID and & \begin{tabular}{c} Accuracy = 94.16\% \\ \end{tabular} & \begin{tabular}{c} Computation \\ complexity and \\ \end{tabular} \\  & & & & CR = 0.5 & power reduced \\ \hline \multirow{2}{*}{\begin{tabular}{c} [PERSON] \\ et al. [231] \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Hearth rate \\ estimation \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} template matched \\ filtering \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} MIT Arrhythmia \\ Database \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} F measure PT = \\ 98.7\% MF = 98.9\% \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Detection of \\ R-peak directly on \\ the CS \\ Measurements \\ \end{tabular} } \\ \hline \multirow{2}{*}{\begin{tabular}{c} [PERSON] \\ [232] \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Heartbeat \\ Classification \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} Template based \\ algorithm \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} MIT-BIH \\ arrhythmia \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} Accuracy 90.00\% \& \\ 81.88\% \\ CR = 40\% \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} DBM for \\ classification \\ \end{tabular} } \\ \hline \hline \end{tabular}
\end{table} 
Table 4: CS Models without the Reconstruction algorithm. results with other algorithms such as OMP, BSBL-BO and BSBL-EM and obtained a notable improvement in SNR and CR. In order to overcome certain limits such as energy-efficiency, cost and proper compression technique, [PERSON] et al. [236] implemented a low power wireless single lead Bluetooth ECG monitoring device based on CS. A sparse binary matrix was explored to realize the CS compression. This compression technique with sleep and wake-up strategy aims to reduce the transmitted data size and consumed power. Utilization of sleep/wake-up scheme and CS compression reduced power consumption compared to other commercially available devices. The advantages of the proposed device are lesser weight, small size, low-cost, single-spot, real-time and wireless module with a good battery lifetime. A study by [PERSON] et al. [237] investigates the efficiency of CS-based real-time ECG signal reconsruction on an IoT gateway embedded with heterogeneous multicore platform (HMP) featuring ARM. Patients ECG data is compressed and sent to the gateway through Bluetooth. The data is reconstructed and categorized at the gateway in order to identify irregularities in the patient's heartbeat. The odroid xu4 is used for data processing, while the Shimmer3 device is used for data collecting. This technique was capable of handling computing latency, security and privacy issues related to cloud-based models. Comparison results between OMP and SP in terms of PRD shows that SP have lower reconstruction error and OMP performed better than SP at most CRs, increasing number of cores and cores frequency resulted in faster reconstruction. In terms of significantly reducing the hardware complexity of the CS reconstruction OMP algorithm, [PERSON] et al. [238] proposed two different modifications to the OMP algorithm, Thresholding technique for OMP (tOMP) and Gradient Descent OMP (GDOMP). To reduce reconstruction time, OMP was used, to modify identification stage in OMP algorithm and to reduce chip area GDOMP was considered. For all three algorithms, they implemented reconfigurable, parallel, and pipelined architectures which were capable of reconstructing various data vector sizes on 65 nm CMOS technology. This resulted in less reconstruction time and chip area for tOMP and GDOMP compared to OMP ASIC design. Table 5 summarizes hardware implementation on CS models and its result on various applications.