## 2 Review Methodology
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S/No** & **Author** & **Year** & **Objective** & **Methodology** \\ \hline
39 & \
\begin{tabular}{c} [PERSON], \\ [PERSON], \& \\ [PERSON] [66] \\ \end{tabular} & 2017 &
\begin{tabular}{c} To develop a machine learning model for developing \\ decision support system for cardiac disease diagnosis \\ \end{tabular} & Hidden Markov model \\ \hline
40 & [PERSON] [67] & 2017 &
\begin{tabular}{c} To design an automated classification of normal and \\ Coronary Artery Disease conditions of ECG \\ \end{tabular} & DWT, DCT, PCA and k-NN, SVM \\ \hline
41 & \begin{tabular}{c} [PERSON], \\ [PERSON] [26] \\ \end{tabular} & 2017 & \
\begin{tabular}{c} To provide design specifications for time-critical \\ medical monitoring applications \\ \end{tabular} &
\begin{tabular}{c} Real-time acquisition and \\ processing using wearable \\ biosensors \\ \end{tabular} \\ \hline
42 & [PERSON], [PERSON] [4] & 2017 & \
\begin{tabular}{c} To develop an IoT mechanism for automatic extraction \\ of information related to heart disease from filtered \\ ECG signals \\ \end{tabular} &
\begin{tabular}{c} Revised Sequential Recursive \\ algorithm, DWT and Fisher's \\ Linear Discriminant \\ \end{tabular} \\ \hline
43 & [PERSON] and [PERSON] [10] & 2017 &
\begin{tabular}{c} To present HDSS, a closed-loop multitier health \\ decision support system \\ \end{tabular} & Ensemble approach \\ \hline
44 & \begin{tabular}{c} [PERSON] and \\ [PERSON] [68] \\ \end{tabular} & 2017 & \
\begin{tabular}{c} To develop a wearable low-power sleep apnea \\ monitoring device for in-home care \\ \end{tabular} &
\begin{tabular}{c} Tunable-Q factor wavelet \\ transforms and RUSBoost \\ classifier \\ \end{tabular} \\ \hline
45 & [PERSON], [PERSON] [69] & 2017 & \
\begin{tabular}{c} To improve the classification performance of ECG \\ diagnosis algorithm \\ \end{tabular} &
\begin{tabular}{c} Recurrent neural networks \\ (RNN) and density-based \\ clustering technique \\ \end{tabular} \\ \hline
46 & [PERSON], [PERSON] [70] & 2017 &
\begin{tabular}{c} To minimize the response time and cost of operating \\ cardiac emergency medical service \\ \end{tabular} & Crowdsourcing method \\ \hline
47 & [PERSON], [PERSON] [71] & 2017 & \
\begin{tabular}{c} To improve the accuracy of interpretations of the \\ 12-lead ECG and to minimize missed co-abnormalities \\ \end{tabular} &
\begin{tabular}{c} Differential Diagnosis \\ Algorithm (DDA) \\ \end{tabular} \\ \hline
48 & [PERSON] [35] & 2017 & \
\begin{tabular}{c} to evaluate the performance of four datasets from \\ PhysioNet physiological repository \\ \end{tabular} &
\begin{tabular}{c} Periodogram approach for \\ VF detection \\ \end{tabular} \\ \hline
49 & \begin{tabular}{c} [PERSON], \\ [PERSON] [72] \\ \end{tabular} & 2017 & \
\begin{tabular}{c} To carry out a correlation analysis of 12-lead ECG \\ signals \\ \end{tabular} &
\begin{tabular}{c} Non-fiducial using \\ Cross-correlation method \\ \end{tabular} \\ \hline
50 & [PERSON] and [PERSON] [73] & 2017 & \
\begin{tabular}{c} To design and evaluate ECG identification method \\ based on non-fiducial feature extraction and window \\ removal method \\ \end{tabular} &
\begin{tabular}{c} Window removal method for \\ feature extraction. Nearest \\ neighbor (NN), support \\ vector machine (SVM), \\ and linear discriminant \\ analysis (LDA) for \\ classification. \\ \end{tabular} \\ \hline
51 & \begin{tabular}{c} [PERSON], \\ [PERSON] [74] \\ \end{tabular} & 2017 & \
\begin{tabular}{c} To develop ECG biometric authentication system based \\ on non-fiducial autocorrelation method \\ \end{tabular} &
\begin{tabular}{c} Non-fiducial autocorrelation \\ and kernel-based method \\ and one-class SVM \\ \end{tabular} \\ \hline
52 & \begin{tabular}{c} [PERSON], [PERSON] [16] \\ \end{tabular} & 2018 & \
\begin{tabular}{c} To review smart health monitoring approaches \\ \end{tabular} &
\begin{tabular}{c} Review paper \\ \end{tabular} \\ \hline
53 & \begin{tabular}{c} [PERSON], [PERSON]- \\ [PERSON] [18] \\ \end{tabular} & 2018 & \
\begin{tabular}{c} To improve classification of arrhythmia detection \\ system \\ \end{tabular} &
\begin{tabular}{c} DENLMS adaptive filter, \\ Coiflet wavelet, HRV \\ features and SVM \\ \end{tabular} \\ \hline
54 & [PERSON] [14] & 2018 &
\begin{tabular}{c} To analyze telemonitoring of health care based on \\ intelligent analysis of unstructured big data in \\ real-time. \\ \end{tabular} & Big data \\ \hline
55 & [PERSON] [17] & 2018 &
\begin{tabular}{c} To improve the ECG classification speed on a noisy \\ ECG signal. \\ \end{tabular} & PCANet and SVM \\ \hline
56 & \
\begin{tabular}{c} [PERSON] and \\ [PERSON] [75] \\ \end{tabular} & 2018 &
\begin{tabular}{c} To develop real-time patient supervision system using \\ IoT-based smart monitoring model. \\ \end{tabular} & Internet-of-things framework \\ \hline \hline \end{tabular}
\end{table}