## 2 Review Methodology
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S/No** & **Author** & **Year** & **Objective** & **Methodology** \\ \hline
57 & [PERSON] [76] & 2018 & \
\begin{tabular}{c} To create new and efficient methods for automated \\ detection of myocardial dysfunctions \\ \end{tabular} &
\begin{tabular}{c} Machine learning using SVM, \\ k-NN, PNN, and RBFNN \\ \end{tabular} \\ \hline
58 & [PERSON] [77] & 2018 & \
\begin{tabular}{c} To develop an automatic approach for the diagnosis of \\ AF patients \\ \end{tabular} &
\begin{tabular}{c} entropy-based features in \\ flexible analytic wavelet \\ transform (FAWT) \\ \end{tabular} \\ \hline
59 & [PERSON] & 2018 & \
\begin{tabular}{c} To develop a bimodal authentication system by fusing \\ ECG and fingerprint features \\ \end{tabular} &
\begin{tabular}{c} Non-fiducial using \\ Morphological-based \\ approach \\ \end{tabular} \\ \hline
60 & [PERSON], & 2018 & \
\begin{tabular}{c} To develop a continuous authentication scheme based \\ on ECG streams for real-time authentication \\ \end{tabular} &
\begin{tabular}{c} Non-fiducial using ECG \\ streams method \\ \end{tabular} \\ \hline
61 & [PERSON] [79] & 2018 & \
\begin{tabular}{c} To design an effective method for fiducial points \\ detection from ECG signal \\ \end{tabular} &
\begin{tabular}{c} Polygonal approximation \\ \end{tabular} \\ \hline
62 & [PERSON], & 2019 & \
\begin{tabular}{c} To implement a set of services to support physicians in \\ diagnosing or treating patients’ health issues \\ \end{tabular} &
\begin{tabular}{c} Deep learning \\ \end{tabular} \\ \hline
63 & [PERSON], [PERSON] [81] & 2019 & \
\begin{tabular}{c} To automatically detect anomalous cardiac events \\ directly from machine-readable, recorded ECG signals \\ \end{tabular} &
\begin{tabular}{c} Machine learning such as \\ Multilayer perceptron, SVM \\ and logistic \\ regression. \\ \end{tabular} \\ \hline
64 & [PERSON] and & 2019 & \
\begin{tabular}{c} To develop an ECG-based automated human \\ identification system \\ \end{tabular} &
\begin{tabular}{c} Fiducial and non-fiducial \\ based methods. Information \\ gain ratio and k-NN. \\ \end{tabular} \\ \hline
65 & [PERSON], [PERSON] [33] & 2019 & \
\begin{tabular}{c} To investigate on effective methods for arrhythmia \\ detection and classification \\ \end{tabular} &
\begin{tabular}{c} Nonlinear and nonstationary \\ decomposition method \\ \end{tabular} \\ \hline
66 & [PERSON] and [PERSON] [82] & 2019 &
\begin{tabular}{c} To design a fuzzy expert system for the diagnosis of \\ coronary artery heart disease. \\ \end{tabular} & Fuzzy-based approach \\ \hline
67 & [PERSON], & 2019 &
\begin{tabular}{c} To design an expert system to predict heart disease \\ using Fuzzy approach \\ \end{tabular} & Fuzzy-based approach \\ \hline
68 & [PERSON] and & 2019 &
\begin{tabular}{c} To apply deep learning algorithm for identification of \\ normal and abnormal heart rhythms \\ \end{tabular} & Deep learning \\ \hline
69 & [PERSON] and & 2019 & \
\begin{tabular}{c} To develop an efficient, flexible filtering technique to \\ remove noise from the heartbeat signal \\ \end{tabular} &
\begin{tabular}{c} Adaptive Filtering and \\ Artificial neural network \\ \end{tabular} \\ \hline
70 & [PERSON], [PERSON] [86] & 2019 &
\begin{tabular}{c} To develop a MULtivariate TIme Series Analysis in the \\ Biomedicine (MULTISAB) system \\ \end{tabular} & Multithread parallelization \\ \hline
71 & \begin{tabular}{c} [PERSON] and \\ Rabinezhadsa- \\ datmahaleh \\ [87] \\ \end{tabular} & 2019 & \
\begin{tabular}{c} To automatically classify ECG beats for arrhythmia \\ detection \\ \end{tabular} &
\begin{tabular}{c} Deep learning, k-NN, SVM \\ and Random forest \\ \end{tabular} \\ \hline
72 & [PERSON], [PERSON] [21] & 2019 &
\begin{tabular}{c} To investigate and review the current state of mobile \\ phone applications in cardiac rhythmology \\ \end{tabular} & Review paper \\ \hline
73 & [PERSON] [3] & 2020 & \
\begin{tabular}{c} To introduce new features for the classification of sleep \\ apnea and normal patients \\ \end{tabular} &
\begin{tabular}{c} Machine learning using \\ GentleBoost classifier \\ \end{tabular} \\ \hline
74 & [PERSON] [22] & 2020 &
\begin{tabular}{c} To study the new scientific applications of AI in \\ biomedicine \\ \end{tabular} & Review paper \\ \hline
75 & [PERSON] [88] & 2020 &
\begin{tabular}{c} To investigate state-of-the-art Big Data analytics tools \\ \end{tabular} & Review paper \\ \hline
76 & \begin{tabular}{c} [PERSON] [89] \\ \end{tabular} & 2020 & \
\begin{tabular}{c} To apply optimization of tree-based classifier for heart \\ disease diagnoses \\ \end{tabular} &
\begin{tabular}{c} Co-operative Co-evolution \\ and Random Forest \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}