# AI-Enabled Electrocardiogram Analysis for Disease Diagnosis
## 4 Use of ECG with AI for Clinical Analysis and Diagnosis
### Use of DL with ECG for Heart Health Diagnosis
\begin{tabular}{c} This work emphasizes the usage of a feature \\ picture. It provides a global feature extraction \\ approach for categorizing multi-periodic \\ heartbeats. Empirical Mode Decomposition (EMD) creates the feature picture, successfully \\ depicting the ’heartbeat condition.” This feature \\ picture is then used by a two-dimensional \\ Convolutional Neural Network (CNN) to \\ classify the patient’s heartbeat status accurately. \\ The efficiency of the suggested technique is \\ demonstrated by the experimental findings, \\ which also indicate how well it can identify and \\ analyze heartbeats. \\ \end{tabular}  \\ \hline
[92] & 2023 & Myocardial infarction & \ \begin{tabular}{c} EfficientNetV2B2- \\ based transfer learning \\ model \\ \end{tabular}  & \begin{tabular}{c} A new method for automatic detection of \\ myocardial infarction (MI) using 12-lead ECG \\ trace images is introduced in the article through \\ an edge domain-based deep representation \\ learning (DRL) approach. \\ \end{tabular}  \\ \hline
[93] & 2023 & Ventricular arrhythmia & CNN & \begin{tabular}{c} This study trained a 5-layer 1D convolutional \\ neural network (CNN) using a selected dataset \\ of 46,595 ECG recordings. The model \\ outperformed comparable models in the \\ literature when tested using cross-validation, \\ held-out data, and an external dataset. Using \\ brief, single-lead ECG data, the \\ computationally effective CNN correctly \\ diagnosed ventricular arrhythmia (VA), \\ demonstrating the possibility for accelerated \\ and enhanced patient treatment using MCT \\ monitor data. \\ \end{tabular}  \\ \hline
[94] & 2023 & \begin{tabular}{c} This work emphasizes the usage of a feature \\ picture. It provides a global feature extraction \\ approach for categorizing multi-periodic \\ heartbeats. Empirical Mode Decomposition (EMD) creates the feature picture, successfully \\ depicting the ’heartbeat condition.” This feature \\ picture is then used by a two-dimensional \\ Convolutional Neural Network (CNN) to \\ classify the patient’s heartbeat status accurately. \\ The efficiency of the suggested technique is \\ demonstrated by the experimental findings, \\ which also indicate how well it can identify and \\ analyze heartbeats. \\ \end{tabular}  \\ \hline
[92] & 2023 &
\begin{tabular}{c}  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year** & **Heart Disease** & \ \begin{tabular}{c} **Deep Learning** \\ **Algorithm** \\ \end{tabular}  & \begin{tabular}{c} **Summary** \\ \end{tabular}  \\ \hline
[94] & 2023 & Myocardial Infarction & DenseNet & \begin{tabular}{c} Image augmentation techniques are applied to \\ detect Normal, Abnormal Heartbeat and \\ Myocardial infarction (MI) to balance the ECG \\ image dataset classes. \\ \end{tabular}  \\ \hline
[95] & 2023 & Arrhythmia & \ \begin{tabular}{c} Conditional generative \\ adversarial network \\ \end{tabular}  & \begin{tabular}{c} An accurate ECG is produced using the \\ contactless cardiac monitoring device, RF-ECG, \\ employing ultra-wideband signals. In addition \\ to mapping mechanical-to-electrical cardiac \\ activity, complementary restoration techniques \\ restore pulse signal fluctuation. Results from \\ experiments support its usefulness in \\ identifying heart problems. \\ \end{tabular}  \\ \hline
[96] & 2023 & \begin{tabular}{c} Abnormal heart \\ rate type \\ \end{tabular}  & \ \begin{tabular}{c} Temporal \\ convolutional network \\ \end{tabular}  & \begin{tabular}{c} The paper proposes a novel ML architecture \\ using temporal convolution networks (TCN) \\ for classifying five heart diseases based on \\ ECG signals. \\ \end{tabular}  \\ \hline
[97] & 2023 & Arrhythmia & LSTM and RNN &