# 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  
With the advancement of deep learning algorithms such as artificial neural network (ANN), convolutional neural networks (CNN), long short-term memory (LSTM), recurrent neural network (RNN), etc., along with tabular and waveform type of data, the image of ECG came in the front. Because neural networks may link characteristics that are challenging to comprehend with the human eye or conventional machine learning approaches, recent research has concentrated chiefly on deep learning [85; 86]. The input was raw data, and the output was disease classification. The first research worked with ECG data to classify them into different arrhythmia types. The second research worked with ECG data to detect ventricular systolic dysfunction. In both types of research, the accuracy of models was better than that of contemporary research using machine learning algorithms without neural networks or DL. The significant advantage of DNN is that there is no need to work with feature extraction or feature selection. Major disadvantages are heavy on calculation and require a significantly large amount of data. Some recent and relevant studies related to using ECG with deep learning for heart disease diagnosis are listed in Table 3. \begin{table}
\begin{tabular}{c c c c c} \hline
**Reference** & **Year** & **Hear Disease** & **Deep Learning Algorithm** & **Summary** \\ \hline \hline \multirow{6}{*}{[87]} & \multirow{6}{*}{2023} & \multirow{6}{*}{General heart disease} & \multirow{6}{*}{CNN} & The suggested system uses the ANOVA-F test as a feature selection technique to organize data efficiently. Using a Convolutional Neural Network Model, it classifies patient data, determines the severity of cardiac illnesses, and swiftly notifies clinicians. Accurate input data from ECG sensors and IoT devices are used. \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \end{tabular}
\end{table} 
Table 3: Use of deep learning and ECG data for heart disease diagnosis. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Year** & **Hear Disease** & \ \begin{tabular}{c} **Deep Learning** \\ **Algorithm** \\ \end{tabular}  & \begin{tabular}{c} **Summary** \\ \end{tabular}  \\ \hline
[88] & 2023 & Arrhythmia & \ \begin{tabular}{c} Generative Adversarial \\ Network (GAN) \\ \end{tabular}  & \begin{tabular}{c} A hierarchical deep learning framework called \\ MadeGAN is presented in this research for the \\ interpretation of ECG signals. MadeGAN \\ combines a Memory-Augmented Deep \\ AutoEncoder with GAN to identify multi-class \\ arrhythmias and anomaly detection. \\ Experimental findings utilizing the MIT-BIH \\ arrhythmia database show that the suggested \\ model performs better than current approaches. \\ \end{tabular}  \\ \hline
[89] & 2023 & Heart failure & \ \begin{tabular}{c} ECG-AI model based \\ on deep learning \\ \end{tabular}  & \begin{tabular}{c} The effectiveness of ECG-AI in predicting heart \\ failure has been verified using real-world data; \\ ECG-AI could potentially play a crucial role in \\ accurately identifying patients. \\ \end{tabular}  \\ \hline
[90] & 2023 & Arrhythmia & \ \begin{tabular}{c} Bi-directional LSTM, \\ CNN, intra LSTM \\ \end{tabular}  & \begin{tabular}{c} This study suggests the MTGB-LSTM \\ approach for categorizing arrhythmias to solve \\ the overfitting issue in CNN. It uses multitask \\ learning and global/intra-LSTM techniques to \\ improve feature selection and avoid local \\ optimum. The MTGBi-LSTM model \\ outperforms the current AFibNet, achieving a \\ 96.48\% accuracy and a 97.73\% sensitivity on the \\ MIT-BIH dataset. \\ \end{tabular}  \\ \hline
[91] & 2023 & Left/right BBB & CNN &