# 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} An LSTM and RNN cascaded model for \\ categorizing ECG signal abnormalities is \\ presented in this study. The suggested model \\ yields a 93.46\% sensitivity, an 84.36\% specificity, and an 89.9\% accuracy. The comparison \\ findings show that LSTM with RNN \\ successfully analyzes ECG signals and is ideal \\ for real-time produced data sequences \\ like ECG. \\ \end{tabular}  \\ \hline
[98] & 2023 & Arrhythmia & MLP & \begin{tabular}{c} The DL algorithms for locating the earliest \\ systolic activation sites were trained using a \\ library of simulated pac beats and \\ post-infarct VIs. The platform successfully \\ localized VIs from implanted device \\ intracardiac ECGs (mean LE = 13.10 2.36 mm) \\ and surface ECGs (mean LE = 9.61 2.61 mm). \\ Clinical VT localization was in line with the \\ targets for ablation. \\ \end{tabular}  \\ \hline
[99] & 2023 & \ \begin{tabular}{c} Abnormal beat \\ classification \\ \end{tabular}  & DNN & \begin{tabular}{c} This work suggests a personalized deep \\ learning method for identifying ECG beats. \\ Preprocessing employs EMD to break down \\ beats into intrinsic mode functions (IMFs) and \\ segment the signal based on R-peaks. A deep \\ learning-based bespoke model is used to \\ recognize beats utilizing certain IMFs. The \\ method accurately detects ECG beats by \\ removing important data using convolutional \\ layers with various steps. \\ \end{tabular}  \\ \hline
[100] & 2023 & \begin{tabular}{c} Abnormal heart rate \\ type \\ \end{tabular}  & \ \begin{tabular}{c} AlexNet, CNN, LSTMs, \\ and ResNet50V2 \\ \end{tabular}  & \begin{tabular}{c} The article introduces a practical and reliable \\ approach for determining the average heart \\ rate by analyzing the reflected light on the skin. \\ \end{tabular}  \\ \hline
[100] & 2023 & \begin{tabular}{c} Abnormal heart rate \\ type \\ \end{tabular}  & \ \begin{tabular}{c} AlexNet, CNN, LSTMs, \\ and ResNet50V2 \\ \end{tabular}  & \begin{tabular}{c} The article introduces a practical and reliable \\ approach for determining the average heart \\ rate by analyzing the reflected light on the skin. \\ \end{tabular}  \\ \hline
[100] & 2023 & \ \begin{tabular}{c} Abnormal heart rate \\ type \\ \end{tabular}  & \begin{tabular}{c} The article introduces a practical and reliable \\ approach for determining the average heart \\ rate by analyzing the reflected light on the skin. \\ \end{tabular}  \\ \hline
[100] & 2023 & \ \begin{tabular}{c} Abnormal heart rate \\ type \\ \end{tabular}  & \begin{tabular}{c} The article introduces a practical and reliable \\ approach for determining the average heart \\ rate by analyzing the reflected light on the skin. \\ \end{tabular}  \\ \hline
[100] & 2023 & \begin{tabular}{c} Abnormal heart rate \\ type \\ \end{tabular}  & \ \begin{tabular}{c} AlexNet, CNN, LSTMs, \\ and ResNet50V2 \\ \end{tabular}  & \begin{tabular}{c} The article introduces a practical and reliable \\ approach for determining the average heart \\ rate by analyzing the reflected light on the skin. \\ \end{tabular}  \\ \hline
[100] & 2023 & \begin{tabular}{c} Abnormal heart rate \\ type \\ \end{tabular}  & \ \begin{tabular}{c} AlexNet, CNN, LSTMs, \\ and ResNet50V2 \\ \end{tabular}  &
\begin{tabular}{c} The article introduces a practical and reliable \\ approach for determining the average heart \\ rate by analyzing the reflected light on the skin. Based on the literature review on deep learning using ECG for heart disease diagnosis, the most popular algorithms among researchers are convolutional neural network (CNN) and long short-term memory (LSTM). Similar to the reasons stated in the previous subsection, the review does not discuss the performance of deep neural networks. Instead, the review discusses the potential use of DL for diagnosis. Like the current scenario using machine learning algorithms, deep learning algorithms are strictly limited to studies or experiments; none of the studies have been implemented in any commercial product or can be used reliably in medical setups.