# 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
However, from the existing experiments, it is evident that the future holds significant promises once the questions with reliability and consistency can be solved. This was primarily concluded because deep learning can train a model by fusing several characteristics in a sophisticated network. This aids the model's learning and comprehension of intricate correlations and patterns in the data. However, because of how intricate these linkages are, it is tough for people to comprehend or duplicate how the model decides entirely. Deep learning algorithms can manage enormous volumes of data and extract useful information from them, surpassing what humans can, in some instances, examine and comprehend. Since ECG data is one-dimensional, 1D CNN can be used instead of 2D CNN [106, 107, 108]. Also, the 2D CNN can be applied when using a time-frequency spectrogram. Also, the image of ECG can be used as input for a standard CNN algorithm. The complexity of the calculation capacity requirement varies based on the neural network's depth. However, it has performed excellently in studies with arrhythmia and atrial fibrillation [109, 110, 111]. Recurrent neural network (RNN) has the advantage of using time series data and has proven excellent performance [112, 113, 114]. The LSTM part from RNN can analyze the sequential relation in ECG data and be used with CNN. Also, \"Bidirectional LSTM\" may simultaneously examine the context before and after a certain point in the data. In order to understand the relationship between the LSTM and LSTM, we have used the LSTM part to analyze the relationship between the LSTM and LSTM. \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
[101] & 2022 & LVH & LSTM and CNN & Using a 12-lead ECG, a CNN-LSTM DL model & was created to identify left ventricular & hypertrophy (LVH). One thousand eight & hundred sixty-three patients’ echocardiogram and ECG data were examined, and the model was developed and evaluated using various patient subgroups according to gender and relative wall thickness. \\ \hline
[102] & 2022 & Arrhythmia & \
\begin{tabular}{c} CNN, LSTM, \\ autoencoder \\ \end{tabular} &
\begin{tabular}{c} Utilizing a network layer design based on \\ LSTM, the team optimized an autoencoder \\ structure that increases the precision of \\ arrhythmia association when used alongside \\ their ECG preprocessing process. \\ \end{tabular} \\ \hline
[103] & 2021 & AF & CNN and LSTM & Using ECG signals and deep learning-based techniques in the time-frequency domain to identify AF and differentiate between terminating and non-terminating AF episodes. \\ \hline
[104] & 2021 & \
\begin{tabular}{c} Premature ventricular \\ contraction \\ \end{tabular} & G.R.U., CNN & In order to enhance the recognition of PVC signals, the paper presents a modified gated \\ recurrent unit model that integrates a scale &
\begin{tabular}{c} parameter into the current bidirectional \\ GRU model. \\ \end{tabular} \\ \hline
[105] & 2021 & Arrhythmia & LSTM and CNN & The article aimed to develop a wearable \\ application that uses signal preprocessing and a &
\begin{tabular}{c} deep learning model comprising 1-D CNN \\ and LSTM. \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._to grasp the whole context and make more precise predictions, gathering information from past and future points in the ECG data is highly helpful [115; 116]. Also, using the advantage of the attention module of locating the most informative part and the ability to length the ECG window with LSTM-CNN can improve efficiency [114]. The only downside of these techniques is that computational cost is very high since they need to work with many parameters.