# AI-Enabled Electrocardiogram Analysis for Disease Diagnosis
## 4 Use of ECG with AI for Clinical Analysis and Diagnosis
### Use of ML with ECG for Heart Health Diagnosis  
The electrocardiogram (ECG), a crucial cardiovascular health diagnostic tool, offers thorough information on cardiac function. Waveform or tabular ECG data are processed by machine learning algorithms, which then extract and choose the most pertinent characteristics for study. The machine learning algorithms, such as regression, support vector machine, decision tree, etc., are being trained using the finalized features. Once the model performance is acceptable, that model can be used to validate and later evaluate using new ECG data for diagnosis purposes. Some recent and relevant studies related to using ECG with machine learning for heart disease diagnosis are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Year** & **Hear Disease** & **Algorithm** & **Summary** & **Limitations** \\ \hline
[72] & 2022 & AF & Support vector machine & P-wave characteristics that indicate postoperative atrial fibrillation are the focus of this study. & Single-lead ECG: The study used a single-lead ECG, which may not be as sensitive as a 12-lead ECG. & Retrospective data: The data were collected retrospectively, which may introduce bias. \\ \hline
[73] & 2021 & Ventricular arrhythmia & Support vector machine & The suggested deep learning approach for diagnosing VA makes use of Cubic SVM. & Requires ECG signal preprocessing. The proposed method requires ECG signal preprocessing, which can be time-consuming and error-prone. \\  & & & 97.6\% accuracy is achieved on the MIT-BIH dataset. & Not generalizable to all arrhythmia: The method was only evaluated on a small number of arrhythmias, so it is unclear how generalizable it is to other arrhythmias. \\ \hline
[74] & 2021 & Amyloid builds up in organs & KNN & This research aims to create an AI-powered tool to recognize cardiac amyloidosis by analyzing a conventional 12-lead electrocardiogram. & Unclear generalizability: How the results would generalize to other populations or settings is unclear. \\  & & & & \\ \hline
[75] & 2021 & CVD & ML & Includes waveform localization, denoising, and ML-based disease identification and feature extraction. & Noisy data: The study consider the impact of noise and artifacts on the ECG signals. Single model: The study used a single machine learning model, which may not be generalizable to other datasets or conditions. \\ \hline
[76] & 2021 & Arrhythmia & Support vector machine & ECG descriptors of several types (LBP, wavelet, HOS, morphological) were extracted. & Limited features: The study only considered a limited number of features, such as the QRS complex and the ST segment. \\  & & & & (MRFO + SVM). & Not interpretable: The model is not interpretable, making it difficult to understand why it makes its predictions. \\ \hline
[77] & 2020 & Arrhythmia & Support vector machine & A method for arrhythmia identification using ECG readings is introduced. & Limited features: The study only considered two features, average energy and zero-crossing, which may not be sufficient to detect all types of arrhythmias accurately. \\  & & & & (MRFO + SVM). & Not interpretable: The model is not interpretable, making it difficult to understand why it makes its predictions. \\ \hline
[78] & 2020 & Arrhythmia & Support vector machine & A method for arrhythmia identification using ECG readings is introduced. & Limited features: The study only considered two features, average energy and zero-crossing, which may not be sufficient to detect all types of arrhythmias accurately. \\  & & & & (MRFO + SVM). & Not interpretable: The model is not interpretable, making it difficult to understand why it makes its predictions. \\  & & & & (MRFO + SVM). & Not interpretable: The model is not interpretable, making it difficult to understand why it makes its predictions. \\ \hline \hline \end{tabular}
\end{table}