# AI-Enabled Electrocardiogram Analysis for Disease Diagnosis
## 4 Use of ECG with AI for Clinical Analysis and Diagnosis
### Use of ML/DL with ECG for Other Health Issues
ECG analysis and cutting-edge AI algorithms can offer doctors important information about stroke diagnosis. Atrial fibrillation (AF) detection utilizing ECG and AI is one of the essential components of the process. AF, an irregular heartbeat, considerably increases the risk of stroke. AI systems can automatically identify the existence of AF by reviewing ECG data, enabling the early detection of those at risk for stroke [134]. The ST-segment alterations in an ECG can be a valuable indicator of an ischemic stroke. Lack of oxygen delivery to the heart causes an ischemic stroke characterized by distinctive abnormalities in the ST segment of the ECG [135]. These ST-segment variations may be analyzed by AI algorithms, which then categorize them as signs of stroke, assisting medical practitioners in making quick and precise diagnoses. ECG signal analysis of the QRS complexes aids in stroke detection. Artificial intelligence (AI) systems can spot abnormalities in these complexes to provide effective stroke detection. AI may also evaluate heart rate variability (HRV) produced from ECG readings to find patterns associated with stroke risk to detect stroke [136]. Early identification of strokes and the evaluation of stroke risk are improved by combining HRV analysis with other clinical data. Convulsions, loss of consciousness, sensory problems, and periods of aberrant electrical activity in the brain are just a few symptoms that can result from seizures. Growing interest has been shown in the application of AI and ECG for epilepsy detection in re \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Year** & **Health Problem** & **Algorithm** & **Vital Parameter** & **Summary** \\ \hline \multirow{4}{*}{[127]} & \multirow{4}{*}{2022} & \multirow{4}{*}{Fetal arrhythmia} & \multirow{4}{*}{Deep learning} & \multirow{4}{*}{Fetal ECG} & A deep learning model divides segmented signals into standard and arrhythmic categories. The approach provides improved accuracy compared to traditional methods by concentrating on labeled segments, which lessens the influence of heartbeat detection findings. \\ \cline{1-1} \cline{4-5}  & & & & & The paper introduces the Box–Cox transformation technique to rectify label distribution and an end-to-end model called “BPNet” with a cross-modal fusion module capable of accurately capturing the relationship between multi-level ECG and PPG features. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._cent years. ECG data may be analyzed using AI algorithms to find patterns connected to epilepsy. These patterns can be utilized to create AI-based tools that assist physicians in making earlier and more accurate diagnoses of epilepsy [137, 138]. The combination of ML/DL and ECG has the potential to completely transform healthcare by enabling the early and precise identification of a wide range of medical disorders outside of heart diseases. These strategies might improve patient care, enable individualized therapy, and aid in the general growth of healthcare systems with more study and development.