# 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
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Ref.** & **Year** & **Health Problem** & **Algorithm** & **Vital Parameter** & **Summary** \\ \hline
[121] & 2023 & Obstructive sleep apnea & CNN and LSTM & ECG and PPG & 200 ECG and PPG & PPG signals were recorded for the investigation using publicly accessible apnea databases. The DC-LSTM network attained a 98.2\% accuracy rate, a 97.4\% sensitivity rate, a 97.5\% specificity rate, and a 0.92 Kappa coefficient. These outcomes are equivalent to those of fully automated algorithms, making it appropriate for OSA e-health monitoring carried out at home. \\ \hline
[122] & 2022 & Respiration rate & Deep learning & ECG, PPG, and accelerometer & The study proposes a new multitasking deep learning network that simultaneously forecasts respiratory rate and waveform, which ultimately helps reduce error rates across different activities. \\ \hline
[123] & 2022 & Respiration rate & CNN-LSTM & ECG, PPG, and EMG & The models were assessed using two window sizes—32 s and 64 s—based on mean absolute error (MAE). The 64 s timeframe produced more accurate forecasts, according to the results. Bi-LSTM with Bahdanau attention outperformed the other models in terms of how well it handled the bio-signals, especially when came to sEMG-based data (MAE of 0.51 0.03) and PPG/ECG-based data (MAE of 0.24 0.03). \\ \hline
[124] & 2023 & Fetal arrhythmia & Hierarchical extreme learning machine & Fetal ECG & This study introduces a multi-domain feature extraction approach to predict fetal arrhythmia accurately. The technique entails utilizing neighborhood component analysis (NCA) to screen sensitive data and a stacked ELM-SAE network for high-level fusion features. \\ \hline
[125] & 2022 & Fetal arrhythmia & Machine learning algorithm & Abdomen ECG & The framework uses approaches for noise reduction and pulls out important characteristics from various domains. The boosted decision tree classifier, one of four machine learning classifiers, achieves the maximum accuracy of 93.12\%. \\ \hline
[126] & 2022 & Fetal heart rate & 1D-CNN & Fetal ECG & Fetal acidosis is correctly identified from FHR signals by a diagnostic method that uses a 1D-CNN model. With signal preprocessing, accuracy rises to 99.09\% on a large dataset. Real-time use is made possible by integration with inexpensive electrical components. \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._Sleep apnea happens when obstruction occurs during airflow while sleeping and disturbs breathing. It increases cardiac disease by threefold and makes a person prone to accidents. Left unnoticed or untreated, it may result in severe CVD. The conventional way of diagnosing sleep apnea is by attaching an electrode while sleeping, which is uncomfortable; researchers in recent years have used single lead ECG measurement and classification using machine learning into sleep apnea or normal conditions. The ECG measurement can be turned into a respiratory signal using signal processing techniques since the latter is hidden in the former. Since ECG signal acquisition is cheaper and more accessible, respiration, another crucial vital sign, can be measured simultaneously while monitoring heart health conditions. The fetal heart rate can be achieved by recording abdominal ECG by placing ECG lead over the abdomen of a pregnant woman. From that fetal heart rate, the possible arrhythmia of the fetus and probable congenital disease information can be observed, as suggested by several studies. Finally, measuring blood pressure in a noninvasive way without a cuff can be achieved using ECG and PPG signals [129; 130; 131; 132]. The pulse transit time can be calculated from those two signals. That parameter is inversely related to the change in blood pressure. Accurate and effective stroke detection is essential for prompt treatment and better patient outcomes. A potential method for stroke diagnosis has evolved that combines artificial intelligence (AI) methods with electrocardiograms (ECG) [133].