## 2 Related Works  
Advances in HRV and ECG analysis have greatly improved our ability to predict and manage CVDs. For example, [PERSON] and [PERSON] [19] demonstrated the use of deep convolutional neural networks (CNNs) combined with TL to classify ECG recordings, specifically focusing on AF detection. This approach significantly reduces the need for extensive annotations by leveraging large pretrained datasets like Icontia11K and fine-tuning them on more specific datasets, such as PhysioNet/CinC Challenge 2017. Similarly, [PERSON] et al. [20] explored the use of HRV to predict cardiovascular and cerebrovascular events in hypertensive patients. Their study employed various data-mining algorithms, including support vector machines, tree-based classifiers, and artificial neural networks, achieving notable sensitivity and specificity rates. This demonstrated that HRV analysis could be a reliable method for early risk stratification of hypertensive patients. Additionally, [PERSON] et al. [21] utilized HRV features and the RUSBOOST algorithm to predict cardiovascular and cerebrovascular events in hypertensive patients, achieving high accuracy and F1 scores. This approach highlights the potential of HRV and machine learning to enhance prognostic assessments and clinical decision-making. [PERSON] et al. [22] advanced this further by integrating dual-tree complex wavelet packet transform (DTCWPT) and nonlinear HRV feature extraction with cost-sensitive RUSBoost (CS-RUSBoost) to effectively identify high-risk hypertensive patients. HRV and ECG analysis have been extended to diagnose a variety of cardiac pathologies. [PERSON] et al. [23] developed an advanced system for assessing hypertension severity using ECG signals. They employed a bi-orthogonal wavelet filter bank (BOWFB) to decompose ECG signals into sub-bands, followed by extracting features, such as sample entropy and wavelet entropy. Their ensemble bagged trees (EBT) classifier achieved a high accuracy of 99.95%, demonstrating the potential for automated early stage hypertension detection. Furthermore, [PERSON] et al. [24] utilized TL to predict myocardial injury from continuous single-lead ECG signals. By pretraining on labeled 12-lead ECGs and fine-tuning on single-lead data, their models achieved significant improvements in prediction accuracy, surpassing traditional diagnostic methods. Additionally, [PERSON] et al. [25] developed a computer-aided diagnosis (CAD) system for MI detection using deep learning, achievingaccuracies of 99.02% and 99.22% with their models, demonstrating the robustness of these methods in clinical applications. Heart failure prediction and management have also benefited from advancements in HRV and ECG analysis. [PERSON] and [PERSON] [26] presented a model combining deep CNNs for feature extraction with long short-term memory (LSTM) networks for classification, achieving an accuracy of 95.21%. This highlights the potential of these technologies for early and accurate diagnosis of congestive heart failure (CHF). [PERSON] et al. [27] also focused on distinguishing between healthy individuals and those with CHF using HRV data and machine learning algorithms. Their study analyzed ECG recordings from the PhysioNet database, achieving the highest accuracy of 77% using KNN and decision tree models. This demonstrates HRV's potential as a non-invasive biomarker for early detection and management of CHF. Machine learning techniques applied to HRV data have also enhanced stroke risk prediction. [PERSON] et al. [28] introduced a hybrid deep transfer learning-based stroke risk prediction (HDTL-SRP) framework, leveraging data from correlated sources like hypertension and diabetes. This approach addressed the challenges of small and imbalanced stroke datasets, demonstrating the significant potential for accurate stroke risk prediction, while preserving patient privacy.