Deep Learning for Cardiac Signal Analysis in Robotic Applications

Part I: Fundamental of Cardiac Signals and Deep Learning<br>1. CARDIO-AI: Compliance and AI Regulation for Deep Learning in ECG and Cardiac Signal Interpretation<br>2. Autoencoders in Cardiology: Opportunities and Challenges for Clinical Integration<br>3. Attention-Driven Convolutional Autoencoder-LSTM Deep Learning for Arrhythmia Detection and Classification<br>4. A Novel Deep Learning Framework for Arrhythmia Detection and Classification in Robotic-Assisted Cardiac Surgery<br>5. HTCB-AF : Hybrid-Transformer CNN-BiGRU with Attention-Guided Beat Fusion for Explainable Arrhythmia Detection<br><br>Part II: AI-Enhanced Cardiac Signal Analysis<br>6. Automated detection of posterior myocardial infarction using dynamical pattern of optimized 2D plot of dVCG signals and geometrical features<br>7. Advancing Diabetes Management: Machine Learning-Based Non-Invasive Glucose Monitoring with Wearable PPG Sensors<br>8. Deep Learning for Atrial Fibrillation Detection from ECG Signals<br>9. AI-Guided Robotic Cardiac Interventions: Precision and Safety<br>10. A Comprehensive Review of Algorithmic Approaches in Generative Artificial Intelligence: Trends, Techniques, and Future Directions<br><br>Part III: Integrating AI with Robotic Cardiac Surgery<br>11. Bio-Inspired Machine Learning Classifiers for Breast Cancer Data Analysis: A WEKA-Based Optimization Approach for Robotic Surgery<br>12. Deep Learning for ECG-Based Arrhythmia Detection and Classification: Architectures, Challenges, and Clinical Translation<br>13. Artificial Intelligence Frameworks for Cardiovascular Diagnosis: From Data Processing to Model Selection, Evaluation, and Clinical Deployment<br>14. Robotic Surgery and Cardiac Bio-Signals: Bridging Human-AI Collaboration<br>15. Federated Learning and Privacy-Preserving AI for Cardiac Signal Analysis in Robotic Surgery