Deep Learning for Cardiac Signal Analysis in Robotic Applications

Dr. Kapil Gupta earned his Ph.D. from the Indian Institute of Information Technology, Design and Manufacturing (IIITDM), Jabalpur, India. He served as an Assistant Professor in Electronics and Communication Engineering at Oriental College of Technology, Bhopal, from 2013 to 2020. He holds a B.E. with Honors in Electronics and Communication Engineering and an M.Tech. in Nano Technology. His research interests encompass signal processing in biomedical applications, time-frequency analysis, artificial intelligence, and cardiovascular systems. Dr. Gupta has published extensively in reputed journals and serves as a reviewer for IEEE and Elsevier. He has organized numerous national and international conferences and has been involved in various technical committees. Dr. Varun Bajaj is an Associate Professor in Electronics and Communication Engineering at Maulana Azad National Institute of Technology Bhopal, India, starting January 2024. Previously, he served at the Indian Institute of Information Technology, Design and Manufacturing (IIITDM) Jabalpur from 2014 to 2024, initially as an Assistant Professor and later as an Associate Professor. He earned his Ph.D. in Electrical Engineering from IIT Indore in 2014, following an M.Tech. in Microelectronics and VLSI Design in 2009, and a B.E. in Electronics and Communication Engineering in 2006. Dr. Bajaj holds various editorial roles, including Associate Editor for the IEEE Sensor Journal and Subject Editor-in-Chief for IET Electronics Letters. A Senior Member of IEEE since 2020, he actively reviews for numerous journals and has delivered over 50 expert talks. He has received multiple awards for his research and has been recognized among the top 2% of researchers globally by Stanford University from 2020 to 2023.

• Part I: Fundamental of Cardiac Signals and Deep Learning1. CARDIO-AI: Compliance and AI Regulation for Deep Learning in ECG and Cardiac Signal Interpretation2. Autoencoders in Cardiology: Opportunities and Challenges for Clinical Integration3. Attention-Driven Convolutional Autoencoder-LSTM Deep Learning for Arrhythmia Detection and Classification4. A Novel Deep Learning Framework for Arrhythmia Detection and Classification in Robotic-Assisted Cardiac Surgery5. HTCB-AF : Hybrid-Transformer CNN-BiGRU with Attention-Guided Beat Fusion for Explainable Arrhythmia DetectionPart II: AI-Enhanced Cardiac Signal Analysis6. Automated detection of posterior myocardial infarction using dynamical pattern of optimized 2D plot of dVCG signals and geometrical features7. Advancing Diabetes Management: Machine Learning-Based Non-Invasive Glucose Monitoring with Wearable PPG Sensors8. Deep Learning for Atrial Fibrillation Detection from ECG Signals9. AI-Guided Robotic Cardiac Interventions: Precision and Safety10. A Comprehensive Review of Algorithmic Approaches in Generative Artificial Intelligence: Trends, Techniques, and Future DirectionsPart III: Integrating AI with Robotic Cardiac Surgery11. Bio-Inspired Machine Learning Classifiers for Breast Cancer Data Analysis: A WEKA-Based Optimization Approach for Robotic Surgery12. Deep Learning for ECG-Based Arrhythmia Detection and Classification: Architectures, Challenges, and Clinical Translation13. Artificial Intelligence Frameworks for Cardiovascular Diagnosis: From Data Processing to Model Selection, Evaluation, and Clinical Deployment14. Robotic Surgery and Cardiac Bio-Signals: Bridging Human-AI Collaboration15. Federated Learning and Privacy-Preserving AI for Cardiac Signal Analysis in Robotic Surgery