Deep Learning for Cardiac Signal Analysis in Robotic Applications
Published on 26. January 2026
Book
Paperback/Softback
978-0-443-45242-0 (ISBN)
Description
Deep Learning for Cardiac Signal Analysis in Robotic Applications delves into the transformative role of artificial intelligence in enhancing robotic-assisted cardiovascular procedures. The book starts with the fundamentals of cardiac signals and deep learning, introducing key heart modalities, including the physiological underpinnings and challenges of signals like ECG and BCG and an overview of deep learning architectures relevant to signal processing. Pre-processing and feature extraction techniques are detailed to prepare readers for advanced analysis. Other sections focus on AI-enhanced cardiac signal analysis, covering arrhythmia detection, myocardial ischemia diagnostics, hypertension monitoring via BCG, and explainable AI approaches for fetal arrhythmia monitoring.
The final section integrates AI with robotic cardiac surgery, addressing real-time signal integration, AI-guided intervention precision, intraoperative decision support, postoperative monitoring, and future trends in cardiac AI and robotic-assisted surgery. Addressing the complexities of heart signal interpretation amidst the dynamic environment of cardiac surgery, this book meets the critical need for a comprehensive resource that bridges deep learning advances with practical surgical applications. It responds to the challenge of understanding intricate bio-signals, such as ECG, VCG, and BCG, by providing clear explanations, case studies, and methodological insights tailored to improve surgical precision, safety, and patient outcomes.
The final section integrates AI with robotic cardiac surgery, addressing real-time signal integration, AI-guided intervention precision, intraoperative decision support, postoperative monitoring, and future trends in cardiac AI and robotic-assisted surgery. Addressing the complexities of heart signal interpretation amidst the dynamic environment of cardiac surgery, this book meets the critical need for a comprehensive resource that bridges deep learning advances with practical surgical applications. It responds to the challenge of understanding intricate bio-signals, such as ECG, VCG, and BCG, by providing clear explanations, case studies, and methodological insights tailored to improve surgical precision, safety, and patient outcomes.
More details
Other editions
Additional editions
Kapil Gupta | Varun Bajaj
Deep Learning for Cardiac Signal Analysis in Robotic Applications
E-Book
01/2026
Elsevier
€166.99
Available for download
Persons
Editor
Assistant Professor (Senior Scale), School of Computer Science, University of Petroleum and Energy Studies, Dehradun, India
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. 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.
Assistant Professor, Indian Institute of Technology, Design, and Manufacturing, Bhopal, India
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.
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.
Content
Part I: Fundamental of Cardiac Signals and Deep Learning
1. CARDIO-AI: Compliance and AI Regulation for Deep Learning in ECG and Cardiac Signal Interpretation
2. Autoencoders in Cardiology: Opportunities and Challenges for Clinical Integration
3. Attention-Driven Convolutional Autoencoder-LSTM Deep Learning for Arrhythmia Detection and Classification
4. A Novel Deep Learning Framework for Arrhythmia Detection and Classification in Robotic-Assisted Cardiac Surgery
5. HTCB-AF: Hybrid-Transformer CNN-BiGRU with Attention-Guided Beat Fusion for Explainable Arrhythmia Detection
Part II: AI-Enhanced Cardiac Signal Analysis
6. Automated detection of posterior myocardial infarction using dynamical pattern of optimized 2D plot of dVCG signals and geometrical features
7. Advancing Diabetes Management: Machine Learning-Based Non-Invasive Glucose Monitoring with Wearable PPG Sensors
8. Deep Learning for Atrial Fibrillation Detection from ECG Signals
9. AI-Guided Robotic Cardiac Interventions: Precision and Safety
10. A Comprehensive Review of Algorithmic Approaches in Generative Artificial Intelligence: Trends, Techniques, and Future Directions
Part III: Integrating AI with Robotic Cardiac Surgery
11. Bio-Inspired Machine Learning Classifiers for Breast Cancer Data Analysis: A WEKA-Based Optimization Approach for Robotic Surgery
12. Deep Learning for ECG-Based Arrhythmia Detection and Classification: Architectures, Challenges, and Clinical Translation
13. Artificial Intelligence Frameworks for Cardiovascular Diagnosis: From Data Processing to Model Selection, Evaluation, and Clinical Deployment
14. Robotic Surgery and Cardiac Bio-Signals: Bridging Human-AI Collaboration
15. Federated Learning and Privacy-Preserving AI for Cardiac Signal Analysis in Robotic Surgery
1. CARDIO-AI: Compliance and AI Regulation for Deep Learning in ECG and Cardiac Signal Interpretation
2. Autoencoders in Cardiology: Opportunities and Challenges for Clinical Integration
3. Attention-Driven Convolutional Autoencoder-LSTM Deep Learning for Arrhythmia Detection and Classification
4. A Novel Deep Learning Framework for Arrhythmia Detection and Classification in Robotic-Assisted Cardiac Surgery
5. HTCB-AF: Hybrid-Transformer CNN-BiGRU with Attention-Guided Beat Fusion for Explainable Arrhythmia Detection
Part II: AI-Enhanced Cardiac Signal Analysis
6. Automated detection of posterior myocardial infarction using dynamical pattern of optimized 2D plot of dVCG signals and geometrical features
7. Advancing Diabetes Management: Machine Learning-Based Non-Invasive Glucose Monitoring with Wearable PPG Sensors
8. Deep Learning for Atrial Fibrillation Detection from ECG Signals
9. AI-Guided Robotic Cardiac Interventions: Precision and Safety
10. A Comprehensive Review of Algorithmic Approaches in Generative Artificial Intelligence: Trends, Techniques, and Future Directions
Part III: Integrating AI with Robotic Cardiac Surgery
11. Bio-Inspired Machine Learning Classifiers for Breast Cancer Data Analysis: A WEKA-Based Optimization Approach for Robotic Surgery
12. Deep Learning for ECG-Based Arrhythmia Detection and Classification: Architectures, Challenges, and Clinical Translation
13. Artificial Intelligence Frameworks for Cardiovascular Diagnosis: From Data Processing to Model Selection, Evaluation, and Clinical Deployment
14. Robotic Surgery and Cardiac Bio-Signals: Bridging Human-AI Collaboration
15. Federated Learning and Privacy-Preserving AI for Cardiac Signal Analysis in Robotic Surgery