Deep Learning for Cardiac Signal Analysis in Robotic Applications
- 1st Edition - May 1, 2026
- Latest edition
- Editors: Kapil Gupta, Varun Bajaj
- Language: English
"Deep Learning for Cardiac Signal Analysis in Robotic Applications" delves into the transformative role of artificial intelligence in enhancing robotic-assisted cardiovas… Read more
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"Deep Learning for Cardiac Signal Analysis in Robotic Applications" delves into the transformative role of artificial intelligence in enhancing robotic-assisted cardiovascular procedures. 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 book is organized into three parts, starting with the fundamentals of cardiac signals and deep learning. It introduces key heart modalities, including the physiological underpinnings and challenges of signals like ECG and BCG, followed by an overview of deep learning architectures relevant to signal processing. Preprocessing and feature extraction techniques are detailed to prepare readers for advanced analysis. Part II focuses 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. This book is an invaluable resource for engineering students and academicians seeking to deepen their understanding of AI applications in healthcare. It equips readers with practical knowledge to tackle challenges in cardiac signal processing and robotic application, fostering interdisciplinary expertise that spans biomedical engineering, computer science, and clinical practice. This book not only advances academic research but also supports innovation in developing intelligent surgical systems and improving patient care.
• Bridges deep learning techniques with practical cardiac robotic surgery applications for improved clinical outcomes and safety
• Offers clear explanations and case studies to simplify complex AI concepts for multidisciplinary audiences
• Provides comprehensive coverage of cardiac signal processing, including noise reduction and anomaly detection methods
• Highlights real-time AI integration for enhanced surgical decision-making and precision in robotic interventions
• Explores ethical, regulatory, and future trends in AI-assisted cardiac healthcare and robotic surgery advancements
• Offers clear explanations and case studies to simplify complex AI concepts for multidisciplinary audiences
• Provides comprehensive coverage of cardiac signal processing, including noise reduction and anomaly detection methods
• Highlights real-time AI integration for enhanced surgical decision-making and precision in robotic interventions
• Explores ethical, regulatory, and future trends in AI-assisted cardiac healthcare and robotic surgery advancements
• Researchers and practitioners in biomedical engineering and artificial intelligence focused on cardiac healthcare innovations • Physicians and cardiac surgeons interested in integrating AI and robotic technologies into clinical practice • Graduate students and academicians in computer science, electronics, and medical sciences exploring deep learning applications
Part I: Fundamental of Cardiac Signals and Deep Learning
1. The Rhythm of the Heart: Understanding Cardiac Modalities
2. Deep Learning Essentials for Cardiac Signal Processing
3. Pre-processing and Feature Extraction of Cardiac Signals
4. Case Studies from Diverse Healthcare Settings
Part II: AI-Enhanced Cardiac Signal Analysis
5. Deep Learning for Arrhythmia Detection and Classification
6. Myocardial Ischemia and Infarction: Deep Learning-Based Diagnostics
7. Hypertension Monitoring and Prediction with BCG Signal Processing
8. Monitoring of Arrhythmic Fetus Using Explainable AI
Part III: Integrating AI with Robotic Cardiac Surgery
9. Real-Time Cardiac Signal Integration in Robotic Surgical Systems
10. AI-Guided Robotic Cardiac Interventions: Precision and Safety
11. Intraoperative Cardiac Monitoring and Decision Support with AI
12. Post-Operative Cardiac Monitoring and Outcome Prediction
13. Future Directions and Emerging Trends in Cardiac AI and Robotic Surgery
14. XAI Techniques Applicable to Robotic Cardiac Systems
1. The Rhythm of the Heart: Understanding Cardiac Modalities
2. Deep Learning Essentials for Cardiac Signal Processing
3. Pre-processing and Feature Extraction of Cardiac Signals
4. Case Studies from Diverse Healthcare Settings
Part II: AI-Enhanced Cardiac Signal Analysis
5. Deep Learning for Arrhythmia Detection and Classification
6. Myocardial Ischemia and Infarction: Deep Learning-Based Diagnostics
7. Hypertension Monitoring and Prediction with BCG Signal Processing
8. Monitoring of Arrhythmic Fetus Using Explainable AI
Part III: Integrating AI with Robotic Cardiac Surgery
9. Real-Time Cardiac Signal Integration in Robotic Surgical Systems
10. AI-Guided Robotic Cardiac Interventions: Precision and Safety
11. Intraoperative Cardiac Monitoring and Decision Support with AI
12. Post-Operative Cardiac Monitoring and Outcome Prediction
13. Future Directions and Emerging Trends in Cardiac AI and Robotic Surgery
14. XAI Techniques Applicable to Robotic Cardiac Systems
- Edition: 1
- Latest edition
- Published: May 1, 2026
- Language: English
KG
Kapil Gupta
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.
Affiliations and expertise
Assistant Professor (Senior Scale), School of Computer Science, University of Petroleum and Energy Studies, Dehradun, IndiaVB
Varun Bajaj
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.
Affiliations and expertise
Assistant Professor, Indian Institute of Technology, Design, and Manufacturing, Bhopal, India