Deep Learning for Cardiac Signal Analysis in Robotic Applications (Medical Robotics and Computer Assisted Surgery: AI-enhanced, Data-driven, and Evidence-based Approaches) - Softcover

Softcover

ISBN 10: 0443452423 ISBN 13: 9780443452420

Publisher: Academic Press, 2026

Synopsis

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.

• 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

About this title

About the Authors

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.

From the Back Cover

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. Pre-processing 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.

Edition details

• Publisher: Academic Press
• Publication date: 2026
• Language: English
• ISBN 10: 0443452423
• ISBN 13: 9780443452420
• Binding: Paperback
• Edition number: 1
• Number of pages: 300
• Editor: Gupta PhD Kapil, Bajaj PhD Varun