Details

No Article Processing and Publication Fee.

The Chapters of Elsevier book appear in Science Direct.

About the Theme 

Robotic-assisted surgery has advanced dramatically in recent years, redefining precision and efficiency in complex medical procedures. Cardiac surgery, in particular, poses unique challenges due to the dynamic nature of heart impulses and the vital necessity for real-time decision making. Deep Learning for Heart Signal Analysis in Robotic Surgery investigates the use of artificial intelligence and deep learning techniques to improve the accuracy, safety, and adaptability of robotic surgical systems during cardiovascular operations. This book gives a thorough introduction of deep learning approaches designed specifically for analyzing electrocardiograms (ECGs), vectorcardigram (VCG), ballistocardiogram (BCG), and other critical bio-signals in robotic surgery contexts. It covers critical issues such data acquisition, preprocessing, feature extraction, anomaly detection, and real-time decision support for surgeons. The book uses cutting-edge neural networks, such as convolutional neural networks (CNNs), explainable Al, recurrent neural networks (RNNs), and transformer-based models, to show how Al- driven analysis can enhance patient outcomes, reduce complications, and aid with surgical navigation.

This book, intended for researchers, physicians, biomedical engineers, and Al practitioners, bridges the gap between cutting-edge Al developments and their practical application in robotic cardiac surgery. It also discusses regulatory considerations, ethical consequences, and the future of deep learning in healthcare robotics. Deep Learning for Heart Signal Analysis in Robotic Surgery is an important resource for furthering the field of Al-driven precision medicine due to its interdisciplinary approach.

Specific topics (but not limited to) are:

• The Rhythm of the Heart: Understanding Cardiac Modalities
• Deep Learning Essentials for Cardiac Signal Processing
• Pre-processing and Feature Extraction of Cardiac Signals
• Case Studies from Diverse Healthcare Settings
• Deep Learning for Arrhythmia Detection and Classification
• Myocardial Ischemia and Infarction: Deep Learning-Based Diagnostics
• Hypertension Monitoring and Prediction with BCG signal processing
• Monitoring of Arrhythmic Fetus using explainable AI
• Real-Time Cardiac Signal Integration in Robotic Surgical Systems
• AI-Guided Robotic Cardiac Interventions: Precision and Safety
• Intraoperative Cardiac Monitoring and Decision Support with Al
• Post-Operative Cardiac Monitoring and Outcome Prediction
• Future Directions and Emerging Trends in Cardiac AI and Robotic Surgery.
• XAI Techniques Applicable to Robotic Cardiac Systems

Submission Process 

Scientists, researchers, academicians, research scholars and others working in the field of BCI using Al are requested to express their interest by writing to us at elsevier.book1@gmail.com with CC to kapil.gupta@ddn.upes.ac.in and varun.bajaj@manit.ac.in

Important Dates

• Submission of Abstract (100-200 Words): 1st July 2025
• Acceptance of Abstract: 5th July 2025
• Submission of Full Chapter: 5th September,, 2025
• Acceptance of Chapter: 10th September, 2025
• Submission of Final Chapter (Revised with Permission & Copyright): 25st September, 2025

Editors:

Dr. Kapil Gupta University of Petroleum and Energy Studies Dehradun, Uttarakhand

Dr. Varun Bajaj Maulana Azad National Institute of Technology Bhopal 462003 MP India