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.

• 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