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.
