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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. 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
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978-0-443-45243-7 (9780443452437)
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Part I: Fundamental of Cardiac Signals and Deep Learning1. CARDIO-AI: Compliance and AI Regulation for Deep Learning in ECG and Cardiac Signal Interpretation2. Autoencoders in Cardiology: Opportunities and Challenges for Clinical Integration3. Attention-Driven Convolutional Autoencoder-LSTM Deep Learning for Arrhythmia Detection and Classification4. A Novel Deep Learning Framework for Arrhythmia Detection and Classification in Robotic-Assisted Cardiac Surgery5. HTCB-AF : Hybrid-Transformer CNN-BiGRU with Attention-Guided Beat Fusion for Explainable Arrhythmia DetectionPart II: AI-Enhanced Cardiac Signal Analysis6. Automated detection of posterior myocardial infarction using dynamical pattern of optimized 2D plot of dVCG signals and geometrical features7. Advancing Diabetes Management: Machine Learning-Based Non-Invasive Glucose Monitoring with Wearable PPG Sensors8. Deep Learning for Atrial Fibrillation Detection from ECG Signals9. AI-Guided Robotic Cardiac Interventions: Precision and Safety10. A Comprehensive Review of Algorithmic Approaches in Generative Artificial Intelligence: Trends, Techniques, and Future DirectionsPart III: Integrating AI with Robotic Cardiac Surgery11. Bio-Inspired Machine Learning Classifiers for Breast Cancer Data Analysis: A WEKA-Based Optimization Approach for Robotic Surgery12. Deep Learning for ECG-Based Arrhythmia Detection and Classification: Architectures, Challenges, and Clinical Translation13. Artificial Intelligence Frameworks for Cardiovascular Diagnosis: From Data Processing to Model Selection, Evaluation, and Clinical Deployment14. Robotic Surgery and Cardiac Bio-Signals: Bridging Human-AI Collaboration15. Federated Learning and Privacy-Preserving AI for Cardiac Signal Analysis in Robotic Surgery