Full Description
This book is an essential guide to the constantly changing environment of embedded systems in healthcare in a world where the convergence of technology and healthcare is becoming increasingly important. It further explains different scenarios corresponding to the latest technologies in the healthcare system for early diagnosis, enhanced treatment, and cure of diseases, including remote patient monitoring, cardiac monitoring, and deep learning for remediation.
Features:
• Emphasizes how embedded systems contribute to clinical care by facilitating personalized treatment and informed decision-making for healthcare professionals.
• Highlights the role of embedded systems in tracking treatment progress, enabling healthcare professionals to monitor patient responses and adjust treatment plans accordingly.
• Explores the application of embedded systems in remote patient monitoring, allowing for continuous health monitoring outside traditional healthcare settings.
• Presents the integration of deep learning and telecommunication technology with embedded systems, optimizing their efficiency and utilization in biomedical applications.
• Offers insights into future prospects for advancing biomedical applications through embedded systems, providing a roadmap for further innovation and development in the field.
This reference work is useful for scholars and professionals interested in the applications and optimization of emerging smart technologies in the field of healthcare.
Contents
1. Introduction to Embedded System in Healthcare: Operational Environments in Healthcare Settings 2. Embedded Systems in the medical field: pros. and cons. 3. Automation of in the medical field: pros. and cons. 4. Embedded system in developing imaging devices 5. Deep Learning of Telecommunication Technology for The Efficient Utilization of Embedded Systems 6. Role of Embedded Systems in Diagnosis and Decision-Making for Better Treatment 7. Enhancing Remote Healthcare: Integration of ATOM ECG with eSanjeevani 8. Artificial Intelligence in Embedded Systems for Biomedical Applications 9. Embedded systems in metabolic bariatric surgery: from telemonitoring to deep learning applications. 10. Integration of Machine Learning Techniques with Multi-Criteria Decision-Making for a Better Treatment 11. Embedded systems in developing advanced strategies in healthcare management 12. Human Health Status Detection from the Tongue Image Using Embedded Image Processing
