New Frontiers of Cardiovascular Screening using Unobtrusive Sensors, AI, and IoT (e-bog) af Pal, Arpan
Pal, Arpan (forfatter)

New Frontiers of Cardiovascular Screening using Unobtrusive Sensors, AI, and IoT e-bog

1021,49 DKK (inkl. moms 1276,86 DKK)
New Frontiers of Cardiovascular Screening using Unobtrusive Sensors, AI, and IoT provides insights into real-world problems in cardiovascular disease screening that can be addressed via AI, IoT and wearable based sensing. Non-Communicable Diseases (NCD) are surpassing CDS and emerging as the foremost cause of death. Hence, early screening of CVDs using wearable and other similar sensors is an e...
E-bog 1021,49 DKK
Forfattere Pal, Arpan (forfatter)
Udgivet 9 juli 2022
Længde 232 sider
Genrer Engineering: general
Sprog English
Format epub
Beskyttelse LCP
ISBN 9780128245002
New Frontiers of Cardiovascular Screening using Unobtrusive Sensors, AI, and IoT provides insights into real-world problems in cardiovascular disease screening that can be addressed via AI, IoT and wearable based sensing. Non-Communicable Diseases (NCD) are surpassing CDS and emerging as the foremost cause of death. Hence, early screening of CVDs using wearable and other similar sensors is an extremely important global problem to solve. The digital health field is constantly changing, and this book provides a review of recent technology developments, offering unique coverage of processing time series physiological sensor data. The authors have developed this book with graduate and post graduate students in mind, making sure they provide an accessible entry point into the field. This book is particularly useful for engineers and computer scientists who want to build technologies that work in real world scenarios as it provides a practitioner's view/insights /tricks of the trade. Finally, this book helps researchers working on this important problem to quickly ramp up their knowledge and research to the state-of-the-art. Maps digital health technology to real diseases that are relevant to the medical community Supported with patient data and case studies Gives practitioners insights into the real-world implementation of signal conditioning, signal processing and machine learning