Pervasive Cardiovascular and Respiratory Monitoring Devices: Model-Based Design. Este artículo no está disponible.Pervasive Cardiovascular and Respiratory Monitoring Devices: Model-Based Design

Pervasive Cardiac and Respiratory Monitoring Devices: Model-Based Design is the first book to combine biomedical instrumentation and model-based design. As the scope is limited to cardiac and respiratory devices only, this book offers more depth of information on these devices; focusing in on signals used for home monitoring and offering additional analysis of these devices. The author offers an insight into new industry and research trends, including advances in contactless monitoring of breathing and heart rate. Each chapter presents a section on current trends. As instrumentation as a field is becoming increasingly smart, basic signal processing is also discussed. Real case-studies for each modelling approach are used, primarily covering blood pressure, ECG and radar-based devices. This title is ideal for teaching and supporting learning as it is written in an accessible style and a solutions manual for the problem sets is provided. It will be useful to 4th year undergraduate students, graduate/masters/PhD students, early career researchers and professionals working on an interdisciplinary project; as it introduces the field and provides real world applications. For engineers this book solves the problem of how to assess and calibrate a medical device to ensure the data collected is trustworthy. For students, this book allows for trying concepts and circuits via simulations and learning modeling techniques. Students will learn concepts from this book and be ready to design bioinstrumentations devices based on specifications/requirements.

Miodrag Bolic received his Ph.D. degree in electrical engineering from Stony Brook University, US, in 2004. Since 2004 he has been with the University of Ottawa, Canada where he is Professor with the School of Electrical Engineering and Computer Science. He is a Director of the Computational Analysis and Health Devices research groups. His current research interests include physics based machine learning and uncertainty quantification for biomedical and autonomous vehicles applications. He has published about 80 journal papers and has 5 patents. He teaches at the graduate level courses on biomedical instrumentation and uncertainty quantification in engineering and machine learning.
Prof. Bolic worked on cardiorespiratory monitoring for the last 12 years including both the design of devices and development of signal processing/machine learning algorithms. His work on wearable devices include ECG-assisted oscillometric and BCG-assisted cuffless blood pressure monitoring. His work on contactless monitoring includes breathing and heart rate estimation and breathing pattern classification using RGB, thermal and 3D cameras as well as radars.