Pervasive Cardiovascular and Respiratory Monitoring Devices: Model-Based Design
Miodrag Bolic, University of Ottawa
Academic Press, 2023
ISBN: 9780128209479;
Language: English
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.
Key Features
- Focuses on model-based design using Simscape and MATLAB; learn how to design a system and how to evaluate how different choices affect the output of the system
- Covers pervasive monitoring: shows how to design optimal solutions for pervasive and personalized healthcare monitoring
- Explores uncertainty and sensitivity analysis; understand your model better
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