Advanced Research in Electronic Devices for Biomedical and mHealth by Kesharwani, Rajesh Kumar; Kumar, Upendra; Keservani, Raj K.;

Short description:

Addresses the design challenges and research in electronic device applications in healthcare and biomedical systems, exploring the blending of mobile communications, network technologies, medical sensors, etc. for health diagnosis and monitoring. It looks at machine learning, CNNs, smartphone-based devices, IoT, and other smart technologies.

Long description:

This volume addresses the major design challenges and research potential in electronic device applications in healthcare and biomedical systems, exploring the blending of innovative mobile communications, network technologies, and medical sensor and ubiquitous computing devices with medical and biological applications. The authors explore current and future trends in new communication and network technologies for healthcare delivery and new wireless telemedical and mobile health services. The chapters look at the application of machine learning, convolutional neural networks, smartphone-based devices, IoT sensors, and other smart technologies for health diagnosis and monitoring. The volume also looks at integrated circuit design for healthcare applications. The design of energy harvesting systems for a low power biomedical applications is considered, and another unique chapter illustrates the ability of mHealth technologies by using machine learning to predict which blood groups provide resistance against the COVID-19 Delta variant.

The main driving forces for the transformation of current healthcare systems are the growing aging population, sharp rising healthcare costs, and frequent occurrences of chronic diseases, resulting in the need to deliver healthcare services in more cost-effective and responsive ways. The traditional hospital-centered healthcare systems, which mainly focus on diagnosis and treatment, are now ready to transform into individual-centered based healthcare system, which, in turn, focuses primarily on early detection, early diagnosis, and long-term monitoring. Electronic devices for biomedical and mHealth are facilitating this transformation in innovative ways.

This volume, Advanced Research in Electronic Devices for Biomedical and mHealth, provides a selection of insightful chapters on topics that will be of interest to researchers, faculty, and industry professionals in the fields of biophysics, biomedical engineering, healthcare systems, medical informatics, bioinformatics, and digital electronics devise design.

Table of Contents:

1. Machine Learning in the Healthcare Domain 2. Emerging Trends in IoT with Machine Learning Techniques for Biomedical and Healthcare Systems 3. Machine Learning Techniques Used for Diagnosing Cardiac Abnormalities Using Electrocardiograms 4. Predicting Resilience of Blood Groups Against the COVID-19 Delta Variant Using Machine Learning 5. A Key Role of Convolutional Neural Networks in Biomedical Imaging Applications 6. The Most Recent Developments and Applications of Smartphone-Based Devices in Biomedical and mHealth 7. Real-Time Health Monitoring Using IoT Sensors 8. IC Design: An Approach of Study in Biomedical and mHealth Care Applications 9. Experimental Setup and Analysis of Energy Harvesting Systems for a Low Power Biomedical Application: Lab on Chip Application of Impedance Micropump and Microfluidic Logic Gate 10. Analysis and Application of Power Amplifiers to Biomedical Instrumentation