Technological advancements have enhanced all functions of society and revolutionized the healthcare field. Smart healthcare applications and practices have grown within the past decade, strengthening overall care. Biomedical signals observe physiological activities, which provide essential information to healthcare professionals. Biomedical signal processing can be optimized through artificial intelligence (AI) and machine learning (ML), presenting the next step towards smart healthcare. AI-Enabled Smart Healthcare Using Biomedical Signals will not only cover the mathematical description of the AI- and ML-based methods, but also analyze and demonstrate the usability of different AI methods for a range of biomedical signals. The book covers all types of biomedical signals helpful for smart healthcare applications. Covering topics such as automated diagnosis, emotion identification, and frequency discrimination techniques, this premier reference source is an excellent resource for healthcare administration, biomedical engineers, medical laboratory technicians, medical technology assistants, computer scientists, libraries, students and faculty of higher education, researchers, and academicians.

The book provides details regarding the application of various signal processing and artificial intelligence-based methods for electroencephalography data analysis. It will help readers in understanding the use of electroencephalography signals for different neural information processing and cognitive neuroscience applications. The book: Covers topics related to the application of signal processing and machine learning-based techniques for the analysis and classification of electroencephalography signals Presents automated methods for detection of neurological disorders and other applications such as cognitive task recognition, and brain-computer interface Highlights the latest machine learning and deep learning methods for neural signal processing Discusses mathematical details for the signal processing and machine learning algorithms applied for electroencephalography data analysis Showcases the detection of dementia from electroencephalography signals using signal processing and machine learning-based techniques It is primarily written for senior undergraduates, graduate students, and researchers in the fields of electrical engineering, electronics and communications engineering, and biomedical engineering.

Traditional patient care and treatment approaches often lack the personalized and interactive elements necessary for effective healthcare delivery. This means that the healthcare industry must find innovative solutions to improve patient outcomes, enhance rehabilitation processes, and optimize resource utilization. There is a gap between the traditional approach and the need for innovation that highlights the importance of a comprehensive understanding of emerging technologies, including Kinect Sensor technology, and the potential to transform healthcare practices with this tech. Revolutionizing Healthcare Treatment With Sensor Technology addresses this critical need by thoroughly exploring how Kinect Sensor technology can revolutionize patient care and treatment methodologies. By repurposing and customizing Kinect Sensor for healthcare applications, this book showcases how depth-sensing cameras, infrared sensors, and advanced motion tracking can capture and interpret real-time patient movements and interactions. This book is ideal for healthcare professionals, hospital administrators, researchers, patients, caregivers, and healthcare technology developers seeking to leverage Kinect Sensor technology for enhanced healthcare delivery. Through detailed case studies and practical examples, experts can learn how to integrate Kinect Sensor into various medical settings to gain valuable insights into patients' physical capabilities, monitor their progress, and create personalized treatment plans.

A chasm grows between the currently established knowledge and the rapidly evolving landscape of healthcare. As the field of biomedical research hurtles forward with groundbreaking discoveries and transformative technologies, academic scholars find themselves grappling with a significant dilemma. There exists a disconnect between traditional educational resources and the need to keep pace with the latest innovations that are reshaping medicine, diagnosis, and treatment. This widening gap inhibits scholars from adequately preparing their students and hampers their ability to engage in relevant, cutting-edge research, ultimately impeding the advancement of healthcare as a whole. Biomedical Research Developments for Improved Healthcare serves as the ultimate solution to this academic challenge. This book offers a compelling bridge between the realm of academic theory and the dynamic world of practical, real-world biomedical research. Its primary objective is to equip scholars with the knowledge, insights, and materials needed to inspire the next generation of healthcare professionals. By presenting a comprehensive overview of the most recent and groundbreaking advancements in biomedical research, the book enables scholars to transcend the limitations of traditional academia and empower their students with up-to-date, practical knowledge.

The convergence of quantum technologies and biomedical intelligence is a frontier of boundless potential. The quantum advancements revolutionize disease detection, personalized medicine, and health monitoring frameworks while confronting the pressing challenge of accountability in machine learning systems within the biomedical domain. How do quantum innovations at the nexus of biomedical intelligence redefine biomedical research and healthcare, addressing critical inquiries such as the transformative potential of quantum computing, machine learning, and sensing technologies? Quantum Innovations at the Nexus of Biomedical Intelligence explores the intricate synergy between quantum mechanics and the biomedical domain. This book elucidates the profound implications and applications arising from the fusion of quantum computing, artificial intelligence, and biomedical sciences. This book introduces biomedical engineering, setting the stage for a deep dive into the transformative role of quantum computing and artificial intelligence. As the narrative unfolds, the text navigates the reader through the uncharted territories of quantum-enhanced machine learning, quantum sensing and their profound impact on diagnostics, personalized medicine, and health monitoring frameworks. The intersection of quantum computing and AI in medical advancements and cybersecurity is illuminated, offering a comprehensive understanding of the multifaceted applications of these cutting-edge technologies. This book is ideal for researchers, scientists, academics, and professionals across diverse disciplines in quantum innovations within biomedical intelligence.

Instructional technologies used to be optional and supplemental pedagogical tools until the global health crisis of 2020 compelled education systems to rely on digital devices and services to guarantee academic continuity. Suddenly, the contemporary principles and practices utilized in delivering health education curricula were insufficient and ineffective. Acknowledging the vital role of technology in shaping the future of education, there is now a greater demand to foster innovative interventions and continuous improvement in strategies, methodologies, and systems to empower learners, educators, and leaders in the digital age. This paradigm shift requires a fundamental transformation in the way we approach teaching and learning, and a willingness to embrace new approaches and tools that can enhance the quality of education and support student success. The Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines provides comprehensive coverage of innovative methods and strategies to produce the next generation of health professionals. The book lays the groundwork for implementable teaching and learning models that facilitate knowledge acquisition, enhance perceptual variation, improve skill coordination, and develop a scientific and technological mindset. Each chapter provides an in-depth examination of instructional technologies contextualized in various medical and health domains, including nursing, physiotherapy, radiology, neurophysiology, physical health, dentistry, clinical medicine, and more. This reference work is a must-read for all stakeholders in health education and related fields, including educators, students, researchers, administrators, and healthcare professionals.

Glial cells, including microglia, astrocytes, oligodendrocytes, and their progenitors NG2-glia, serve as key players in maintaining structural integrity and complex brain homeostasis. They actively participate in neurotransmission, energy metabolism, synaptic plasticity, neurogenesis, ion balance, immune defense, and the clearance of neuronal debris. However, the physiological functions of glial cells are often compromised in aging, neurodegenerative diseases such as Alzheimer's, Parkinson's, ALS, and multiple sclerosis, as well as in gliomas, brain tumors demanding specialized understanding for effective therapeutic interventions. Physiology and Function of Glial Cells in Health and Disease provides a comprehensive exploration of the vital role played by glial cells in maintaining neural homeostasis within the central nervous system (CNS). This book delves into the intricate interaction between glial cells and neurons, shedding light on their essential contributions to neural function and overall brain health. The book also highlights emerging research on astrocyte reprogramming for the management of neurodegenerative diseases, offering a glimpse into potential future therapies. This book is an essential resource for researchers, clinicians, and students in the field of neuroscience. Its academic tone, coupled with in-depth discussions and cutting-edge insights, makes it a valuable reference for anyone seeking a comprehensive understanding of the role of glial cells in both health and disease.

The application of genetic engineering techniques by redesigning and repurposing biological systems for novel biotechnical applications has paved the way for the field of synthetic biology. This field boosted the evolution and discovery of various novel technologies essential to the conquest of biological problems related to health, disease, the environment, and energy. The field of synthetic biology is growing rapidly, and further research is required. Applications of Synthetic Biology in Health, Energy, and Environment deliberates on principles and the advancement of synthetic biology and their translation in the fields of health, disease, energy, and the environment. Covering topics such as climate change, bioremediation, and smart drugs, this premier reference source is an excellent resource for students and educators of higher education, industrialists, medical professionals, hospital administrators, policymakers, environmental scientists, pharmacists, librarians, researchers, and academicians.

This book reports on the latest advances in the study of biomedical signal processing, and discusses in detail a number of open problems concerning clinical, biomedical and neural signals. It methodically collects and presents in a unified form the research findings previously scattered throughout various scientific journals and conference proceedings. In addition, the chapters are self-contained and can be read independently. Accordingly, the book will be of interest to university researchers, R&D engineers and graduate students who wish to learn the core principles of biomedical signal analysis, algorithms, and applications, while also offering a valuable reference work for biomedical engineers and clinicians who wish to learn more about the theory and recent applications of neural engineering and biomedical signal processing.

Why are cutting-edge data science techniques such as bioinformatics, few-shot learning, and zero-shot learning underutilized in the world of biological sciences?. In a rapidly advancing field, the failure to harness the full potential of these disciplines limits scientists’ ability to unlock critical insights into biological systems, personalized medicine, and biomarker identification. This untapped potential hinders progress and limits our capacity to tackle complex biological challenges. The solution to this issue lies within the pages of Applying Machine Learning Techniques to Bioinformatics. This book serves as a powerful resource, offering a comprehensive analysis of how these emerging disciplines can be effectively applied to the realm of biological research. By addressing these challenges and providing in-depth case studies and practical implementations, the book equips researchers, scientists, and curious minds with the knowledge and techniques needed to navigate the ever-changing landscape of bioinformatics and machine learning within the biological sciences.