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.

Coverage:
The many academic areas covered in this publication include, but are not limited to:
•Adaptive Data Analysis
•Advanced Image Decomposition
•Automated Diagnosis
•Biomedical Signal Processing
•Brain Simulation
•Common Feature Analysis
•ECG Signal
•Emotion Identification
•Frequency Discrimination Techniques
•Multimedia Learning
•Neuroimaging Techniques
•Retinal Fundus Images