Non-Invasive Health Systems based on Advanced Biomedical Signal and Image Processing
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| ISBN13: | 9781032386942 |
| ISBN10: | 10323869411 |
| Kötéstípus: | Keménykötés |
| Terjedelem: | 474 oldal |
| Méret: | 234x156 mm |
| Súly: | 1040 g |
| Nyelv: | angol |
| Illusztrációk: | 210 Illustrations, black & white; 71 Illustrations, color; 52 Halftones, black & white; 25 Halftones, color; 158 Line drawings, black & white; 46 Line drawings, color; 147 Tables, black & white |
| 691 |
The book contains up-to-date noninvasive monitoring and diagnosing systems closely developed by a set of scientists, engineers, and physicians. The chapters are the results of different biomedical projects and theoretical studies that were coupled by simulations and real-world data.
This book contains up-to-date noninvasive monitoring and diagnosing systems closely developed by a set of scientists, engineers, and physicians. The chapters are the results of different biomedical projects and theoretical studies that were coupled by simulations and real-world data.
Non-Invasive Health Systems based on Advanced Biomedical Signal and Image Processing provides a multifaceted view of various biomedical and clinical approaches to health monitoring systems. The authors introduce advanced signal- and image-processing techniques as well as other noninvasive monitoring and diagnostic systems such as inertial sensors in wearable devices and novel algorithm-based hybrid learning systems for biosignal processing. The book includes a discussion of designing electronic circuits and systems for biomedical applications and analyzes several issues related to real-world data and how they relate to health technology including ECG signal monitoring and processing in the operating room. The authors also include detailed discussions of different systems for monitoring various conditions and diseases including sleep apnea, skin cancer, deep vein thrombosis, and prosthesis controls.
This book is intended for a wide range of readers including scientists, researchers, physicians, and electronics and biomedical engineers. It will cover the gap between theory and real life applications.
1. Upper Limb Recovery Prediction Based on Multilevel Mixed Effect EMG Synergy and Biomarker Values. 2. EMG Feature Extraction Based on Cardinality with Deep Learning Concepts. 3. Surface Electromyography Sensors for Human Activity Recognition: Recent Advancements and Perspectives. 4. ECG signal monitoring and processing in the operating room. 5. Photoplethysmography and Inertial Sensors in Wearable Devices for Healthcare: Multimodal Signal Processing for Increasing Accuracy. 6. Non-invasive system for measuring parameters relevant to sleep quality and detecting sleep diseases: The data model. 7. PREDICTING INCIDENCE OF STROKE VIA SUPERVISED MACHINE LEARNING METHODS ON CLASS IMBALANCED DATA. 8. Ultrasound vector flow imaging, a promising technique towards a new carotid atheroma risk stratification. 9. A Pre-screening Technique for Coronary Artery Disease with Multi-channel Phonocardiography and Electrocardiography. 10. Exploring the feasibility of estimating the carotid-to-femoral pulse wave velocity using machine learning algorithms. 11. DVT Diagnosis based on HOS & Scattering Operators. 12. Non-invasive AI-assisted Techniques for 3D Printing of the Heart via Image Analysis: Current State, Challenges, and Future Directions. 13. COVID-19 and Pneumonia Detection System using Deep Learning with Chest X-ray Images. 14. Scattering Operators and high order statistics along with Elastography to Identify & Characterize Salivary Gland Abnormalities. 15. SA-SVM Classification-Based Smart Feature Selection Algorithm for Skin Cancer Detection. 16. A Review on Advanced CNN Architecture in Diagnosing Alzheimer?s Disease. 17. Combination of sensors-based monitoring system and internet of things (IoT): A survey and framework for remote and intensive care unit patients. 18. End-to end solutions for the Remote Monitoring of Post-operative Prehabilitation program: IoT solution challenges.