Machine Learning Models and Architectures for Biomedical Signal Processing presents the fundamental concepts of machine learning techniques for bioinformatics in an interactive way. The book investigates how efficient machine and deep learning models can support high-speed processors with reconfigurable architectures like graphic processing units (GPUs), Field programmable gate arrays (FPGAs), or any hybrid system. This great resource will be of interest to researchers working to increase the efficiency of hardware and architecture design for biomedical signal processing and signal processing techniques.
1. Machine Learning Models and Architectures for Biomedical Signal Processing - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page vi 7. List of contributors - p. Page vii 8. Preface - p. Page vii 9. Acknowledgments - p. Page ix 10. List of Illustrations - p. Page xii 11. List of Tables - p. Page xiii 12. 1 : Recent trends in biomedical informatics - p. Page xiii 13. 2 : Biomedical signal processing technique - p. Page 16 14. 3 : Transfer learning-based arrhythmia classification using electrocardiogram - p. Page 41 15. 4 : Exploring machine learning models for biomedical signal processing: a comprehensive review - p. Page 62 16. 5 : Machine learning for audio processing: from feature extraction to model selection - p. Page 94 17. 6 : Enhancing insights: unravelling the potential of preprocessing MRI for artificial intelligence based Alzheimer's disease classification - p. Page 120 18. 7 : Machine learning models for text and image processing - p. Page 143 19. 8 : Assistive technology for neuro-rehabilitation applications using machine learning techniques - p. Page 173 20. 9 : Deep learning architectures in computer vision based medical imaging applications with emerging challenges - p. Page 188 21. 10 : Relevance of artificial intelligence, machine learning, and biomedical devices to healthcare quality and patient outcomes - p. Page 224 22. 11 : Artificial intelligence-based electrocardiogram signal processing applications - p. Page 263 23. 12 : Deep learning approach for the prediction of skin diseases - p. Page 295 24. 13 : Brain–computer interface - p. Page 317 25. 14 : Human-computer interface developments include systems that can decipher enhanced human language and contextual cues while interacting with digital devices - p. Page 348 26. 15 : Brain-computer interfaces for elderly and disabled persons - p. Page 363 27. 16 : Machine learning model implementation with FPGAs - p. Page 391 28. 17 : Smart biomedical devices for smart healthcare - p. Page 419 29. 18 : FPGA implementation for explainable machine learning and deep learning models to real-time problems - p. Page 443 30. 19 : Software applications for biometric informatics - p. Page 465 31. 20 : Smart medical devices: making healthcare more intelligent - p. Page 485 32. 21 : Security modules for biomedical signal processing using Internet of Things - p. Page 500 33. 22 : Artificial intelligence-based diagnostic tools for cardiovascular risk prediction - p. Page 524 34. 23 : Machine learning algorithm approach in risk prediction of liver cancer - p. Page 543 35. Index - p. Page 569
Machine Learning Models and Architectures for Biomedical Signal Processing presents the fundamental concepts of machine learning techniques for bioinformatics in an interactive way. The book investigates how efficient machine and deep learning models can support high-speed processors with reconfigurable architectures like graphic processing units (GPUs), Field programmable gate arrays (FPGAs), or any hybrid system. This great resource will be of interest to researchers working to increase the efficiency of hardware and architecture design for biomedical signal processing and signal processing techniques.
1. Machine Learning Models and Architectures for Biomedical Signal Processing - p. Page i 2. Cover - p. Page i 3. Title page - p. Page ii 4. Table of Contents - p. Page iii 5. Copyright - p. Page iii 6. Contents - p. Page vi 7. List of contributors - p. Page vii 8. Preface - p. Page vii 9. Acknowledgments - p. Page ix 10. List of Illustrations - p. Page xii 11. List of Tables - p. Page xiii 12. 1 : Recent trends in biomedical informatics - p. Page xiii 13. 2 : Biomedical signal processing technique - p. Page 16 14. 3 : Transfer learning-based arrhythmia classification using electrocardiogram - p. Page 41 15. 4 : Exploring machine learning models for biomedical signal processing: a comprehensive review - p. Page 62 16. 5 : Machine learning for audio processing: from feature extraction to model selection - p. Page 94 17. 6 : Enhancing insights: unravelling the potential of preprocessing MRI for artificial intelligence based Alzheimer's disease classification - p. Page 120 18. 7 : Machine learning models for text and image processing - p. Page 143 19. 8 : Assistive technology for neuro-rehabilitation applications using machine learning techniques - p. Page 173 20. 9 : Deep learning architectures in computer vision based medical imaging applications with emerging challenges - p. Page 188 21. 10 : Relevance of artificial intelligence, machine learning, and biomedical devices to healthcare quality and patient outcomes - p. Page 224 22. 11 : Artificial intelligence-based electrocardiogram signal processing applications - p. Page 263 23. 12 : Deep learning approach for the prediction of skin diseases - p. Page 295 24. 13 : Brain–computer interface - p. Page 317 25. 14 : Human-computer interface developments include systems that can decipher enhanced human language and contextual cues while interacting with digital devices - p. Page 348 26. 15 : Brain-computer interfaces for elderly and disabled persons - p. Page 363 27. 16 : Machine learning model implementation with FPGAs - p. Page 391 28. 17 : Smart biomedical devices for smart healthcare - p. Page 419 29. 18 : FPGA implementation for explainable machine learning and deep learning models to real-time problems - p. Page 443 30. 19 : Software applications for biometric informatics - p. Page 465 31. 20 : Smart medical devices: making healthcare more intelligent - p. Page 485 32. 21 : Security modules for biomedical signal processing using Internet of Things - p. Page 500 33. 22 : Artificial intelligence-based diagnostic tools for cardiovascular risk prediction - p. Page 524 34. 23 : Machine learning algorithm approach in risk prediction of liver cancer - p. Page 543 35. Index - p. Page 569
