Non-Stationary and Nonlinear Data Processing for Automated Computer-Aided Medical Diagnosis demonstrates the applications of machine learning and deep learning combined with signal processing techniques for human-machine interface applications using EMG signals. The book includes the analysis and classification of various heart diseases based on bio-signals like electrocardiogram (ECG), photoplethysmography (PPG), and phonocardiogram (PCG) signals. Various machine learning approaches, including advanced algorithms like multivariate signal processing, time-frequency analysis, and nonlinear signal processing are covered for CAD of neural, muscular, and cardiovascular diseases. The methods for CAD of various brain disorders are also included.
Presented techniques utilize advanced non-stationary and nonlinear signal processing, along with machine learning and deep learning-based classification processes. CAD methods for diagnosing various neurological diseases are based on bio-signals such as electroencephalogram (EEG) and magnetoencephalogram (MEG), as well as medical images like magnetic resonance imaging (MRI) and computerized tomography (CT). Finally, the book addresses various types of medical signals and images, integrating nonlinear and non-stationary signal processing, machine learning, and deep learning within the CAD framework for diagnosing various diseases.
1. Chapter 1: Introduction to computer-aided medical diagnosis (CAMD) systems - p. 1 2. Chapter 2: Advanced signal processing and machine learning techniques for computer-aided medical diagnosis - p. 15 3. Chapter 3: EEG based imagined speech recognition for BCI applications - p. 41 4. Chapter 4: Study of recent trends in the detection of stress for human well-being - p. 87 5. Chapter 5: Automated visual attention analysis system using non-Euclidean feature analysis through EEG signals - p. 115 6. Chapter 6: ECG sensor-based devices for cardiac disease diagnosis - p. 133 7. Chapter 7: Automated detection of Parkinson's disease using speech signals - p. 151 8. Chapter 8: Automated emotion detection using multimodal physiological signals from wearables - p. 165 9. Chapter 9: Automated brain tumor diagnosis using MRI - p. 187 10. Chapter 10: Deep learning techniques for automated ophthalmic disease diagnosis using fundus images - p. 203 11. Chapter 11: PPG-based diagnosis system for atrial fibrillation - p. 217 12. Chapter 12: EMG signal-based devices for neuromuscular diseases - p. 231 13. Chapter 13: Wearable systems for real-time disease diagnosis and predictive analytics - p. 269 14. Chapter 14: Deep representation learning for computer-aided detection of pneumonia and tuberculosis using chest X-ray images - p. 311 15. Chapter 15: Computer-aided detection of kidney diseases using ultrasound images - p. 325 16. Chapter 16: IoT-enabled diagnosis system for telemedicine applications - p. 339 17. Chapter 17: Automated brain tumor diagnosis using MRI - p. 351 18. Chapter 18: Nonstationary signal processing techniques in EEG for rehabilitation and treatment using brain–computer interfaces - p. 369 19. Chapter 19: Fourier–Bessel domain discrete Stockwell transform for automated recognition of imagined words and phrases from multichannel EEG signals - p. 405 20. Index - p. 417
Non-Stationary and Nonlinear Data Processing for Automated Computer-Aided Medical Diagnosis demonstrates the applications of machine learning and deep learning combined with signal processing techniques for human-machine interface applications using EMG signals. The book includes the analysis and classification of various heart diseases based on bio-signals like electrocardiogram (ECG), photoplethysmography (PPG), and phonocardiogram (PCG) signals. Various machine learning approaches, including advanced algorithms like multivariate signal processing, time-frequency analysis, and nonlinear signal processing are covered for CAD of neural, muscular, and cardiovascular diseases. The methods for CAD of various brain disorders are also included.
Presented techniques utilize advanced non-stationary and nonlinear signal processing, along with machine learning and deep learning-based classification processes. CAD methods for diagnosing various neurological diseases are based on bio-signals such as electroencephalogram (EEG) and magnetoencephalogram (MEG), as well as medical images like magnetic resonance imaging (MRI) and computerized tomography (CT). Finally, the book addresses various types of medical signals and images, integrating nonlinear and non-stationary signal processing, machine learning, and deep learning within the CAD framework for diagnosing various diseases.
1. Chapter 1: Introduction to computer-aided medical diagnosis (CAMD) systems - p. 1 2. Chapter 2: Advanced signal processing and machine learning techniques for computer-aided medical diagnosis - p. 15 3. Chapter 3: EEG based imagined speech recognition for BCI applications - p. 41 4. Chapter 4: Study of recent trends in the detection of stress for human well-being - p. 87 5. Chapter 5: Automated visual attention analysis system using non-Euclidean feature analysis through EEG signals - p. 115 6. Chapter 6: ECG sensor-based devices for cardiac disease diagnosis - p. 133 7. Chapter 7: Automated detection of Parkinson's disease using speech signals - p. 151 8. Chapter 8: Automated emotion detection using multimodal physiological signals from wearables - p. 165 9. Chapter 9: Automated brain tumor diagnosis using MRI - p. 187 10. Chapter 10: Deep learning techniques for automated ophthalmic disease diagnosis using fundus images - p. 203 11. Chapter 11: PPG-based diagnosis system for atrial fibrillation - p. 217 12. Chapter 12: EMG signal-based devices for neuromuscular diseases - p. 231 13. Chapter 13: Wearable systems for real-time disease diagnosis and predictive analytics - p. 269 14. Chapter 14: Deep representation learning for computer-aided detection of pneumonia and tuberculosis using chest X-ray images - p. 311 15. Chapter 15: Computer-aided detection of kidney diseases using ultrasound images - p. 325 16. Chapter 16: IoT-enabled diagnosis system for telemedicine applications - p. 339 17. Chapter 17: Automated brain tumor diagnosis using MRI - p. 351 18. Chapter 18: Nonstationary signal processing techniques in EEG for rehabilitation and treatment using brain–computer interfaces - p. 369 19. Chapter 19: Fourier–Bessel domain discrete Stockwell transform for automated recognition of imagined words and phrases from multichannel EEG signals - p. 405 20. Index - p. 417
