Non-stationary and Nonlinear Data Processing for Automated Computer-Aided Medical Diagnosis

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. It 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, will be covered for CAD of neural, muscular, and cardiovascular diseases. The methods for CAD of various brain disorders will also be included. The presented techniques will 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 will be 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 will address 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.