Singular Spectrum Analysis of Biomedical Signals.pdf
This book focuses on singular spectrum analysis (SSA), a pattern recognition method, and its bivariate, multivariate, and robust variants. SSA can be applied to the detection of abnormalities in periodic biosignals, such as ECG, oxygen levels, arterial pressure, EEGs, and more. This book describes signal source separation, extraction, decomposition, and factorization in the context of biomedical signals.
Physiological, Anatomical, Functional, and Biological Processes In Vivo Metabolic and Biochemical Processes Event Related Brain Activities Movement Related Cortical Activities Muscular Potentials Heart and Long Abnormalities and Sounds Other Biological Activities and Behaviours Digital Signals and Images in Biomedicine Trends and Their Statistical Properties Linearity and Chaos Stationarity Electroencephalography and Magnetoencephalography; Characteristics and Properties Electromyography Functional Magnetic Resonance Imaging Spike Sequences in Biometrics Cyclostationarity Digital Models of Physiological Systems Mathematical Model Linear and Nonlinear systems Constrained Models Hybrid Systems Modelling, Decomposition, Detection and Reconstruction Spectral-Subtractive Methods Filtering Method and Noise Removal Techniques Statistical Model Based Technique Sub-space Approaches Blind Source Separation Tensor Factorization Singular Spectrum Analysis Univariate SSA Multivariate SSA Change Point Detection Missing Values and Data Inpainting 2-D SSA SSA of Biomedical signal Processing Subspace Decomposition and Classification SSA and Its Application to Extraction of Cyclic Signals Constrained Blind Source Separation of Single Channel Data Detection of Anomalies and Abnormalities Identification and Characterization of Trends and Evolutions Correlated Trends Prediction of Future Events SSA based Image Processing Technique Algorithm Development Functional MRI Analysis Other Applications Hybrid Systems Using SSA SSA as a Filtering Method Cascaded Adaptive Filters SSA-Local Linear Neuro-Fuzzy Model Appendix: Linear Algebra: An Overview