Biological signals classification and analysis (Q2516983)

The book offers a comprehensive yet well focused exposition of the classification and analysis of biological signals. Those signals, being generated by humans, create a genuine challenge given their inherent nonlinearity and/or chaotic character, non-stationarity, and the existence of various sources of noises. These challenges are well emphasized and reflected in the content and an overall organization of the volume. The book, divided into six chapters, is well structured and the exposition is clear and carefully thought-out. Three main parts can be distinguished, each of them coming with a well-defined objective. The first part, composed of two chapters, focuses on the background material about signals and linear and nonlinear models, including topics such as deterministic and stochastic signals. Particular attention is paid to the description of properties and operations on signals (sampling, quantization, stationarity, spectrum, response analysis). Biological signals, presented in Chapter 3, concern three categories of signals, namely, ECG, EEG, and EMG. These signals are discussed in depth and their underlying origin and main characteristics are well covered. Chapters 4 and 5 are devoted to selected methods of signal processing of biological signals. Chapter 4 focuses on the description of signals (viz., independence, Gaussian characterization, orthogonality). Several ways of computing distance between probability density functions (Kolgomogorov-Smirnov, Hellinger, etc.) along with the detection and estimation methods are presented as well. Chapter 5 is about decomposition methods and covers principle component analysis and wavelet decomposition. Chapter 6 delivers some conclusions and a list of references. The carefully selected illustrative material, a great deal of figures, solved examples, and the clear interpretations of results offer a tangible didactic value of the book. While the exposition of the material is coherent, it could have been desirable to have a stronger and more convincing linkage between biological signals and their processing. The book has all necessary components included in its first and the third part. The wealth and processing of biological signals discussed at length in the second part of the book could have been eventually better exposed when applying different techniques elaborated on in the two remaining parts of the book. The list of references is very limited, comprising only 24 entries. In general, the book could attract attention of a broad audience. It shall be useful to students in various courses on biological signal analysis. For practitioners, the volume may also serve as a handy compendium of updated material on biological signal processing.