Assessing Complexity in Physiological Systems through Biomedical Signals Analysis

Assessing physiological complexity.

Physiologists both admire and fear complexity, but we have made relatively few attempts to understand it. Inherently complex systems are more difficult to study and less predictable. However, a deeper understanding of physiological systems can be achieved by modifying experimental design and analysis to account for complexity. We begin this essay with a tour of some mathematical views of comple...

Analysis of independent components in biomedical signals

Fractal characterization of complexity in temporal physiological signals.

This review first gives an overview on the concept of fractal geometry with definitions and explanations of the most fundamental properties of fractal structures and processes like self-similarity, power law scaling relationship, scale invariance, scaling range and fractal dimensions. Having laid down the grounds of the basics in terminology and mathematical formalism, the authors systematicall...

Independent component analysis for biomedical signals.

Independent component analysis (ICA) is increasing in popularity in the field of biomedical signal processing. It is generally used when it is required to separate measured multi-channel biomedical signals into their constituent underlying components. The use of ICA has been facilitated in part by the free availability of toolboxes that implement popular flavours of the techniques. Fundamentall...

Complexity analysis of electrocardiographic signals.

Two types of electrocardiographic data series were investigated using appropriate tests based on a selection of semi-quantitative analysis algorithms. Distribution histograms, power spectra, auto-correlation functions, state-space portraits, Lyapunov exponents and wavelet transformations were applied to electrocardiograms of normal and stressed subjects. Statistical analysis using the Student's...