Nonlinear Analysis of Physiological Signals: A Review

Faust, Oliver and Bairy, Muralidhar G (2012) Nonlinear Analysis of Physiological Signals: A Review. Journal of Mechanics in Medicine and Biology, 12 (4).

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This paper reviews various nonlinear analysis methods for physiological signals. The assessment is based on a discussion of chaos-inspired methods, such as fractal dimension (FD), correlation dimension (D2), largest Lyapunov exponet (LLE), Renyi's entropy (REN), Shannon spectral entropy (SEN), and approximate entropy (ApEn). We document that these methods are used to extract discriminative features from electroencephalograph (EEG) and heart rate variability (HRV) signals by reviewing the relevant scienti¯c literature. EEG features can be used to support the diagnosis of epilepsy and HRV features can be used to support the diagnosis of cardiovascular diseases as well as diabetes. Documenting the widespread use of these and other nonlinear methods supports our thesis that the study of feature extraction methods, based on the chaos theory, is an important subject which has been gaining more and signi¯cance in biomedical engineering. We adopt the position that pursuing research in the ¯eld of biomedical engineering is ultimately a progmatic activity, where it is necessary to engage in features that work. In this case, the nonlinear features are working well, even if we do not have conclusive evidence that the underlying physiological phenomena are indeed chaotic.

Item Type: Article
Uncontrolled Keywords: cardiovascular disease; diabetes; heart rate variability; electroencephalogram;fractal dimension; correlation dimension; Lyapunov exponent; Renyi entropy; Kolmogorov Sinai entropy; Shannon spectral entropy; approximate entropy.
Subjects: Engineering > MIT Manipal > Biomedical
Depositing User: MIT Library
Date Deposited: 01 Apr 2013 09:42
Last Modified: 01 Apr 2013 09:42

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