Nonlinear Modelling of Physiological Signals
A special issue of Signals (ISSN 2624-6120).
Deadline for manuscript submissions: closed (30 December 2020) | Viewed by 286
Special Issue Editors
2. School of Engineering, Monash University, Subang Jaya, Selangor, Malaysia
Interests: applied mathematics; fractal neural engineering; signal and pattern analysis
Interests: thermal-fluid sciences; thermal and molecular physics; heat and mass transfer (including biological systems); ultra-fast heat transfer; energy storage (phase-change materials); power engineering; signal processing (including biological signals); equations of mathematical physics (PDEs)
Interests: nonlinear dynamics; engineering, applied and computational mathematics; mathematical modelling; nonlinear analysis; fluid dynamics; medical physics; computational fluid dynamics; numerical Simulation; mathematical physics; analysis
Special Issue Information
Quantifying of physiological signals is a necessary approach in biomedical research. Due to the nonlinear and complex structure of physiological signals (such as Electroencephalography (EEG) and Electromyography (EMG) signals), employing nonlinear analysis techniques has advantages over using linear techniques. Besides the analysis of the variations of physiological signals in different conditions, the modelling of these variations has great advantages, specially in case of prediction of complex structure of these signals.
For this purpose, researchers employ different techniques such as fractal theory. In fact, prediction of structure of signals enables us to relate the future of signal to its history. This job specially has great advantage when we talk about the prediction of future of different diseases based on the current and past stages of the diseases.
Therefore, this special issue focuses on the recent advances in the modelling of physiological signals using different mathematical and computational techniques.
Dr. Hamidreza Namazi
Prof. Vladimir Kulish
Prof. Walter Legnani
Guest Editors
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Keywords
- Physiological signal modelling
- Complexity analysis
- Nonlinear systems
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