Special Issue "Information Theory Applied to Physiological Signals"
Deadline for manuscript submissions: 30 September 2017
Information theory is a well-known methodology, traditionally used in communication engineering, and has relatively recently been extended and applied to a variety of emerging areas, including bioengineering. Conceptually, physiological systems can be considered as communication channels of a special kind, which admit the information content can be analysed; this concept has been particularly successful in the analysis of neural systems. Information theory has also been traditionally applied to systems which require well-defined metrics for quantifying their dynamic behaviours, or for quantifying their degrees of nonlinearity and complexity.
Signal analyses, based on information theory, have typically taken the form of entropy, probability, and divergence analyses. In this Special Issue, we consider the most widely analysed physiological signals, such as electrocardiograms (ECG), electroencephalograms (EEG), electromyograms (EMG), electrooculograms (EOG), and respiratory signals. The application of information theory principles to physiological signals has undoubtedly shed light on the intrinsic dynamics and mechanisms underlying many physiological systems, consequently elucidating interactions that would not have been possible using temporal or spectral analyses alone.
With the understanding of the mechanisms governing many physiological systems still remaining a challenge, information theory based analyses are likely to continue to substantially aid in the comprehensive understanding of the physiology and signal generating mechanism. Another challenge is to develop information theoretic measures for real-world physiological data which are notoriously noisy, with drifting baselines, and which do not obey any synthetic probability distribution.
The main goal of this Special Issue is, therefore, to disseminate new and original research based on information theory analyses of physiological signals, in order to assist in both the understanding of physiological phenomena, diagnosis and treatment, and for planning healthcare strategies to prevent the occurrences of certain pathologies. Furthermore, manuscripts summarizing the most recent state-of-the-art of this topic will also be welcome.
Prof. Dr. Danilo P. Mandic
Prof. Dr. Andrzej Cichocki
Prof. Dr. Chung-Kang Peng
Manuscript Submission Information
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- Joint analysis of physiological signals at multiple temporal, frequency, and spatial scales (ECG, EEG, EMG EOG, etc.)
- Multiscale entropy, complexity loss theory for the monitoring and management of diseases
- Entropy or information content for data fusion from recordings of different natures
- Computationally efficient entropy measures for physiological data
- Kullback-Leibler divergence, other divergences applied to physiological monitoring
- Extensions to symbolic dynamics and coding in biological systems
- Practical considerations: entropic scales and embedding dimensions, sample size, signal modality characterization for health
- Levels of consciousness, fatigue, fitness for duty
- Heart rate variability (HRV) analysis, co-morbidity between HRV and other physiological responses
- Psychophysiological signals (physical/mental/emotional analysis), especially in newborns and the elderly
- Complexity loss theory in dementia, epilepsy, posture, and sleep disorders
- Other clinical applications of multiscale entropy