Heart Rate Variability: Algorithms and Software Tools
A special issue of Mathematical and Computational Applications (ISSN 2297-8747).
Deadline for manuscript submissions: closed (31 May 2020) | Viewed by 3372
Special Issue Editor
Interests: biomedical signal processing; ECG; heart rate variability
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Heart rate variability (HRV) can be defined as variations in time between contiguous heart beats. These variations are not constant, and they are regulated by factors extrinsic to the circulatory system, particularly by the nervous system. Thus, the analysis of the HRV allows evaluating, in both an indirect and noninvasive way, the state of the nervous system, and its sympathetic and parasympathetic components.
In the last few years, new analysis methodologies have been defined, new experiments to explain its physiological fundamentals have been developed, and clinical studies in various fields have been performed.
However, this is not a closed research field, and there are still problems that need to be solved: Nonstationary records, determining optimal measurement conditions, obtaining robust indexes, determination of normality, interoperability between measurement devices and applications, etc.
In this SI, we would like to receive contributions describing algorithms capable of improving heart rate signal recording and processing and obtaining robust HRV indexes, software tools that facilitate the work of scientists in the field, and advanced methodologies for clinical diagnosis. Potential topics include (but are not limited to):
- Artifact removal and filtering;
- Time domain robust indexes;
- Spectral estimation;
- Nonlinear indexes of heart rate variability;
- Machine learning for clinical diagnosis;
- Software tools for HRV analysis.
Assoc. Prof. Dr. Xosé Antón Vila Sobrino
Guest Editor
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Keywords
- Heart rate variability analysis
- Spectral estimation of nonstationary signals
- Nonlinear dynamic of heart rate
- Robust heart rate variability indexes
- Machine learning for heart rate variability based clinical diagnosis
- Software tools for heart rate variability analysis
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