Effect of extreme data loss on heart rate signals quantified by entropy analysis

Yu, Liang; Wang, Jun; Li, Jin; Da-Zhao, Liu

doi:10.1016/j.physa.2014.06.074

Cited by 3 publications

(1 citation statement)

References 24 publications

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“…One of the most important examples of these signals is the heart rate signals. Therefore, heart rate signals have been the focus of many research studies for a long time (Patidar et al 2015;Li et al 2015;Alvarez-Estevez and Moret-Bonillo 2016). Actually, there is need for the present increased interest in heart rate (HR) analysis since a great amount of information is embedded in the time series of heart rate signals; for example, these information were found to be directly linked with the heart health and can be used to diagnose some cognitive and central nervous system pathologies causing cardiac diseases (Costa-Neto et al 2008;Yun et al 2015;Hascoët et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Analysis of heart rate signals during meditation using visibility graph complexity

2018

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In the dynamics analysis of heart rate, the complexity of visibility graphs (VGs) is seen as a sign of short term variability in signals. The present study was conducted to investigate the possible impact of meditation on heart rate signals complexity using VG method. In this study, existing heart rate signals in Physionet database were used. The dynamics of the signals were then studied both before and during meditation by examining the complexity of VGs using graph index complexity (GIC). Generally, the obtained results showed that the heart rate signals were more complex during meditation. The simple process of calculating the GIC of VG and its adaptability to the chaotic nature of the biological signals can help in estimating the heart rate complexity in meditation.

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