Global recurrence quantification analysis and its application in financial time series

He, Jiayi; Shang, Pengjian; Zhang, Yali

doi:10.1007/s11071-020-05543-4

Cited by 11 publications

(1 citation statement)

References 34 publications

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“…Recurrence Quantifi cation Analysis (RQA) is useful in analyzing dynamic systems from a time series. RPs were introduced to visually distinguish different dynamical behaviors in time series since periodic orbits and chaotic and random behaviors generate distinct structures in the RPs (48). Measures based on diagonal structures can fi nd chaos-order transitions, and measures based on vertical or horizontal structures can fi nd chaos-chaos transitions.…”

Section: (5)mentioning

confidence: 99%

Mathematical model presenting to assess variations in heart rate of ‎different age groups

JAHANI¹,

MORIDANI²,

ANISI³

2023

BLL

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OBJECTIVE: Recently, older people's cardiovascular systems have been affected by aging-related changes. An electrocardiogram (ECG) provides information about cardiac health. Analyzing ECG signals can help doctors and researchers diagnose many deaths. Besides direct ECG analysis, some measurements can be extracted from the ECG signals, and one of the most important measurements is heart rate variability (HRV). Research and clinical domains can benefi t from HRV measurement and analysis as a potential noninvasive tool for assessing autonomic nervous system activity. The HRV describes the variation between an ECG signal's RR intervals over time and the change in that interval over time. An individual's heart rate (HR) is a non-stationary signal, and its variation can indicate a medical condition or impending cardiac disease. Many factors, such as stress, gender, disease, and age, infl uence HRV. METHODS: The data for this study is taken from a standard database, the Fantasia Database, which contains 40 subjects, including two groups of 20 young subjects (21-34 years old) and 20 older subjects (68-85 years old). We used two non-linear methods, Poincare and Recurrence Quantifi cation Analysis (RQA), to determine how different age groups affect HRV using Matlab and Kubios software. RESULTS: By analyzing some features extracted from this non-linear method based on a mathematical model and making a comparison, the results indicate that the SD1, SD2, SD1/SD2, and area of an ellipse (S) in Poincare will be lower in old people than in young people, but %REC, %DET, Lmean and Lmax will recur more often in older people than in younger ones. Poincare Plot and RQA show opposite correlations with aging. In addition, Poincaré's plot showed that young people have a greater range of changes than the elderly. CONCLUSION: According to the result of this study, heart rate changes can be reduced by aging, and ignoring this issue could lead to cardiovascular disease in the future (Tab.

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Section: (5)mentioning

confidence: 99%