Recurrence plot of heart rate variability signal in patients with vasovagal syncopes

Schlenker, Jakub; Socha, Vladimír; Riedlbauchová, Lucie; Nedělka, Tomáš; Schlenker, Anna; Potočková, Veronika; Mala, Sarka; Kutílek, Patrik

doi:10.1016/j.bspc.2015.10.007

Cited by 36 publications

(15 citation statements)

References 37 publications

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“…In healthy subjects, the cardiovascular system reacts inmediately to system stimuli, thus decreasing the time the organism stays in the same or similar state. However, under pathological circumstances, the control system simplifies and the recurrences to similar states increase [ 52 ]. This suggests a loss of complexity and more regularity in the system in the presence of apnea.…”

Section: Discussionmentioning

confidence: 99%

“…In general, we can find the same behavior in the remaining features: higher values for apnea minutes. This fact implies lower HRV, simplification of heart rate dynamics and greater predictability and, ultimately, pathological conditions [ 41 , 52 ].…”

Section: Discussionmentioning

confidence: 99%

“…It quantifies the percentage of recurrent points in an RP and represents the average number of neighbours each element of the phase space has in its neighbourhood. Higher recurrence means lower system variability, since REC represents the probability that a certain state recurs [ 52 ]. where N is the dimension of the recurrence matrix.…”

Section: Methodsmentioning

confidence: 99%

“…The maximal length of vertical lines (Vmax) gives information about the duration of the laminar states [ 45 ]. LAM is more robust against noise than TT and Vmax [ 52 ].…”

Section: Methodsmentioning

confidence: 99%

“…However, they also suggested that further studies would be necessary to compare results in other physiological conditions, such as illness. In the last 15 years, many authors have introduced RQA, among others, for the analysis of HRV in ventricular tachycardia [ 33 ], ventricular arrhythmias [ 32 ], sleep apnea [ 11 – 13 , 34 ], diabetes mellitus [ 41 ], paroxysmal atrial fibrillation [ 36 ], preeclampsia [ 51 ], and vasovagal syncopes [ 52 ]. So RPs and RQA can be considered a classical tool to analyse the cardiac system.…”

Section: Introductionmentioning

confidence: 99%

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Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold

et al. 2018

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Our contribution focuses on the characterization of sleep apnea from a cardiac rate point of view, using Recurrence Quantification Analysis (RQA), based on a Heart Rate Variability (HRV) feature selection process. Three parameters are crucial in RQA: those related to the embedding process (dimension and delay) and the threshold distance. There are no overall accepted parameters for the study of HRV using RQA in sleep apnea. We focus on finding an overall acceptable combination, sweeping a range of values for each of them simultaneously. Together with the commonly used RQA measures, we include features related to recurrence times, and features originating in the complex network theory. To the best of our knowledge, no author has used them all for sleep apnea previously. The best performing feature subset is entered into a Linear Discriminant classifier. The best results in the “Apnea-ECG Physionet database” and the “HuGCDN2014 database” are, according to the area under the receiver operating characteristic curve, 0.93 (Accuracy: 86.33%) and 0.86 (Accuracy: 84.18%), respectively. Our system outperforms, using a relatively small set of features, previously existing studies in the context of sleep apnea. We conclude that working with dimensions around 7–8 and delays about 4–5, and using for the threshold distance the Fixed Amount of Nearest Neighbours (FAN) method with 5% of neighbours, yield the best results. Therefore, we would recommend these reference values for future work when applying RQA to the analysis of HRV in sleep apnea. We also conclude that, together with the commonly used vertical and diagonal RQA measures, there are newly used features that contribute valuable information for apnea minutes discrimination. Therefore, they are especially interesting for characterization purposes. Using two different databases supports that the conclusions reached are potentially generalizable, and are not limited by database variability.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…The maximal length of vertical lines (Vmax) gives information about the duration of the laminar states [ 45 ]. LAM is more robust against noise than TT and Vmax [ 52 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold

et al. 2018

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Dynamic analysis of meteorological time series in Hong Kong: A nonlinear perspective

Yan¹,

Chan

et al. 2021

Intl Journal of Climatology

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Many meteorological systems are chaotic in nature, which inevitably limits its predictability. Accurate prediction of meteorological variables depends primarily on properly diagnosing the complex underlying dynamics. Nonlinear dynamic analysis has shown to be particularly useful for such purpose. In this study, the concept of recurrence analysis was extended to investigate and characterize the underlying dynamics of meteorological time series (i.e., wind speed, temperature, pressure and relative humidity) based on daily observation in Hong Kong during a period from 1998 to 2018. The existence of chaos was clearly identified based on the phase space reconstruction diagram and the recurrence plot. It was shown that the underlying dynamics associated with wind speed appears to indicate higher level of complexity as compared to those of pressure and temperature, which, in consequence, may lead to more irregular time‐dependent behaviour and lower predictability. Moreover, season‐to‐seasonal variability in the dynamics of meteorological time series was evident, particularly for wind speed and temperature. Overall, this study shows that the recurrence analysis can be well applied as a useful diagnostic tool to investigate the dynamics of meteorological systems, which is expected to provide a new avenue regarding the modelling and prediction of the behaviour of meteorological parameters.

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Recurrence Analysis of EEG Power and HRV Time Series for Asynchronous BCI Control

et al. 2019

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Recurrence plot of heart rate variability signal in patients with vasovagal syncopes

Cited by 36 publications

References 37 publications

Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold

Improving the understanding of sleep apnea characterization using Recurrence Quantification Analysis by defining overall acceptable values for the dimensionality of the system, the delay, and the distance threshold

Dynamic analysis of meteorological time series in Hong Kong: A nonlinear perspective

Recurrence Analysis of EEG Power and HRV Time Series for Asynchronous BCI Control

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