The detrended fluctuation analysis of heartbeat intervals in time series during stress tests

Salcedo-Martínez, Amparo; Pérez-López, Nancy Gabriela; Zamora-Justo, José Alberto; Gálvez-Coyt, G.; Muñoz-Diosdado, Alejandro

doi:10.1063/1.5095928

Cited by 2 publications

(4 citation statements)

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“…Once we had analyzed the selected visibility graph parameters for white, pink, and Brownian noises and we had verified the differences, we decided to applied these calculations in heartbeat time series, since it has been reported that series of young and healthy subjects present long-term correlations [ 1 ], such as in the pink noise. However, some conditions (age or illness) modify this behavior, causing decorrelations or short-term correlations in series, such as in the white and Brownian noises, respectively [ 60 , 61 , 62 , 90 ].…”

Section: Discussionmentioning

confidence: 99%

“…Several studies [ 1 , 2 , 4 , 8 , 12 , 13 , 51 , 52 , 53 ] have showed the correlation between the results of nonlinear dynamic techniques applied to heartbeat time interval series (or tachograms) and the health status or age of a subject. When we observed the graphical representation of the networks obtained ( Figure 2 a, Figure 3 a, Figure 4 a and Figure 8 ), noticeable differences could be observed with the naked eye.…”

Section: Discussionmentioning

confidence: 99%

“…Nowadays, the scientific community is studying new tools for physiological signal analysis that can be successfully used for the classification of different groups by computational algorithms [ 1 , 2 , 3 , 4 ]. Especially, the analyses of electrocardiograms (ECG) by different methods have shown potential applications for the prediagnosis of several diseases [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Changes in HRV induced by low or intense physical activity have been widely studied [ 1 , 13 , 15 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 ]; when the subject is exercising, cardiovascular disorders can be detected that are not observed when the subject is inactive [ 70 ]. Therefore, by introducing extra work for the heart while exercising and monitoring the subject’s heart activity with an electrocardiograph, it may be possible to discern heart problems that are not seen when the subject is at rest [ 68 ].…”

Section: Introductionmentioning

confidence: 99%

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Visibility Graph Analysis of Heartbeat Time Series: Comparison of Young vs. Old, Healthy vs. Diseased, Rest vs. Exercise, and Sedentary vs. Active

Muñoz-Diosdado

Solís-Montufar

Zamora-Justo

2023

Entropy

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Using the visibility graph algorithm (VGA), a complex network can be associated with a time series, such that the properties of the time series can be obtained by studying those of the network. Any value of the time series becomes a node of the network, and the number of other nodes that it is connected to can be quantified. The degree of connectivity of a node is positively correlated with its magnitude. The slope of the regression line is denoted by k-M, and, in this work, this parameter was calculated for the cardiac interbeat time series of different contrasting groups, namely: young vs. elderly; healthy subjects vs. patients with congestive heart failure (CHF); young subjects and adults at rest vs. exercising young subjects and adults; and, finally, sedentary young subjects and adults vs. active young subjects and adults. In addition, other network parameters, including the average degree and the average path length, of these time series networks were also analyzed. Significant differences were observed in the k-M parameter, average degree, and average path length for all analyzed groups. This methodology based on the analysis of the three mentioned parameters of complex networks has the advantage that such parameters are very easy to calculate, and it is useful to classify heartbeat time series of subjects with CHF vs. healthy subjects, and also for young vs. elderly subjects and sedentary vs. active subjects.

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