Holter registers and metabolic syndrome

Muñoz-Diosdado, Alejandro; Ramírez-Hernández, L.; Aguilar-Molina, Ana María; Zamora-Justo, José Alberto; Gutiérrez-Calleja, R. A.; Virgilio-González, C. D.

doi:10.1063/1.4901381

Cited by 3 publications

(5 citation statements)

References 8 publications

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“…Discrimination was possible thanks to the significant reduction in heart rate that individuals suffer while they are sleeping, and the contrast with the beat to beat interval of the awake individual, which demands a greater flow of blood and this is reflected in the time between R and R [10] in the electrocardiogram signal.…”

Section: Methodsmentioning

confidence: 99%

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Interbeat RR Interval Time Series Analyzed with the Sample Entropy Methodology

Muñoz-Diosdado¹,

Gálvez-Coyt²

2017

World Congress on Electrical Engineering and Computer Systems and Science

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-Computational entropies are methods of non-linear analysis that allows an estimate of the irregularity of a system. Different types of computational entropy were considered and tested in order to obtain one that would give an index of signal complexity taking into account the size of the analysed time series, the computational resources demanded by the method, and the accuracy of the calculation. An algorithm for the generation of fractal time-series with a certain value of the spectral exponent β was used for the characterization of the different entropy algorithms. We obtained a significant variation for most of the algorithms in terms of the series size, which could result counterproductive for the study of real signals of different lengths. The best method was sample entropy, which shows great independence of the series size. With this method, time series of heart interbeat RR intervals or tachograms of healthy subjects and patients with congestive heart failure were analysed. The calculation of sample entropy was carried out for 24-hour tachograms and time subseries of 6-hours for sleepiness and wakefulness. The comparison between the two populations shows a significant difference that is accentuated when the patient is sleeping.

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Section: Methodsmentioning

confidence: 99%

“…In this case, values of = 0.2 and = 3 were chosen, which proved to be suitable for this type of time series analysis, allowing a greater efficiency in the method and reducing the loss of information [10].…”

Section: Methodsmentioning

confidence: 99%

Interbeat RR Interval Time Series Analyzed with the Sample Entropy Methodology

Muñoz-Diosdado¹,

Gálvez-Coyt²

2017

World Congress on Electrical Engineering and Computer Systems and Science

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“…Detrended Fluctuation Analysis (DFA) and Higuchi's Fractal Dimension have also been successfully used to determine the state of health of a person [11]. These techniques were also considered for the prediagnosis report.…”

Section: Holter Monitor Prototype and Acquisition Softwarementioning

confidence: 99%

“…The importance of using Holter monitoring for diagnosis of diseases that may affect the heart has been studied, it was found that the number of ectopic heartbeat depends on the eating habits and physical activity performed by the individual [6]. That is why it was considered necessary to design a Holter monitor and a software that allows to obtain the results of the nonlinear dynamics analysis of heartbeat interval series.…”

Section: Introductionmentioning

confidence: 99%

“…There have been several studies [1 -5] that involve the analysis of heart rate variability by using nonlinear dynamics methods; particularly, multifractal analysis has been used to study time series obtain from physiological variables [3,4]. From multifractal spectrum (MSp) the multifractality degree can be obtained, it is defined as the multifractal spectrum width and can be interpreted as a measure of the system complexity, it provides how nonlinear and non-stationary the time series is.…”

Section: Introductionmentioning

confidence: 99%

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