Investigation of the relationship between anxiety and heart rate variability in fibromyalgia: A new quantitative approach to evaluate anxiety level in fibromyalgia syndrome

Bilgin, Süleyman; Arslan, Evren; Elmas, Onur; Yıldız, Sedat; Çolak, Ömer Halil; Bilgin, Gürkan; Koyuncuoğlu, Hasan Rıfat; Akkuş, Selami; Çömlekçi, Selçuk; Köklükaya, Etem

doi:10.1016/j.compbiomed.2015.10.003

Cited by 15 publications

(20 citation statements)

References 20 publications

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“…Similar data were also obtained by other researchers. In particular, one study showed that the low sensitivity and specificity of the stress index as a prognostic index of mortality allows using it as an auxiliary means only; the same applies to high‐frequency heart rate variability parameters—they can potentially be used as an auxiliary diagnostic method in combination with other methods …”

Section: Discussionmentioning

confidence: 99%

“…In particular, the fact that it is impossible to rule out the effect of external adverse factors on the sample, in spite of the said sample being arbitrarily defined as healthy. Here, it is worth mentioning the considerable spread of instant values of time domain heart rate variability indices, which were noted in literature, related to the emotional state, changes in the endocrine profile, and the effect of anxiety on heart rate variability indices . Despite the fact that primary processing of experimental data arrays included the elimination of postthreshold values, it is impossible to assess the contribution of momentary impacts for each individual test subject.…”

Section: Discussionmentioning

confidence: 99%

“…The analysis of recent studies showed that among conventional methods of heart rate variability analysis, time domain methods are preferred less, while frequency domain methods are preferred more, as is their use in combination, which is caused by the rapid development of computer technologies that deal with digital processing of signals, including spectral analysis methods, such as multifractal and wavelet transform analysis …”

Section: Introductionmentioning

confidence: 99%

“…One could note the diversity of methods that researchers use to mathematically process and analyze experimental data on heart rate variability indices. In addition to conventional statistical methods, they also use entropy analysis and transitional processes in nonlinear systems, as well as currently popular neural network algorithms …”

Section: Introductionmentioning

confidence: 99%

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Predictors of aging based on the analysis of heart rate variability

Botsva

Naishtetik

Khimion

et al. 2017

Pacing Clinical Electrophis

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The use of neural network computations and the modification of the algorithm through the construction of individual training samples for different age intervals, and the creation of individual ensembles of classification neural networks, therefore achieved a prediction of the age of examined persons based on the values of their time and frequency domain heart rate variability indices, with 87% accuracy for women and 85% accuracy for men in the 66-85 years age interval and at least 85% for age groups across the entire sample.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predictors of aging based on the analysis of heart rate variability

Botsva

Naishtetik

Khimion

et al. 2017

Pacing Clinical Electrophis

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“…It affects mainly muscles and the parts where the muscles are attached to bones. It is a disease which is more common in women [4] [5]. As it happens in every illness, in order to begin the right and fast treatment for FMS, quick diagnosis is very important [3] [6].…”

Section: Introductionmentioning

confidence: 99%

Determination of New Bio Signal and Tests Alternative to Verbal Pain Scale for Diagnosing Fibromyalgia Syndrome

Uçar

Bozkurt²,

Bozkurt³

2016

IJIGSP

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Abstract-In this study; values obtained through the analysis of blood samples, taken under laboratory conditions, from patients diagnosed with fibromyalgia syndrome and healthy subjects and the sympathetic skin response parameters were used. With the aim of classifying verbal pain scale, which is one of the psychological test scores used for fibromyalgia syndrome diagnosis; relation between the sympathetic skin response effect on other test data and the verbal pain scale were reviewed by using different conditions of available data. Within this framework, three different algorithms were used for classification with high accuracy rates. These algorithms are: Multi-Layer Feed-Forward Neural Networks, Probabilistic Neural Network and Radial Basis Function Neural Network. For Multi-Layer Feed-Forward Neural Networks classification algorithm, classification was done with three different training algorithms, Levenberg-Marquardt back propagation, Resilient back propagation and the Scaled conjugate gradient back propagation and the results were compared elaborately. Based on the results, by using all variables the following accuracy rates were obtained: 68.2% accuracy with Levenberg-Marquardt training algorithm, 77.3% accuracy with the Resilient back propagation training algorithm, and 68.18% accuracy with the Scaled conjugate gradient training algorithm. These success rates show that there is a relationship between verbal pain scale, sympathetic skin response and other test data.

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Modulation of Autonomic Function by Physical Exercise in Patients with Fibromyalgia Syndrome: A Systematic Review

Andrade

Vilarino

Serafim

et al. 2019

PM&R

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Objective To evaluate the effects of physical exercise on autonomic dysfunction in patients with fibromyalgia syndrome (FM). Literature Survey and Methodology A systematic review of experimental studies published until December 2017 that analyzed the effect of physical exercise on autonomic dysfunction in patients with FM was performed using the PubMed, Pedro, Scopus, ScienceDirect, and Web of Science databases. Synthesis A total of 1105 articles were identified, 12 of which were included in the final analysis. The most analyzed exercise modalities were aerobic and resistance exercises. Overall, the studies demonstrated that aerobic exercise performed twice a week with moderate to high intensity was effective in reducing autonomic dysfunction by increasing heart rate variability. Resistance training was associated with reduced symptoms of anxiety and depression as well as increased muscle strength; however, it did not reduce autonomic dysfunction in these patients in the short or long term. Conclusions Preliminary evidence suggests that aerobic exercise reduces autonomic dysfunction in patients with FM, whereas resistance training reduces psychological symptoms such as anxiety and depression. Level of Evidence I

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Investigation of the relationship between anxiety and heart rate variability in fibromyalgia: A new quantitative approach to evaluate anxiety level in fibromyalgia syndrome

Cited by 15 publications

References 20 publications

Predictors of aging based on the analysis of heart rate variability

Predictors of aging based on the analysis of heart rate variability

Determination of New Bio Signal and Tests Alternative to Verbal Pain Scale for Diagnosing Fibromyalgia Syndrome

Modulation of Autonomic Function by Physical Exercise in Patients with Fibromyalgia Syndrome: A Systematic Review

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