Nonlinear analysis of heart rate variability within independent frequency components during the sleep–wake cycle

Vigo, Daniel Eduardo; Domı́nguez, J.; Guinjoan, Salvador M.; Scaramal, Mariano; Ruffa, Eduardo; Solernó, Juan I.; Siri, Leonardo C. Nicola; Cardinali, Daniel P.

doi:10.1016/j.autneu.2009.10.007

Cited by 46 publications

(38 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These results were in agreement with those in previous reports, which adopted kinds of nonlinear analysis methods, such as SampEn [16] and DFA [27] [28] . The changing tendency of the heart rate complexity reflect the corresponding cardiovascular events during sleep.…”

Section: Discussionsupporting

confidence: 95%

“…But the study of Daniel et.al. [16] showed that SampEn during NREM is the largest for men aging from 21-32 years old, while SampEn during REM and wake don't reveal significant differences. It's unable to determine whether the age or the gender, or the method has led to this inconsistent result.…”

Section: Introductionmentioning

confidence: 94%

“…However, with the method of Approximate Entropy (ApEn) and Sample Entropy (SampEn), only 100~5000 data can induce stable results. They are favorable with the analysis of short term data series [10], and have broad application in the investigation of HRV and the activity of cardiovascular system [10][11][12][13][14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiscale entropy based analysis of HRV during sleep

Xiao

Yan

Yang

et al. 2012

2012 5th International Conference on BioMedical Engineering and Informatics

View full text Add to dashboard Cite

The potential application of multiscale entropy (MSE) to analyze the heart rate variability (HRV) during different sleep stages, i.e. wake/Rapid Eye Movement (REM) /light sleep/deep sleep, was investigated. The RR sequences of 5 min from four sleep stages were analyzed by the measurements of MSE under three scales (MSE1, MSE2 and MSE3). The results demonstrated that the complexity of heart rate (HR) in non-REM(NREM) is significantly higher (p<0.001) than that in wake and REM, and the complexity becomes higher with sleep deepening (p<0.01). MSE3 corresponding to r=0.25*STD reveal the most powerful entropy measurement for classifying different sleep stages.

show abstract

Section: Discussionsupporting

confidence: 95%

Section: Introductionmentioning

confidence: 94%

See 1 more Smart Citation

Multiscale entropy based analysis of HRV during sleep

Xiao

Yan

Yang

et al. 2012

2012 5th International Conference on BioMedical Engineering and Informatics

View full text Add to dashboard Cite

show abstract

“…With exception of one recording at POST with only six valid segments, each recording had at least 19 (79%) valid bins. The failed recording was prematurely interrupted during the night; it was included for the rest activity analyses, since it could still provide useful information about sleep-wake differences of HRV ( 24 ), but excluded for the circadian analyses because the six recorded bins were consecutive and therefore a cosine curve could not be fi tted.…”

Section: Resultsmentioning

confidence: 99%

Circadian Rhythm of Autonomic Cardiovascular Control During Mars500 Simulated Mission to Mars

Vigo

Tuerlinckx²,

Ogrinz³

et al. 2013

Aviation, Space, and Environmental Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

“…The decomposing independent frequency components of HRV (HF and LF components) have been studying to demonstrate sympathetic and parasympathetic activity [35][36][37]. However, they neglected a complex interaction between the independent frequency components of HRV.…”

Section: Analysis Of Interaction Featuresmentioning

confidence: 99%

Use of Mutual Information and Transfer Entropy to Assess Interaction between Parasympathetic and Sympathetic Activities of Nervous System from HRV

Zheng

Pan

et al. 2017

Entropy

View full text Add to dashboard Cite

Abstract:Obstructive sleep apnea (OSA) is a common sleep disorder that often associates with reduced heart rate variability (HRV) indicating autonomic dysfunction. HRV is mainly composed of high frequency components attributed to parasympathetic activity and low frequency components attributed to sympathetic activity. Although, time domain and frequency domain features of HRV have been used to sleep studies, the complex interaction between nonlinear independent frequency components with OSA is less known. This study included 30 electrocardiogram recordings (20 OSA patient recording and 10 healthy subjects) with apnea or normal label in 1-min segment. All segments were divided into three groups: N-N group (normal segments of normal subjects), P-N group (normal segments of OSA subjects) and P-OSA group (apnea segments of OSA subjects). Frequency domain indices and interaction indices were extracted from segmented RR intervals. Frequency domain indices included nuLF, nuHF, and LF/HF ratio; interaction indices included mutual information (MI) and transfer entropy (TE (H→L) and TE (L→H)). Our results demonstrated that LF/HF ratio was significant higher in P-OSA group than N-N group and P-N group. MI was significantly larger in P-OSA group than P-N group. TE (H→L) and TE (L→H) showed a significant decrease in P-OSA group, compared to P-N group and N-N group. TE (H→L) were significantly negative correlation with LF/HF ratio in P-N group (r = −0.789, p = 0.000) and P-OSA group (r = −0.661, p = 0.002). Our results indicated that MI and TE is powerful tools to evaluate sympathovagal modulation in OSA. Moreover, sympathovagal modulation is more imbalance in OSA patients while suffering from apnea event compared to free event.

show abstract

Nonlinear analysis of heart rate variability within independent frequency components during the sleep–wake cycle

Cited by 46 publications

References 31 publications

Multiscale entropy based analysis of HRV during sleep

Multiscale entropy based analysis of HRV during sleep

Circadian Rhythm of Autonomic Cardiovascular Control During Mars500 Simulated Mission to Mars

Use of Mutual Information and Transfer Entropy to Assess Interaction between Parasympathetic and Sympathetic Activities of Nervous System from HRV

Contact Info

Product

Resources

About