Increased non-stationarity of heart rate during general anaesthesia with sevoflurane or desflurane in children

Yum, Myung‐Kul; Kim, J.-T.; Kim, Hyeong‐Seog

doi:10.1093/bja/aen080

Cited by 13 publications

(5 citation statements)

References 38 publications

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“…During examination of HR variability comparing general anesthesia to the awake state, the potential of using Hurst exponents for analysis has recently been demonstrated and has been shown to be more discriminating than spectral indices. 22 This experiment indicates no advantage in using a physiologically-derived, auto-correlated noise file over a computer-generated white noise file to drive the ventilator in BVV mode. We obtained the physiologicallyderived file from data from a spontaneously breathing, healthy female, sitting at rest.…”

Section: Discussionmentioning

confidence: 89%

Physiological noise versus white noise to drive a variable ventilator in a porcine model of lung injury

Buchman

et al. 2008

Can J Anesth/J Can Anesth

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This study demonstrates that the nature of the variability files, chosen to drive the variable ventilator, had no effect on indices of gas exchange or respiratory mechanics in this model. A considerable overlap of the multifractal files existed. The potential to drive a variable ventilator using algorithm-derived files with multifractal characteristics, thereby eliminating the requirement to use physiologically-derived signals, is discussed.

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Section: Discussionmentioning

confidence: 89%

Physiological noise versus white noise to drive a variable ventilator in a porcine model of lung injury

Buchman

et al. 2008

Can J Anesth/J Can Anesth

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“…11 However, unwanted activity in the HRV, such as mechanical and central effects of breathing and the influence of dominant very low-frequency activity, can cause distortion to time and frequency analyses, leading to possible misinterpretation of the data. 12,18,19 Therefore, to distinguish changes in HR that are of interest in the investigation from these sources, an advanced detrending method was used, shown not to affect HRV statistical outcomes. 18 The smoothing parameter was selected so that the HRV components of interest were not significantly affected by the detrending process.…”

Section: Assessmentmentioning

confidence: 99%

“…10 Autonomic function changes can be measured by heart rate variability (HRV). [10][11][12] HRV is a measure of the variability of the normal-to-normal QRS complexes of the PQRST waveform of the electrocardiogram (ECG), which reflects innervation of the heart by the sympathetic and parasympathetic divisions of the ANS. It provides a measure of the relative contribution and activity of these divisions based on beat-to-beat interval variations of the heart.…”

Section: Introductionmentioning

confidence: 99%

Influence of neurological lesion level on heart rate variability and fatigue in adults with spinal cord injury

et al. 2015

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Study design: Group cohort design. Objectives: To determine the influence of spinal cord injury (SCI) and neurological level on heart rate variability (HRV) and associations with fatigue. Setting: SCI rehabilitation outpatient and community settings in New South Wales, Australia. Methods: Participants included 45 adults with SCI living in the community and 44 able-bodied controls. Socio-demographic, neurological injury, psychological, HRV and eye blink variables were assessed. Multivariate analysis of variance and post hoc protected t-tests were used to determine differences in HRV and fatigue as a function of the neurological level. Pearson's correlation analysis was used to determine the relationships between these factors. Results: Participants with SCI had significantly reduced sympathetic activity. Those with tetraplegia had lowered sympathetic activity compared with those with paraplegia and able-bodied controls. Neither were differences in parasympathetic activity found between groups nor were there any significant differences found for the time domain or non-linear domains. Higher levels of fatigue were found in the SCI sample, and participants with tetraplegia had higher fatigue levels compared with those with paraplegia. Fatigued participants were more likely to have altered autonomic function--that is, reduced sympathetic activity. Conclusions: Higher levels of neurological impairment in people with SCI are more likely to result in disordered cardiovascular control involving reduced sympathetic activity, whereas elevated fatigue was found to be associated with increased sympathetic dysfunction. Findings highlight the need to address risks associated with this dysfunction, such as improved HRV and fatigue screening for people with SCI and improved education on cardiovascular risk factors.

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“…We can observe that the value of Hurst ranges from 0.0002 to 0.0018 for all the features, which is similar to the work in [43] where the scaling exponent α is close to 0 for healthy heart beat data on a very low frequency range. Also in [44], the Hurst exponent of short term ECG series is close to 0 in the low frequency range during normalcy (pre- anesthetic stage).…”

Section: Resultsmentioning

confidence: 99%

Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

et al. 2013

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BackgroundIdentifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) signals, being an activity of the autonomous nervous system (ANS), reflect the underlying true emotional state of a person. However, the performance of various methods developed so far lacks accuracy, and more robust methods need to be developed to identify the emotional pattern associated with ECG signals.MethodsEmotional ECG data was obtained from sixty participants by inducing the six basic emotional states (happiness, sadness, fear, disgust, surprise and neutral) using audio-visual stimuli. The non-linear feature ‘Hurst’ was computed using Rescaled Range Statistics (RRS) and Finite Variance Scaling (FVS) methods. New Hurst features were proposed by combining the existing RRS and FVS methods with Higher Order Statistics (HOS). The features were then classified using four classifiers – Bayesian Classifier, Regression Tree, K- nearest neighbor and Fuzzy K-nearest neighbor. Seventy percent of the features were used for training and thirty percent for testing the algorithm.ResultsAnalysis of Variance (ANOVA) conveyed that Hurst and the proposed features were statistically significant (p < 0.001). Hurst computed using RRS and FVS methods showed similar classification accuracy. The features obtained by combining FVS and HOS performed better with a maximum accuracy of 92.87% and 76.45% for classifying the six emotional states using random and subject independent validation respectively.ConclusionsThe results indicate that the combination of non-linear analysis and HOS tend to capture the finer emotional changes that can be seen in healthy ECG data. This work can be further fine tuned to develop a real time system.

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Increased non-stationarity of heart rate during general anaesthesia with sevoflurane or desflurane in children

Cited by 13 publications

References 38 publications

Physiological noise versus white noise to drive a variable ventilator in a porcine model of lung injury

Physiological noise versus white noise to drive a variable ventilator in a porcine model of lung injury

Influence of neurological lesion level on heart rate variability and fatigue in adults with spinal cord injury

Classification of emotional states from electrocardiogram signals: a non-linear approach based on hurst

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