Linear and nonlinear analysis of heart rate variability during propofol anesthesia for short-duration procedures in children

Toweill, Daniel; Kovarik, W. D.; Carr, Richard E.; Kaplan, Danny; Lai, Susanna; Bratton, Susan L.; Goldstein, Brahm

doi:10.1097/01.pcc.0000074260.93430.6a

Cited by 26 publications

(17 citation statements)

References 46 publications

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“…Toweill et al . speculated that HF correlates with depth of anesthesia during propofol anesthesia, whereas LF allows for the assessment of the sympathetic response to pain in children with propofol anesthesia.…”

Section: Discussionmentioning

confidence: 99%

“…The authors concluded that the balance of the autonomic nervous activity might play a significant role in the determination of the HR response to endotracheal intubation. Toweill et al (12) speculated that HF correlates with depth of anesthesia during propofol anesthesia, whereas LF allows for the assessment of the sympathetic response to pain in children with propofol anesthesia.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Comparison of heart rate variability response in children undergoing elective endotracheal intubation with and without neuromuscular blockade: a randomized controlled trial

Janda

Bajorat

Kudlik

et al. 2013

Pediatric Anesthesia

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Comparison of heart rate variability response in children undergoing elective endotracheal intubation with and without neuromuscular blockade: a randomized controlled trial

Janda

Bajorat

Kudlik

et al. 2013

Pediatric Anesthesia

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“…Despite nonspecific pathophysiological mechanisms, there is ample evidence that the frequency contributions to HRV are altered in illness states, and that the degree of alteration correlates with illness severity. It is illustrative that alterations in the spectral HRV analysis related to illness severity have been demonstrated from hypovolaemia [49] to heart failure [50-52], from hypertension [53,54] to coronary artery disease [55,56], and from angina [57] to myocardial infarction [58], in addition to chronic renal failure [59], autonomic neuropathy secondary to diabetes mellitus [60], depth of anaesthesia [61] and more. Spectral analysis of HRV has been applied in the ICU.…”

Section: Frequency Domain Analysismentioning

confidence: 99%

Complex systems and the technology of variability analysis

2004

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Characteristic patterns of variation over time, namely rhythms, represent a defining feature of complex systems, one that is synonymous with life. Despite the intrinsic dynamic, interdependent and nonlinear relationships of their parts, complex biological systems exhibit robust systemic stability. Applied to critical care, it is the systemic properties of the host response to a physiological insult that manifest as health or illness and determine outcome in our patients. Variability analysis provides a novel technology with which to evaluate the overall properties of a complex system. This review highlights the means by which we scientifically measure variation, including analyses of overall variation (time domain analysis, frequency distribution, spectral power), frequency contribution (spectral analysis), scale invariant (fractal) behaviour (detrended fluctuation and power law analysis) and regularity (approximate and multiscale entropy). Each technique is presented with a definition, interpretation, clinical application, advantages, limitations and summary of its calculation. The ubiquitous association between altered variability and illness is highlighted, followed by an analysis of how variability analysis may significantly improve prognostication of severity of illness and guide therapeutic intervention in critically ill patients.

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“…A study concluded the power spectral analysis of heart rate variability may be a practical use for measuring DoA [9]. Horiguchi and Nishikawa evaluated studies of anesthesia based on monitoring the heart rate with the drug propofol [10, 11].…”

Section: Introductionmentioning

confidence: 99%

Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks

Sadrawi

Fan

Abbod

et al. 2015

BioMed Research International

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This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.

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Linear and nonlinear analysis of heart rate variability during propofol anesthesia for short-duration procedures in children

Cited by 26 publications

References 46 publications

Comparison of heart rate variability response in children undergoing elective endotracheal intubation with and without neuromuscular blockade: a randomized controlled trial

Comparison of heart rate variability response in children undergoing elective endotracheal intubation with and without neuromuscular blockade: a randomized controlled trial

Complex systems and the technology of variability analysis

Computational Depth of Anesthesia via Multiple Vital Signs Based on Artificial Neural Networks

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