Analysis of depth of anesthesia with Hilbert–Huang spectral entropy

Li, Xiaoli; Duan, Li; Liang, Zhenhu; Voss, Logan J.; Sleigh, Jamie

doi:10.1016/j.clinph.2008.08.006

Cited by 104 publications

(80 citation statements)

References 36 publications

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“…Thereby, the SampEn and ApEn of denoised EEG have been compared to the commercial indexes firstly, and then the statistical analysis and comparison with diagnosis results of anesthetists will be the next step in our study. Moreover, other complexity measures of EEG signals would also be used in further analysis and improvement, like the permutation entropy [23], Hilbert-Huang spectral entropy [24], and so on.…”

Section: Discussionmentioning

confidence: 99%

Analysis of EEG via Multivariate Empirical Mode Decomposition for Depth of Anesthesia Based on Sample Entropy

Wei

Liu

Fan

et al. 2013

Entropy

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Abstract:In monitoring the depth of anesthesia (DOA), the electroencephalography (EEG) signals of patients have been utilized during surgeries to diagnose their level of consciousness. Different entropy methods were applied to analyze the EEG signal and measure its complexity, such as spectral entropy, approximate entropy (ApEn) and sample entropy (SampEn). However, as a weak physiological signal, EEG is easily subject to interference from external sources such as the electric power, electric knives and other electrophysiological signal sources, which lead to a reduction in the accuracy of DOA determination. In this study, we adopt the multivariate empirical mode decomposition (MEMD) to decompose and reconstruct the EEG recorded from clinical surgeries according OPEN ACCESSEntropy 2013, 15 3459 to its best performance among the empirical mode decomposition (EMD), the ensemble EMD (EEMD), and the complementary EEMD (CEEMD) and the MEMD. Moreover, according to the comparison between SampEn and ApEn in measuring DOA, the SampEn is a practical and efficient method to monitor the DOA during surgeries at real time.

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Section: Discussionmentioning

confidence: 99%

Analysis of EEG via Multivariate Empirical Mode Decomposition for Depth of Anesthesia Based on Sample Entropy

Wei

Liu

Fan

et al. 2013

Entropy

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“…The SE analysis was performed for trials of baseline EEG and trials of stimulus-response EEG. To reduce noise, each EEG trial 10 was obtained by time-averaging 20 non-overlapping subsegments of 1 s duration which were filtered to within the standard SSVEP frequency range of interest (13)(14)(15)(16)(17)(18)(19)(20). The recording sampling frequency was 1200 Hz.…”

Section: (B) Intrinsic Measures Of Respiratory Sinus Arrhythmiamentioning

confidence: 99%

“…Intrinsic intra-component measures characterize the scale-wise system behaviour within each component; typical examples include instantaneous frequency and instantaneous amplitude. Statistical significance tests, based on the dyadic filterbank property of EMD, have been developed to identify information-bearing scales [8], while Li et al [18] calculated spectral entropy using the MHHS and found that it was better suited to tracking responses to anaesthesia in EEG than other spectral entropy measures. Sample entropy (SE) [29] and its multi-scale extension, multi-scale sample entropy (MSE) [30,31], are widely used tools for assessing complex nonlinear couplings within time series.…”

Section: Introductionmentioning

confidence: 99%

Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework

2015

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A novel multi-scale approach for quantifying both inter-and intra-component dependence of a complex system is introduced. This is achieved using empirical mode decomposition (EMD), which, unlike conventional scale-estimation methods, obtains a set of scales reflecting the underlying oscillations at the intrinsic scale level. This enables the datadriven operation of several standard data-association measures (intrinsic correlation, intrinsic sample entropy (SE), intrinsic phase synchrony) and, at the same time, preserves the physical meaning of the analysis. The utility of multi-variate extensions of EMD is highlighted, both in terms of robust scale alignment between system components, a pre-requisite for inter-component measures, and in the estimation of feature relevance. We also illuminate that the properties of EMD scales can be used to decouple amplitude and phase information, a necessary step in order to accurately quantify signal dynamics through correlation and SE analysis which are otherwise not possible. Finally, the proposed multi-scale framework is applied to detect directionality, and higher order features such as coupling and regularity, in both synthetic and biological systems.

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“…Several electroencephalogram (EEG) based methods have been developed to assess patient's level of consciousness during general anesthesia based on spectral and entropy measures [7,8]. However, several studies have demonstrated their ineffectiveness in monitoring patients sedation level in the ICU environment [9,10].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, subjective methods such as the Riker sedation agitation scale (SAS), Richmond agitation sedation scale (RASS), Ramsay sedation scale are used to score the level of sedation in ICUs. These scoring systems rely mainly on patient's response to external and noxious stimulation, which are subjective and are less discriminative during over-sedated state [6].Several electroencephalogram (EEG) based methods have been developed to assess patient's level of consciousness during general anesthesia based on spectral and entropy measures [7,8]. However, several studies have demonstrated their ineffectiveness in monitoring patients sedation level in the ICU environment [9,10].…”

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confidence: 99%

Personalized Sedation Level Monitoring in ICU Patients Using Heart Rate Variability

Nagaraj

Biswal

Valdery

et al. 2016

2016 Computing in Cardiology Conference (CinC)

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Aim: To develop an automated system to monitor sedation levels in intensive care unit patients using heart rate variability (HRV).Methods: We developed an automatic sedation level prediction system using HRV as input to a support vector machine learning algorithm. Our data consisted of electrocardiogram recordings from a heterogeneous group of 50 mechanically ventilated adults receiving sedatives in an ICU setting. The target variable was the Richmond agitation-sedation scale score, grouped into four levels: "comatose" (-5), "deeply sedated" (-4 to -3), "lightly sedated" (-2 to 0), and "agitated" (+1 to +4). As input we used 14 features derived from the normalized-RR (NN) interval. We used leave-one-subject-out cross-validated accuracy to measure system performance.Results: A patient-independent version of the proposed system discriminated between the 4 sedation levels with an overall accuracy of 52%. A patient-specific version, where the training data was supplemented with the patient's labeled HRV epochs from the preceding 24 hours, improved classification accuracy to 60%.Conclusions: Our preliminary results suggest that the HRV varies systematically with sedation levels and has potential to supplement current clinical sedation level assessment methods. With additional variables such as disease pathology, and pharmacological data, the proposed system could lead to a fully automated system for depth of sedation monitoring IntroductionAccurate administration and optimization of sedatives dosing is important in intensive care units (ICU) [1]. Patients are often sedated in the ICU to increase their level of comfortness, tolerate several procedures and facilitate mechanical ventilation and analgesia [2,3]. Therefore, it is critical to maintain patient's optimal sedation level since both over-and under sedation could lead to adverse patient outcomes including prolonged mechanical ventilation and ICU stay, increased risk of pneumonia and delirium [4,5]. Currently, subjective methods such as the Riker sedation agitation scale (SAS), Richmond agitation sedation scale (RASS), Ramsay sedation scale are used to score the level of sedation in ICUs. These scoring systems rely mainly on patient's response to external and noxious stimulation, which are subjective and are less discriminative during over-sedated state [6].Several electroencephalogram (EEG) based methods have been developed to assess patient's level of consciousness during general anesthesia based on spectral and entropy measures [7,8]. However, several studies have demonstrated their ineffectiveness in monitoring patients sedation level in the ICU environment [9,10]. Therefore, a more robust and reliable objective method is crucial to assess the depth of sedation in the ICU.Heart rate variability (HRV) is a popular noninvasive method that can provide information about the functioning of the autonomous nervous system using electrocardiogram (ECG). We recently investigated the potential of HRV for automatically predicting level of sedation using machine l...

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Analysis of depth of anesthesia with Hilbert–Huang spectral entropy

Cited by 104 publications

References 36 publications

Analysis of EEG via Multivariate Empirical Mode Decomposition for Depth of Anesthesia Based on Sample Entropy

Analysis of EEG via Multivariate Empirical Mode Decomposition for Depth of Anesthesia Based on Sample Entropy

Intrinsic multi-scale analysis: a multi-variate empirical mode decomposition framework

Personalized Sedation Level Monitoring in ICU Patients Using Heart Rate Variability

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