Behaviour of spectral entropy, spectral edge frequency 90%, and alpha and beta power parameters during low-dose propofol infusion

Mahon, Padraig; Greene, Barry R.; Boylan, Geraldine B.; Shorten, G.

doi:10.1093/bja/aen161

Cited by 25 publications

(19 citation statements)

References 23 publications

(26 reference statements)

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“…The EMD [3] is an adaptive method for non-linear, non-stationary data analysis based on the intrinsic characteristics of time series. In the EMD method, the original signal X(t) is decomposed in terms of Intrinsic Mode Functions (IMFs), c i , expressed as follows: (1) where r n is the residue of X(t).…”

Section: The Extended Emd Methodsmentioning

confidence: 99%

“…Furthermore, different methods have been proposed to analyze the EEG signal [1][2][3] that reveal the relationship between the EEG signals and the level of consciousness of patients. Specifically, the development of entropy methods in the last two decades has contributed to the analysis and application of this type of physiological signals.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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: The Extended Emd Methodsmentioning

confidence: 99%

Section: Introductionmentioning

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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show abstract

“…This is a reasonable location to observe the strong synchronization and amplitude increase of frontal alpha to beta bands close to loss of consciousness induced by propofol medication (e.g. [6], [5]). Multichannel analysis showed a consistent behavior for all the subjects participating in the experiment, that is a gradual increase in frontal activity towards LOC as it was expected from previous studies.…”

Section: Resultsmentioning

confidence: 80%

“…Frequency and time domain derivations of spontaneous EEG have been involved in anesthesia research and monitoring, e.g. [3], [4], [5], [6]. Processed auditory evoked potentials (AEP) have been proposed as a potential method for the detection of intra-operative awareness [7].…”

Section: Introductionmentioning

confidence: 99%

Kalman smoother based time-varying spectrum estimation of EEG during single agent propofol anesthesia

Georgiadis

Tarvainen

Kaskinoro

et al. 2009

2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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A time-varying parametric spectrum estimation method for analyzing EEG dynamics is presented. EEG signals are first modeled as a time-varying auto-regressive stochastic process and the model parameters are estimated recursively with a Kalman smoother algorithm. Time-varying spectrum estimates are then obtained from the estimated parameters. The proposed method was applied to measurements collected during low dose propofol anesthesia. The method was able to detect changes of event related (de)synchronization type elicited by verbal command.

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“…Due to that very decisive reason, it would be reasonable to monitor the brain activity by examining the EEG to assess the DoA [2]. Several studies have been addressed to evaluate the relationship between the EEG and the anesthesia [3–5]. EEG continuous signals state the complicate nonlinearity and progressive properties [6, 7] and are frequently interfered by other signals, like the electric power and elctrosurgical knives.…”

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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Behaviour of spectral entropy, spectral edge frequency 90%, and alpha and beta power parameters during low-dose propofol infusion

Abstract: Relative beta power and spectral entropy when considered over the propofol effect-site range studied here increase in value, and correlate well with clinical assessment of sedation.

Cited by 25 publications

References 23 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

Kalman smoother based time-varying spectrum estimation of EEG during single agent propofol anesthesia

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

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