A COMPARISON OF MEDIAN FREQUENCY, SPECTRAL EDGE, FREQUENCY BAND RATIOS, TOTAL POWER, AND DOMINANCE SHIFTS IN THE DETERMINATION OF DEPTH OF ANESTHESIA

Background Approximate entropy, a measure of signal complexity and regularity, quantifies electroencephalogram changes during anesthesia. With increasing doses of anesthetics, burst-suppression patterns occur. Because of the high-frequency bursts, spectrally based parameters such as median electroencephalogram frequency and spectral edge frequency 95 do not decrease, incorrectly suggesting lightening of anesthesia. The authors investigated whether the approximate entropy algorithm correctly classifies the occurrence of burst suppression as deepening of anesthesia. Methods Eleven female patients scheduled for elective major surgery were studied. After propofol induction, anesthesia was maintained with isoflurane only. Before surgery, the end-tidal isoflurane concentration was varied between 0.6 and 1.3 minimum alveolar concentration. The raw electroencephalogram was continuously recorded and sampled at 128 Hz. Approximate entropy, electroencephalogram median frequency, spectral edge frequency 95, burst-suppression ratio, and burst-compensated spectral edge frequency 95 were calculated offline from 8-s epochs. The relation between burst-suppression ratio and approximate entropy, electroencephalogram median frequency, spectral edge frequency 95, and burst-compensated spectral edge frequency 95 was analyzed using Pearson correlation coefficient. Results Higher isoflurane concentrations were associated with higher burst-suppression ratios. Electroencephalogram median frequency (r = 0.34) and spectral edge frequency 95 (r = 0.29) increased, approximate entropy (r = -0.94) and burst-compensated spectral edge frequency 95 (r = -0.88) decreased with increasing burst-suppression ratio. Conclusion Electroencephalogram approximate entropy, but not electroencephalogram median frequency or spectral edge frequency 95 without burst compensation, correctly classifies the occurrence of burst-suppression pattern as increasing anesthetic drug effect.

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Quantitative EEG Correlations with Brain Glucose Metabolic Rate during Anesthesia in Volunteers

Anesthesiology ◽

10.1097/00000542-199808000-00007 ◽

1998 ◽

Vol 89 (2) ◽

pp. 323-333 ◽

Cited By ~ 113

Author(s):

Michael T. Alkire

Keyword(s):

Bispectral Index ◽

Cerebral Metabolism ◽

Functional Brain Imaging ◽

Total Power ◽

Alpha Power ◽

Median Frequency ◽

Isoflurane Anesthesia ◽

Spectral Edge ◽

Beta Power ◽

Metabolic Reduction

Background To help elucidate the relationship between anesthetic-induced changes in the electroencephalogram (EEG) and the concurrent cerebral metabolic changes caused by anesthesia, positron emission tomography data of cerebral metabolism obtained in volunteers during anesthesia were correlated retrospectively with various concurrently measured EEG descriptors. Methods Volunteers underwent functional brain imaging using the 18fluorodeoxyglucose technique; one scan always assessed awake-baseline cerebral metabolism (n = 7), and the other scans assessed metabolism during propofol sedation (n = 4), propofol anesthesia (n = 4), or isoflurane anesthesia (n = 5). The EEG was recorded continuously during metabolism assessment using a frontal-mastoid montage. Power spectrum variables, median frequency, 95% spectral edge, and bispectral index (BIS) values subsequently were correlated with the percentage of absolute cerebral metabolic reduction (PACMR) of glucose utilization caused by anesthesia. Results The percentage of absolute cerebral metabolic reduction, evident during anesthesia, trended median frequency (r = -0.46, P = 0.11), and the spectral edge (r = -0.52, P = 0.07), and correlated with anesthetic type (r = -0.70, P < 0.05), relative beta power (r = -0.60, P < 0.05), total power (r = 0.71,P < 0.01), and bispectral index (r = -0.81,P < 0.001). After controlling for anesthetic type, only bispectral index (r = 0.40, P = 0.08) and alpha power (r = 0.37, P = 0.10) approached significance for explaining residual percentage of absolute cerebral metabolic reduction prediction error. Conclusions Some EEG descriptors correlated linearly with the magnitude of the cerebral metabolic reduction caused by propofol and isoflurane anesthesia. These data suggest that a physiologic link exists between the EEG and cerebral metabolism during anesthesia that is mathematically quantifiable.

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Median Frequency Revisited

Anesthesiology ◽

10.1097/01.anes.0000278904.63884.4c ◽

2007 ◽

Vol 107 (3) ◽

pp. 397-405 ◽

Cited By ~ 14

Author(s):

Denis Jordan ◽

Gudrun Stockmanns ◽

Eberhard F. Kochs ◽

Gerhard Schneider

Keyword(s):

Spectral Parameter ◽

Bispectral Index ◽

Median Frequency ◽

Spectral Parameters ◽

Data Set ◽

Spectral Edge Frequency ◽

Spectral Edge ◽

Prediction Probability ◽

Hypnotic Agent ◽

The Fourier Transform

Background In the past, several electroencephalographic parameters have been presented and discussed with regard to their reliability in discerning consciousness from unconsciousness. Some of them, such as the median frequency and spectral edge frequency, are based on classic spectral analysis, and it has been demonstrated that they are of limited capacity in differing consciousness and unconsciousness. Methods A generalized approach based on the Fourier transform is presented to improve the performance of electroencephalographic parameters with respect to the separation of consciousness from unconsciousness. Electroencephalographic data from two similar clinical studies (for parameter development and evaluation) in adult patients undergoing general anesthesia with sevoflurane or propofol are used. The study period was from induction of anesthesia until patients followed command after surgery and includes a reduction of the hypnotic agent after tracheal intubation until patients followed command. Prediction probability was calculated to assess the ability of the parameters to separate consciousness from unconsciousness. Results On the basis of the training set of 40 patients, a new spectral parameter called weighted spectral median frequency was designed, achieving a prediction probability of 0.82 on the basis of the "classic" electroencephalographic frequency range up to 30 Hz. Next, in the evaluation data set, the prediction probability was 0.79, which is higher than the prediction probability of median frequency (0.58) or spectral edge frequency (0.59) and the Bispectral Index (0.68) as calculated from the same data set. Conclusions A more general approach of the design of spectral parameters leads to a new electroencephalographic spectral parameter that separates consciousness from unconsciousness significantly better than the Bispectral Index.

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Electroencephalogram variability analysis for monitoring depth of anesthesia

Journal of Neural Engineering ◽

10.1088/1741-2552/ac3316 ◽

2021 ◽

Author(s):

Yi-Feng Chen ◽

Shou-Zen Fan ◽

Maysam F Abbod ◽

Jiann-Shing Shieh ◽

...

Keyword(s):

Correlation Coefficient ◽

Pearson Correlation ◽

Permutation Entropy ◽

Time Interval ◽

Detection Accuracy ◽

Median Frequency ◽

Depth Of Anesthesia ◽

Spectral Edge Frequency ◽

Significance Level ◽

Significant Difference

Abstract In this paper, a new approach of extracting and measuring the variability in electroencephalogram (EEG) was proposed to assess the depth of anesthesia (DOA) under general anesthesia. The EEG variability (EEGV) was extracted as a fluctuation in time interval that occurs between two local maxima of EEG. Eight parameters related to EEGV were measured in time and frequency domains, and compared with state-of-the-art DOA estimation parameters, including sample entropy, permutation entropy, median frequency and spectral edge frequency of EEG. The area under the receiver-operator characteristics curve (AUC) and Pearson correlation coefficient were used to validate its performance on 56 patients. Our proposed EEGV-derived parameters yield significant difference for discriminating between awake and anesthesia stages at a significance level of 0.05, as well as improvement in AUC and correlation coefficient on average, which surpasses the conventional features of EEG in detection accuracy of unconscious state and tracking the level of consciousness. To sum up, EEGV analysis provides a new perspective in quantifying EEG and corresponding parameters are powerful and promising for monitoring DOA under clinical situations.

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