Department of Anaesthesiology, Technische Universität München, Klinikum rechts der Isar, Ismaningerstr. 22, 81675 Munich, Germany
* Corresponding author. E-mail: Gerhard.Schneider{at}lrz.tum.de
Accepted for publication August 6, 2004.
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Abstract |
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Methods. Ninety artefact-free EEG segments of length 8 s were obtained from a database that contains perioperatively recorded EEG data. For the present analysis, EEG data were selected from 39 patients with propofolremifentanil or sevofluraneremifentanil anaesthesia with a period of awareness. Half of the EEG segments were recorded during periods of awareness as defined by an adequate response to the command squeeze my hand. The other half were from unresponsive patients. The power spectral density was calculated for each segment. The performance of each frequency bin of the power spectrum as a detector of awareness was assessed with a remapped prediction probability rPK, i.e. the prediction probability PK mapped to a range of 0.51.
Results. The remapped prediction probability was high (rPK>0.8) for low frequencies (<15 Hz) and for high frequencies (>26 Hz), with a minimum (rPK<0.55) at 21 Hz. Indentations in the performance spectrum occur at the power-line frequency (50 Hz) and its harmonics and at 78 Hz, probably caused by the continuous impedance measurement of another device used in parallel. With the exception of the indentations, the remapped prediction probability of the high frequencies (>35 Hz) was >0.95.
Conclusions. The best performance for the detection of awareness was achieved by EEG power spectral frequencies from >35 Hz up to 127 Hz. This frequency band may be dominated by muscle activity. The frequency band between 15 and 26 Hz may be of limited value, as reflected by lower rPK values.
Keywords: awareness ; measurement techniques, spectral analysis ; monitoring, electroencephalography ; monitoring, intraoperative
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Introduction |
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In the present study we examined which parts of the EEG power spectrum are most useful for discrimination between awareness and responsiveness. Usually, the power spectrum is analysed on the basis of broader frequency bands, which represent the sum of power of several smaller frequency bands (bins). In the present analysis, we used a different approach and calculated a performance spectrum. This spectrum represents the performance of each single EEG power spectral frequency bin to discriminate between awareness and unresponsiveness. The spectral band width was 1 Hz for each frequency bin tested.
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Methods |
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As the original study was a comparison between BIS and Patient State Index (PSI), the EEG was simultaneously recorded with a Physiometrix PSA-4000. A single-channel EEG signal recorded from the Aspect A-1000 was analysed with electrodes applied according to the international 1020 system at positions AT1, Fz (reference) and Fp1 (ground). EEG segments of 8 s duration were selected from time intervals immediately preceding or following the transition between awareness and unconsciousness. After visual analysis, artefact-contaminated EEG segments were excluded. Only segments with a stable baseline and without high-amplitude artefacts were analysed. Forty-five EEG segments from the awake patient state and 45 segments from the unresponsive state were included in the analysis. The 8 s EEG segments were divided into eight non-overlapping subsegments of duration 1 s. The mean value was subtracted and a power spectrum was computed for each of the eight subsegments of an EEG segment, with a Hanning window applied to reduce spectral leakage, and averaged to form a single power spectrum for each EEG segment. The interval of frequency bins in the power spectrum was 1 Hz. Each of the frequency bins was treated as a single parameter and tested for its ability to separate awareness from unresponsiveness; 127 such parameters (frequency bins) were available (we do not count the zero frequency bin). The prediction probability PK6 was calculated for each frequency bin of the power spectrum. For each frequency bin, PK is calculated by analysis of the power of this frequency bin and the recorded level of anaesthesia (i.e. awareness or unresponsiveness). PK is a value between 0 and 1. For PK analysis, power values are ranked in descending order. If the recorded levels of anaesthesia are entirely separated by this ranking, PK=1 (or PK=0). With increasing overlaps of the level of anaesthesia after this ranking, PK approaches 0.5. PK=1 for the detection of awareness means that the power of the frequency bin increases as the patient responds to the verbal command squeeze my hand. Alternatively, PK=0 means that the power of the frequency bin decreases as the patient responds to command. PK=0.5 means that the power of the frequency bin is useless for predicting the response to the verbal command squeeze my hand. Based on an Excel macro (PK MACRO) provided by Warren D. Smith, a Visual Basic for Applications (VBA) module was programmed. This module was integrated into a Microsoft Access database which contained the spectral power information of all frequency bins of the selected EEG signals. We used a modified prediction probability rPK with values remapped to the interval between 0.5 and 1 to obtain the performance of the parameters as a detector of awareness independent of negativity or positivity of the correlation. Remapped PK was calculated using the formula
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Results |
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Discussion |
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We found indentations for the power-line frequency of 50 Hz and its harmonic at 100 Hz. An additional indentation is located at 78 Hz, and may be caused by the continuous impedance check performed by the Physiometrix PSA-4000 device used in parallel with the recording A-1000 device. This suggests that signal quality may severely decrease when other devices interfere. Most likely, these indentations at 50 and 100 Hz are caused by power-line interference present in the signal. Some of the EEG segments used revealed an increased power of the 50 Hz component relative to its neighbouring frequency bins and thus indicated the presence of 50 Hz interference.
The high performance of higher-frequency components suggests that facial muscle activity may have influenced or possibly dominated the results, as EMG activity during awareness is higher than during unresponsiveness. None of our patients had been under complete neuromuscular block. Under the given circumstances, we cannot reliably differentiate between EEG and EMG activity because the EEG gamma band and the EMG frequency range overlap. As a consequence, the present results do not indicate whether the EEG or the EMG are the reason for the good performance of high frequencies.
Classical EEG analysis is usually performed in frequency bands below 30 Hz, i.e. the delta, theta, alpha and beta bands. The results of our study show that frequencies in the beta band perform quite differently. An increase of the lower part of the beta band (<21 Hz) indicates an increasing probability of unresponsiveness, whereas an increase of the higher part of the beta band (>21 Hz) indicates an increasing probability of awareness. Owing to this oppositional behaviour it may be reasonable to split the beta band into two parts when analysing beta-band power.
Several EEG monitors of depth of anaesthesia also use higher-frequency bands. The BIS components Beta Ratio and Sync Fast Slow use frequencies up to 47 Hz.1 Beta Ratio incorporates frequencies between 11 and 20 Hz (i.e. mainly beta activity) and between 30 and 47 Hz (i.e. gamma activity). The SNAP index, which has now been withdrawn from the market, was calculated from a low-frequency band (0.140 Hz) and a high-frequency band.2 The frequency range 4080 Hz is omitted because it may be dominated by EMG. However, our study did not show differences between the frequency ranges 4080 Hz and 80127 Hz. The Datex Entropy Module calculates state entropy (SE) from the EEG-dominated 0.832 Hz frequency band and the response entropy (RE) from the 0.847 Hz frequency band, which includes EMG activity.8 These examples show that higher-frequency components (>30 Hz) of EEG or EMG may be useful for depth of anaesthesia monitoring. Currently, it is uncertain whether the high-frequency signal components reflect activity of the main target organ of anaesthesia, the brain (EEG), or just an indirect and unspecific parameter, i.e. muscle activity (EMG). Further studies are required to evaluate the performance of these components during complete or varying neuromuscular blockade. The results presented here indicate that the high-frequency components are well suited to detect awareness, but also that the performance to detect awareness may be limited when artefacts are present.
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Acknowledgments |
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References |
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2 Wong CA, Fitzgerald PC, McCarthy RJ. Comparison of depth of anesthesia indices (SNAP vs Bispectral) during balanced general anesthesia in patients undergoing outpatient gynecologic surgery. www.asa-abstracts.com 2002, A-553
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8 Viertiö-Oja H, Meriläinen P, Paloheimo M, et al. Optimization of response time of spectral EEG entropy enables early warning of emergence from unconsciousness. Abstracts of the 5th International Conference on Memory, Awareness and Consciousness 2001. http://www.maacc.org/_aab/029.pdf
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