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Anesth Analg 2007;104:135-139 © 2007 International Anesthesia Research Society doi: 10.1213/01.ane.0000249045.52690.e8
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Abstract |
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 Top Abstract IntroductionMETHODSRESULTSDISCUSSIONREFERENCES |
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BACKGROUND: Recently, an increasing number of electroencephalogram (EEG)-based monitors of the hypnotic component of anesthesia has become available. Most of these monitors calculate a numerical index reflecting the hypnotic component of anesthesia. Most of the underlying algorithms are proprietary. Therefore, a quality check or comparison of different indices is very complex.
METHODS: Because there is limited information about the algorithms used for index calculation of the different monitors, a reliable comparison or test of the monitors is possible only if the same set of EEG data are presented to each monitor.
RESULTS: Parallel EEG monitoring during surgery is limited to two or three monitors because the space for electrode placement on the head is limited. This problem can be solved by using the EEG player to play back recorded EEG data to different monitors.
CONCLUSIONS: The output of the player corresponds to the original EEG signal. A comparison of different indices based on identical EEGs is therefore possible. The index reproducibility can also be checked, if the same signal is presented to different monitors.
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Introduction |
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TopAbstractIntroductionMETHODSRESULTSDISCUSSIONREFERENCES |
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During the last decade, more electroencephalogram (EEG)-based monitors of the hypnotic component of anesthesia have become available. These monitors calculate an index that reflects the hypnotic component of anesthesia. Although there are different approaches to EEG analysis, all resulting indices propose to reflect the same clinical end point—the hypnotic component of anesthesia. The underlying algorithms are often proprietary and not accessible to the public. This may impede the theoretical evaluation of such an algorithm. Hence, volunteer and patient studies are required to assess the performance of such indices and, ideally, a study will provide information about the performance of several indices. A qualitative analysis and comparison of different indices are of great interest because it may help in the evaluation of the performance of different monitors using the same data set. For such a comparison, ideally the identical EEG data should be analyzed by different monitors.
Connecting different monitors to a patient’s head to compare the indices is very difficult. The space for electrode placement, especially on the forehead, is limited. This could lead to an improper electrode placement if more than one monitor is used. With the EEG player, identical, recorded EEG can be played back to the monitor one after another and thus, these problems are avoided.
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METHODS |
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TopAbstractIntroductionMETHODSRESULTSDISCUSSIONREFERENCES |
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The EEG-player was designed to play data from an EEG database containing data from patient and volunteer studies (1). The recorded EEG data are stored in a file format with a data structure similar to a database (CED Filing System [CFS], Cambridge Electronic Design, Cambridge, UK), designed to store large arrays of measurement data. Data are stored in blocks. Each data block is of the same size and holds information of all acquired channels recorded at the same time (2). A block usually contains 1or 2 s of raw EEG signal recorded with a 1 kHz sampling rate. The original EEG is recorded with a 5 kHz analog-to-digital converter sample rate followed by a digital low pass filtering and decimation to 1 kHz. The final bandwidth of the recorded signal is 0.5–400 Hz. The resolution of the analog-to-digital converter is 12 bit.
The digitized EEG signal must be converted into an analog signal which subsequently can be analyzed by an EEG-based monitor. In addition to a personal computer, a digital-to-analog converter with an output connected to a monitor is required.
A program was written in LabView (National Instruments TM, Austin, TX) to perform the signal processing. The inputs and outputs of the acquisition card’s digital-to-analog converter can be controlled by this program. It is also used to open the CFS files and extract the EEG data channels to be played back. Sequences of the recorded EEG signal can be modified for experimental purposes (e.g., signal noise or artifacts can be added). Selected sequences can be looped or other sequences can be cut out. EEG data which are not stored in CFS can also be used, but they must be converted into a text file format storing each data point of the EEG channel data into a new line. In addition, the software can be used to control the acquisition of data sent from the serial port of the connected monitors (i.e., index values calculated from the played EEG data).
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RESULTS |
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TopAbstractIntroductionMETHODSRESULTSDISCUSSIONREFERENCES |
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A multifunction data acquisition (DAQ) card (PCI 6036E, National Instruments TM) with two analog outputs converts stored and extracted EEG data into an analog signal and outputs a continuous wave form (Fig. 1). To perform the output in real time, sampling rate settings of the output must be identical to the sampling rate of the recorded EEG.
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The selected EEG data set is stored in a First In First Out buffer and sent to the acquisition card’s on-board memory to ensure a data output performance in real-time. The EEG signal output starts after the entire data are stored on memory. By these means, a delay of the output operation, caused by program internal interrupt routines, can be avoided. The DAQ card performs the output operation of data stored on the card’s memory independently from other tasks running on the computer’s central processor unit. Data in the memory are converted into a continuous wave form by a 16-bit digital-to-analog converter (DAC). The output voltage range is up to ±10 V. The resolution of the output wave form is 0.3 mV if the DAC is set to the maximum output range. The output reflects the EEG signal, but the amplitude exceeds the original range. Therefore, it must be converted into the characteristic EEG voltage range [±200 µV].
A voltage divider circuit decreases the output wave form amplitude by the factor of 50,000:1, converting an output signal amplitude of 1 V to EEG typical 20 µV. The voltage divider circuit consists of two series-connected voltage dividers for each channel. The resolution of the wave form after amplitude adjustment is 6 nV. An additional low-pass filtering to smooth the wave form is not necessary. Using only resistors and no additional capacitors, the signal’s phase characteristics are not changed.
To avoid additional noise distortion on the EEG signal, the voltage divider circuit was implemented using a surface-mounted design and shielded cables. EEG-based anesthesia monitors can be connected to the EEG player via the male part of simple uncoated 13-mm snap fasteners (standard electrode plugs). The electrode cables of the monitors can be easily connected to the player (Fig. 2). For the aspect monitoring system, the patient interface cable was modified to allow attachment of standard plugs instead of the aspect EEG sensors. To simulate different impedances, additional resistors can be switched on.
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Many monitors allow online data acquisition via a standard serial connection or specific acquisition software. The EEG player program automatically opens the serial port and stores the parameter values (e.g., EEG index, signal quality, electromyogram) sent by the monitor in a text file. With the time information received from the monitor and the computer replaying the EEG data, both, monitor and EEG data can be synchronized. With this synchronization, the recorded monitor parameters can be used for further analysis and comparison with other monitors. In order to test whether the recording and re-play alters an EEG signal and the performance of a monitor, simultaneous recordings with the respective monitor and digital EEG should be performed. Next, the EEG is re-played with the monitor connected to the DAQ card’s analog output. An agreement between index values recorded from patients and from re-played EEG signals has been demonstrated for the Bispectral Index TM calculated by the A-1000 TM and A-2000 TM (Aspect Medical Systems, Newton, MA), for the Narcotrend TM (MonitorTechnik, Bad Bramstedt, Germany) and the Cerebral State Monitor (Danmeter A/S, Odense, Denmark).
For a closer validation of the EEG player’s performance, artificial signals were generated with LabView, and played to the EEG recording device via the EEG player performing amplitude adjustment into the EEG amplitude range.
The digital data of the artificially generated signal and the data of the recorded signal were compared and evaluated. The recorded data showed no difference in the spectral component, and only minimal amplitude differences caused by the resistor’s tolerance. Sequences of the compared data are shown in Figure 3.
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DISCUSSION |
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TopAbstractIntroductionMETHODSRESULTSDISCUSSIONREFERENCES |
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With the EEG player, different monitors of the hypnotic component of anesthesia can be evaluated. For this assessment, a set of identical EEG data can be used. The recorded EEG data are played back to the monitors in real time. As the EEG data are from a database, which also contain results of clinical assessment of the level of sedation and consciousness, such a test not only allows analysis of correlation between the tested monitors and indices, but also of agreement between indices and the results of clinical assessment. This information can be obtained from stored data, even if there is no information about the algorithms used for index calculation. This overcomes the problem that no index values can be calculated from EEG data, as long as the index algorithm is not accessible.
The given acquisition card allows plugging two monitors to the EEG player at the same time using the analog outputs. Alternatively, the analysis of different monitors can be performed by sequential attachment of the monitors to the EEG player with repeated re-play of identical EEG data. Different monitors should not be connected simultaneously to the same output, because of potential interference. For example, an impedance check of the first monitor could result in a signal contamination received by the other monitor. The EEG player and an appropriate set of recorded EEG data provide a well-defined platform for comparison and testing of indices and monitors. This helps to compare different approaches to EEG-based monitoring of anesthesia. Results of newly developed indices will be directly comparable to previous studies. Even without new prospective patient or volunteer studies, numerous characteristics and the performance of a new index, or new versions of a known index, can be tested.
This has been done on the basis of a study in 40 patients having surgery under general anesthesia, including a period of intended responsiveness between intubation and skin incision (3). Originally, the study was performed to compare the Patient State Index TM and the Bispectral Index TM. The described EEG player was used to analyze the performance of additional monitors (4,5). In addition to the analysis of different monitors, the EEG player also allows testing new revisions of existing indices, even if the manufacturer does not provide detailed information about the implemented modifications.
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Footnotes |
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Accepted for publication September 25, 2006.
Author for correspondence and reprint requests to Gerhard Schneider, MD, Department of Anesthesiology, Technische Universität München, Klinikum Rd. Isar, Ismaninger St., 22, 81675 München, Munich, Germany. Address e-mail to g.schneider{at}lrz.tu-muenchen.de.
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REFERENCES |
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TopAbstractIntroductionMETHODSRESULTSDISCUSSIONREFERENCES |
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- Ningler M, Schneider G, Stockmanns G, et al. Databank for support of comprehensive study evaluations of signals for anesthesia monitoring. Biomed Tech (Berl) 2002;47 (Suppl 1, Part 2):550–3.
- Cachelin A, Gaskell G, Smith G, et al. CFS: the CED filing system. Cambridge, England: Cambridge Electronic Design, 1998.
- Schneider G, Gelb AW, Schmeller B, et al. Detection of awareness in surgical patients with EEG-based indices-bispectral index and patient state index. Br J Anaesth 2003;91:329–35.
[Abstract/Free Full Text] - Schneider G, Kochs EF, Horn B, et al. Narcotrend does not adequately detect the transition between awareness and unconsciousness in surgical patients. Anesthesiology 2004;101:1105–11.[Web of Science][Medline]
- Blum J, Klesper S, Kochs EF, Schneider G. Cerebral state index: reliable differentiation between consciousness and unconsciousness? [abstract]. Anesthesiology 2005;103:A72.
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