Novel characterization method of impedance cardiography signals using time-frequency distributions.

The purpose of this document is to describe a methodology to select the most adequate time-frequency distribution (TFD) kernel for the characterization of impedance cardiography signals (ICG). The predominant ICG beat was extracted from a patient and was synthetized using time-frequency variant Fourier approximations. These synthetized signals were used to optimize several TFD kernels according to a performance maximization. The optimized kernels were tested for noise resistance on a clinical database. The resulting optimized TFD kernels are presented with their performance calculated using newly proposed methods. The procedure explained in this work showcases a new method to select an appropriate kernel for ICG signals and compares the performance of different time-frequency kernels found in the literature for the case of ICG signals. We conclude that, for ICG signals, the performance (P) of the spectrogram with either Hanning or Hamming windows (P = 0.780) and the extended modified beta distribution (P = 0.765) provided similar results, higher than the rest of analyzed kernels. Graphical abstract Flowchart for the optimization of time-frequency distribution kernels for impedance cardiography signals.

Monitoring hypnotic effect and nociception with two EEG-derived indices, qCON and qNOX, during general anaesthesia.

BACKGROUND: The objective of the present study was to validate the qCON index of hypnotic effect and the qNOX index of nociception. Both indices are derived from the frontal electroencephalogram (EEG) and implemented in the qCON 2000 monitor (Quantium Medical, Barcelona, Spain). METHODS: The study was approved by the local ethics committee, including data from 60 patients scheduled for ambulatory surgery undergoing general anaesthesia with propofol and remifentanil, using TCI. The Bis (Covidien, Boulder, CO, USA) was recorded simultaneously with the qCON. Loss of eyelash reflex [loss of consciousness (LOC)] was recorded, and prediction probability for Bis and qCON was calculated. Movement as a response to noxious stimulation [laryngeal mask airway (LMA) insertion, laryngoscopy and tracheal intubation] was registered. The correlation coefficient between qCON and Bis was calculated. The patients were divided into movers/non-movers as a response to noxious stimulation. A paired t-test was used to assess significant difference for qCON and qNOX for movers/non-movers. RESULTS: The prediction probability (Pk) and the standard error (SE) for qCON and Bis for detecting LOC was 0.92 (0.02) and 0.94 (0.02) respectively (t-test, no significant difference). The R between qCON and Bis was 0.85. During the general anaesthesia (Ce propofol > 2 µg/ml, Ce remifentanil > 2 ng/ml), the mean value and standard deviation (SD) for qCON was 45 (8), while for qNOX it was 40 (6). The qNOX pre-stimuli values were significantly different (P < 0.05) for movers/non-movers as a response to LMA insertion [62.5 (24.0) vs. 45.5 (24.1)], tracheal intubation [58.7 (21.8) vs. 41.4 (20.9)], laryngoscopy [54.1 (21.4) vs. 41.0 (20.8)]. There were no significant differences in remifentanil or propofol effect-site concentrations for movers vs. non-movers. CONCLUSION: The qCON was able to reliably detect LOC during general anaesthesia with propofol and remifentanil. The qNOX showed significant overlap between movers and non-movers, but it was able to predict whether or not the patient would move as a response to noxious stimulation, although the anaesthetic concentrations were similar.

Modeling the effect of propofol and remifentanil combinations for sedation-analgesia in endoscopic procedures using an Adaptive Neuro Fuzzy Inference System (ANFIS).

BACKGROUND: The increasing demand for anesthetic procedures in the gastrointestinal endoscopy area has not been followed by a similar increase in the methods to provide and control sedation and analgesia for these patients. In this study, we evaluated different combinations of propofol and remifentanil, administered through a target-controlled infusion system, to estimate the optimal concentrations as well as the best way to control the sedative effects induced by the combinations of drugs in patients undergoing ultrasonographic endoscopy. METHODS: One hundred twenty patients undergoing ultrasonographic endoscopy were randomized to receive, by means of a target-controlled infusion system, a fixed effect-site concentration of either propofol or remifentanil of 8 different possible concentrations, allowing adjustment of the concentrations of the other drug. Predicted effect-site propofol (C(e)pro) and remifentanil (C(e)remi) concentrations, parameters derived from auditory evoked potential, autoregressive auditory evoked potential index (AAI/2) and electroencephalogram (bispectral index [BIS] and index of consciousness [IoC]) signals, as well as categorical scores of sedation (Ramsay Sedation Scale [RSS] score) in the presence or absence of nociceptive stimulation, were collected, recorded, and analyzed using an Adaptive Neuro Fuzzy Inference System. The models described for the relationship between C(e)pro and C(e)remi versus AAI/2, BIS, and IoC were diagnosed for inaccuracy using median absolute performance error (MDAPE) and median root mean squared error (MDRMSE), and for bias using median performance error (MDPE). The models were validated in a prospective group of 68 new patients receiving different combinations of propofol and remifentanil. The predictive ability (P(k)) of AAI/2, BIS, and IoC with respect to the sedation level, RSS score, was also explored. RESULTS: Data from 110 patients were analyzed in the training group. The resulting estimated models had an MDAPE of 32.87, 12.89, and 8.77; an MDRMSE of 17.01, 12.81, and 9.40; and an MDPE of -1.86, 3.97, and 2.21 for AAI/2, BIS, and IoC, respectively, in the absence of stimulation and similar values under stimulation. P(k) values were 0.82, 0.81, and 0.85 for AAI/2, BIS, and IoC, respectively. The model predicted the prospective validation data with an MDAPE of 34.81, 14.78, and 10.25; an MDRMSE of 16.81, 15.91, and 11.81; an MDPE of -8.37, 5.65, and -1.43; and P(k) values of 0.81, 0.8, and 0.8 for AAI/2, BIS, and IoC, respectively. CONCLUSION: A model relating C(e)pro and C(e)remi to AAI/2, BIS, and IoC has been developed and prospectively validated. Based on these models, the (C(e)pro, C(e)remi) concentration pairs that provide an RSS score of 4 range from (1.8 µg·mL(-1), 1.5 ng·mL(-1)) to (2.7 µg·mL(-1), 0 ng·mL(-1)). These concentrations are associated with AAI/2 values of 25 to 30, BIS of 71 to 75, and IoC of 72 to 76. The presence of noxious stimulation increases the requirements of C(e)pro and C(e)remi to achieve the same degree of sedative effects.

Can the cerebral state monitor replace the bispectral index in monitoring hypnotic effect during propofol/remifentanil anaesthesia?

BACKGROUND: In 2004, the cerebral state monitor, CSM, was launched as a low-cost alternative to the bispectral index, BIS, for monitoring depth of sleep during anaesthesia. We tested whether the two monitors would reflect hypnosis equally during propofol/remifentanil anaesthesia. METHODS: During laparoscopy or breast/surface surgery, 55 non-paralyzed patients were monitored simultaneously with the BIS and the CSM. Trend curves for the indexes [BIS and cerebral state index (CSI)] were compared for congruence. The difference between the two indexes for the entire course was quantified, and the ability of the two monitors to separate awake from asleep during induction was described. RESULTS: In the majority of the patients, 87%, there was a good fit between the indexes. There were major deviations in seven patients, in whom CSI indicated that the patients were awake during parts of the course despite clinical sleep, correctly identified with the BIS. Both indexes separated awake from asleep during induction in the individual patient, but the overlap in values between patients was more pronounced for CSI. CONCLUSION: CSM and BIS show some important differences in measuring hypnotic state during clinical propofol/remifentanil anaesthesia.

Pitfalls and challenges when assessing the depth of hypnosis during general anaesthesia by clinical signs and electronic indices.

The objective of this article was to review the present methods used for validating the depth of hypnosis. We introduce three concepts, the real depth of hypnosis (DHreal), the observed depth of hypnosis (DHobs), and the electronic indices of depth of hypnosis (DHel-ind). The DHreal is the real state of hypnosis that the patient has in a given moment during the general anaesthesia. The DHobs is the subjective assessment of the anaesthesiologist based on clinical signs. The DHel-ind is any estimation of the depth of hypnosis given by an electronic device. The three entities DHreal, DHobs and DHel-ind should in the ideal situation be identical. However, this is rarely the case. The correlation between the DHobs and the DHel-ind can be affected by a number of factors such as the stimuli used for the assessment of the level of consciousness or the administration of analgesic agents or neuro muscular blocking agents. Opioids, for example, can block the response to tactile and noxious stimuli, and even the response to verbal command could vanish, hence deeming the patient in a lower depth of hypnosis than the real patient state. The DHel-ind can be disturbed by the presence of facial muscular activity. In conclusion, although several monitors and clinical scoring scales are available to assess the depth of hypnosis during general anaesthesia, care should be taken when interpreting their results.

Comparison of auditory evoked potentials and the A-line ARX Index for monitoring the hypnotic level during sevoflurane and propofol induction.

BACKGROUND: Extraction of the middle latency auditory evoked potentials (AEP) by an auto regressive model with exogenous input (ARX) enables extraction of the AEP within 1.7 s. In this way, the depth of hypnosis can be monitored at almost real-time. However, the identification and the interpretation of the appropriate signals of the AEP could be difficult to perform during the anesthesia procedure. This problem was addressed by defining an index which reflected the peak amplitudes and latencies of the AEP, developed to improve the clinical interpretation of the AEP. This index was defined as the A-line Arx Index (AAI). METHODS: The AEP and AAI were compared with the Modified Observers Assessment of Alertness and Sedation Scale (MOAAS) in 24 patients scheduled for cardiac surgery, anesthetized with propofol or sevoflurane. RESULTS: When comparing the AEP peak latencies and amplitudes and the AAI, measured at MOAAS level 5 and level 1, significant differences were achieved. (mean(SD) Nb latency: MOAAS 5 51.1 (7.3) ms vs. MOAAS 1: 68.6 (8.1) ms; AAI: MOAAS 5 74.9 (13.3) vs. MOAAS 1 20.7 (4.7)). Among the recorded parameters, the AAI was the best predictor of the awake/anesthetized states. CONCLUSION: We conclude that both the AAI values and the AEP peak latencies and amplitudes correlated well with the MOAAS levels 5 (awake) and 1 (anesthetized).

Effect of sevoflurane on the mid-latency auditory evoked potentials measured by a new fast extracting monitor.

BACKGROUND: Mid-latency auditory evoked potentials (MLAEP) are widely suppressed during general anesthesia and may therefore be useful for assessment of the depth of anesthesia. However, interpretation of amplitudes and latencies in the AEP signal is time consuming. A new monitor (A-line) that quantifies the MLAEP into an index has therefore been developed. The present study aimed to assess the precision of a prototype of the new monitor and to test the hypothesis that the depth of anesthesia index shows a graded response with changing steady-state end-expiratory concentrations of sevoflurane. METHODS: We studied 10 ASA physical status I or II patients undergoing elective hysterectomy under combined epidural and general anesthesia by sevoflurane. Baseline auditory evoked potentials were recorded in the conscious patient immediately before induction of general anesthesia. Depth of anesthesia indices were recorded before anesthesia and at decreasing end-expiratory steady-state sevoflurane concentrations of 2.0%, 1.5%, 1.0% and 0.5%. All indices were recorded in duplicate 6 s apart. By use of an autoregressive model with exogenous input (ARX-model), the monitor extracted the AEP within 6 s. The depth of anesthesia AEP index calculated in this way was defined as the A-line ARX index (AAI). RESULTS: Approximately 95% of the differences between repeated recordings were 5 AAI-units or less. A wide interindividual variation was observed at each observation point. AAI at 1%, 1.5% and 2% end-expiratory concentration was significantly less than the baseline AAI obtained before induction of anesthesia (P < 0.001). AAI did not change significantly in the 1-2% concentration range. CONCLUSION: The new monitor was precise. Attenuation of the A-line ARX-index (AAI) for mid-latency auditory evoked potentials (MLAEP) during general anesthesia was profound. However, the monitor did not show a graded response with changing end-expiratory steady-state concentrations of sevoflurane.

Effects of CO2 anaesthesia on central nervous system activity in swine.

The objective of the study was to examine the changes in central nervous system (CNS) activity and physical behaviour during induction and awakening from CO2 anaesthesia. Two studies, each using pigs immersed into 90% CO2 gas for a period of 60 s were performed. In study 1, we monitored middle latency auditory evoked potentials (changes in latencies, amplitudes and a depth of anaesthesia index), electroencephalographic parameters (delta, theta, alpha and beta electroencephalographic power and 95% spectral edge frequency) and heart rate; and in study 2, we monitored body movements and arterial and venous partial pressure of CO2 and O2. No behavioural signs of distress were observed during the early part of the induction. The swine exhibited muscular activity from 13-30 s after induction-start as well as during awakening from anaesthesia, possibly because of a transitory weaker suppression of the brain stem than of the cortex. The CNS and blood gas parameters started to change from the very start of induction. The CNS suppression lasted only approximately one minute after the end of the induction period. The two studies indicated a good temporal relationship between changes in amplitude, depth of anaesthesia index, spectral edge frequency, and arterial PCO2 during the induction period.

Middle-latency auditory evoked potentials during induction of thiopentone anaesthesia in pigs.

A method is described for measuring middle-latency auditory evoked potentials (MLAEP) in consciously awake, non-sedated pigs during the induction of thiopentone anaesthesia (0.6 ml/kg, 2.5% thiopentone solution). It was done by using autoregressive modelling with an exogenous input (ARX). The ability to perceive pain during the induction was compared with (1) the changes in latencies and amplitudes of the MLAEP, (2) the change in a depth of anaesthesia index based on the ARX-model and (3) the change in the 95% spectral edge frequency. The pre-induction MLAEP was easily recordable and looked much like the one in man, dogs and rats. The temporal resolution in the ARX method was sufficiently high to describe the fast changes occurring during induction of thiopentone anaesthesia. As previously reported from studies in man, dogs and rats, induction of thiopentone anaesthesia resulted in significantly increased latencies and decreased amplitudes of the MLAEP trace as well as in a significantly reduced depth of anaesthesia index and spectral edge frequency. None of the changes, however, related well to the ability to react to a painful stimulus. Whether an ARX-based depth of anaesthesia index designed especially for pigs might be better than the present index (designed for man) for assessing depth of anaesthesia must await the results of further studies.

EEG complexity as a measure of depth of anesthesia for patients.

A new approach for quantifying the relationship between brain activity patterns and depth of anesthesia (DOA) is presented by analyzing the spatio-temporal patterns in the electroencephalogram (EEG) using Lempel-Ziv complexity analysis. Twenty-seven patients undergoing vascular surgery were studied under general anesthesia with sevoflurane, isoflurane, propofol, or desflurane. The EEG was recorded continuously during the procedure and patients' anesthesia states were assessed according to the responsiveness component of the observer's assessment of alertness/sedation (OAA/S) score. An OAA/S score of zero or one was considered asleep and two or greater was considered awake. Complexity of the EEG was quantitatively estimated by the measure C(n), whose performance in discriminating awake and asleep states was analyzed by statistics for different anesthetic techniques and different patient populations. Compared with other measures, such as approximate entropy, spectral entropy, and median frequency, C(n) not only demonstrates better performance (93% accuracy) across all of the patients, but also is an easier algorithm to implement for real-time use. The study shows that C(n) is a very useful and promising EEG-derived parameter for characterizing the (DOA) under clinical situations.

Changes in rapidly extracted auditory evoked potentials during tracheal intubation.

BACKGROUND: One of the problems encountered in assessment of the hypnotic level during anesthesia is the extraction of a consistent and reliable measure online and close to real time. Hemodynamic parameters such as heart rate and blood pressure are not, at least with the traditional single parameter versus time presentation, adequate for ensuring an optimal level of anesthesia, especially when using neuromuscular blocking agents (NMBA). In the literature, it has been demonstrated that auditory evoked potentials (AEP) are able to provide two aspects relevant to determining level of anesthesia: firstly, they have identifiable anatomical significance and, secondly, their characteristics reflect the way the brain perceives a stimulus. METHODS: The aim of this study was to evaluate the AEP index based on a system identification model, the autoregressive model with exogenous input (ARX-model), and to compare it to the classical method, the moving time average (MTA). The ARX enables the extraction within 15-25 sweeps, depending on the signal-to-noise ratio (SNR), whereas MTA typically needs 250-500 sweeps. The hypothesis of the present study was that since the ARX-model extracts the AEP faster than the MTA-model, the former should be able to detect changes during the brief, intense stimulus of endotracheal intubation. Twelve female patients scheduled for gynecological surgery were included in the study. Anesthesia was initiated with thiopentone and maintained with isoflurane and alfentanil. The AEP was mapped into an index (AEP-index) normalized to 100 when the individual was awake and decreasing to an average of 25 during thiopentone induced anaesthesia. The results were compared to those obtained by MTA-extracted AEP. RESULTS: During tracheal intubation 9 patients showed an increase in the ARX-extracted AEP-index larger than 15, and 6 of these patients showed an increase larger than 25 (mean increase=33, SD=18). The MTA-extracted AEP-index showed only one patient with an increase larger than 15. The ARX-extracted AEP changed significantly faster than the MTA-extracted AEP. CONCLUSION: The ARX-extracted AEP-index increases during tracheal intubation. There is a significant difference between the ARX-extracted AEP and the traditional MTA-extracted AEP, in terms of response time. In order to trace short-lasting changes in the hypnotic level by AEP, the AEP should be extracted by a method with a fast response such as the ARX-model.

[Comparison of an auditory evoked potentials index and a bispectral index versus clinical signs for determining the depth of anesthesia produced by propofol or sevoflurane]. / Comparación de la efectividad de un índice de potenciales evocados auditivos y un índice biespectral con los signos clínicos en la determinación de la profundidad hipnótica durante anestesia con propofol o sevoflurano.

OBJECTIVES: To evaluate an anesthetic depth index (ADI) obtained from auditory evoked potentials and a bispectral EEG index (BIS) in comparison with clinical assessment of anesthetic depth using the modified observer's assessment of awareness/sedation scale (MOAA/SS), for induction of anesthesia with propofol or sevoflurane as the only agent. PATIENTS AND METHODS: The ADI and BIS were recorded simultaneously in this prospective study and compared to the MOAA/SS during the anesthetic induction of 26 adults undergoing elective heart surgery. Assignment of patients to two groups was random. Group A (n = 13) patients were induced with propofol (target dose 5 micrograms.ml-1 in 5 min). Induction in group B (n = 13) was with sevoflurane (8% tidal volume). A scheme of awake-sleeping-awake-sleeping was followed. The means of the two indexes were compared (Mann-Whitney test) one minute before the patient slept (awake) and one minute later (sleeping), and the evolution of the indexes was compared during awake/sleep and sleep/awake phase changes and while the patients were in a stable sleep phase. The sensitivity and specificity of each index was analyzed in function of the MOAA/SS. We also analyzed the time elapsing from the moment the patient fell asleep (MOAA/SS 2) until the two indexes reached published reference values (ADI = 38, BIS = 60). RESULTS: After induction with propofol (group A) the ADI fell to 29.2 +/- 11.7 and the BIS fell to 63.5 +/- 13.4. After induction with sevoflurane (group B) the ADI fell to 33.8 +/- 14.9 and the BIS to 66.8 +/- 15. The ADI value that best discriminated between arousal and sleeping (sensitivity 100%) was 38; the BIS value that best discriminated was 60. The responses to sound in decibels (dB) during "awake/sleeping" and "sleeping/awake" phases were, respectively, -3.8 dB and -4.5 dB for the ADI and -1.5 dB and -0.8 dB for the BIS. With the patient in stable sleep, response to the two indexes was at -0.79 dB. In group A, the ADI detected MOAA/SS 2 significantly earlier (ADI 13.1 +/- 30 s; BIS 56 +/- 36 s; p < 0.05). No patient reported remembering the study period. CONCLUSIONS: Monitoring anesthetic depth with the ADI or BIS was technically easy and effective for detecting whether patients were awake or sleeping. The ADI response was faster and identified awake/sleeping and sleeping/awake phase changes better than did the BIS.

Identification of causal relations between haemodynamic variables, auditory evoked potentials and isoflurane by means of fuzzy logic.

The aim of this study was to identify a possible relationship between haemodynamic variables, auditory evoked potentials (AEP) and inspired fraction of isoflurane (ISOFl). Two different models (isoflurane and mean arterial pressure) were identified using the fuzzy inductive reasoning (FIR) methodology. A fuzzy model is able to identify non-linear and linear components of a causal relationship by means of optimization of information content of available data. Nine young female patients undergoing hysterectomy under general anaesthesia were included. Mean arterial pressure (MAP), heart rate (HR), end-tidal expired carbon dioxide (CO2ET), AEP and ISOFl were monitored with a sampling time of 10 s. The AEP was extracted using an autoregressive model with exogenous input (ARX model) which decreased the processing time compared with a moving time average. The AEP was mapped into a scalar, termed the depth of anaesthesia index (DAI) normalized to 100 when the patient was awake and descending to an average of 25 during loss of consciousness. The FIR methodology identified those variables among the input variables (MAP, HR, CO2ET, DAI or ISOFl) that had the highest causal relation with the output variables (ISOFl and MAP). The variables with highest causal relation constitute the ISOFl and MAP models. The isoflurane model predicted the given anaesthetic dose with a mean error of 12.1 (SD 10.0)% and the mean arterial pressure model predicted MAP with a mean error of 8.5 (7.8)%.

On-line analysis of middle latency auditory evoked potentials (MLAEP) for monitoring depth of anaesthesia in laboratory rats.

In laboratory animals as well as in human beings a depth of anaesthesia, where the subject has no pain or recall of events from the surgery, should be provided. Haemodynamic parameters such as heart rate and blood pressure are not a guarantee for an optimal depth of anaesthesia, especially when using neuromuscular blocking agents (NMBA). A number of studies suggest that the Middle Latency Auditory Evoked Potentials (MLAEP) contain information about the state of consciousness in humans. The purpose of this study was to examine whether the AEP could serve as an indicator of depth of anaesthesia in rats. The AEP was elicited with a click stimulus and monitored in an 80 ms window synchronised to the stimulus. The AEP was extracted applying an Auto Regressive Model with Exogenous Input (ARX-model) from which a Depth of Anaesthesia Index (DAI) was calculated. DAI was normalised to 100 while awake and decreasing gradually to a level between 50 and 20 as the rat was anaesthetised. Nine rats were anaesthetised and included in the study. Four doses of Hypnorm vet. and Dormicum were given as a total, each with 5 minutes interval. Clinical signs of the level of anaesthesia were observed simultaneously with the AEP. The results showed that in four rats DAI decreased to a level below 30 while anaesthetised. In the remaining five rats the AEP was only decreased to a level below 45. The results indicated that a simple dosing regimen based on weight was unable to give the same depth of anaesthesia in individual rats. The decrease in the DAI correlated well with the loss of stimulus response. In conclusion, MLAEP could be used as an indicator of depth of anaesthesia in rats during Hypnorm vet. and Dormicum administration. However studies applying other anaesthetic drugs should be carried out, before a conclusion of the general utility of the method can be made.

On-line analysis of AEP and EEG for monitoring depth of anaesthesia.

Achieving and monitoring adequate depth of anaesthesia is a challenge to the anaesthetist. With the introduction of muscle relaxing agents, the traditional signs of awareness are often obscured or difficult to interpret. These signs include blood pressure, heart rate, pupil size, etc. However, these factors do not describe the depth of anaesthesia, (DA), in a cerebral activity sense, hence there is a desire to achieve a better measure of the DA. Auditory Evoked Potentials (AEP) provide two aspects relevant to anaesthesia: (1) they have identifiable anatomical significance and, (2) their characteristics reflect the way in which the brain reacts to a stimulus. However, AEP is embedded in noise from the ongoing EEG background activity. Hence, processing is needed to improve the signal to noise ratio. The methods applied were moving time averaging (MTA) and ARX-modeling. The EEG was collected from the left hemisphere and analysed by FFT to 1 sec epochs and the spectral edge frequency was calculated. Both the changes in ARX extracted AEP and the spectral edge frequency of the EEG correlated well with the time interval between propofol induction and onset of anaesthesia measured by clinical signs (i.e., cessation of eye-lash reflex). The MTA extracted AEP was significantly slower in tracing the transition from consciousness to unconsciousness.

Spectral analysis of cyclic fluctuations in haemodynamic parameters in critically ill patients.

In critically ill patients haemodynamic parameters are being routinely monitored. All of the fluctuations in blood pressures cannot be visualised since on most monitors the time window is too short and trend curves do not have a sufficient time resolution. Therefore, frequency analysis was applied to an 800-second window. Systemic artery pressure, central venous pressure and pulmonary artery pressure curves of 6 patients were sampled with a frequency of 40 Hz. The signals were transformed into the frequency domain by the Fast Fourier Transform method. Bispectral analysis was applied to determine the origin of higher frequencies. There were three main frequencies present: heart stroke rate, respiratory frequency and a slow frequency (< 0.05 Hz), which was equal to the used infusion rate (2-10 ml/h) of vaso-active drugs. Continuous infusion of short-acting vaso-active drugs delivered by pulsatile diaphragm pumps to produce slow significant fluctuations in especially the arterial blood pressures (range: 5-40 mmHg). The periodicity of these slow fluctuations is not visualised during routine monitoring, so the observer may misinterpret the cause of changes in blood pressure and make inappropriate clinical decisions. A solution for detection of such slow waves is Fast Fourier Transform combined with bispectral analysis.