H. Litvan

Detrended Fluctuation Analysis of EEG as a Measure of Depth of Anesthesia

For several decades, a number of methods have been developed for the noninvasive assessment of the level of consciousness during general anesthesia. In this paper, detrended fluctuation analysis is used to study the scaling behavior of the electroencephalogram as a measure of the level of consciousness. Three indexes are proposed in order to characterize the patient state. Statistical analysis demonstrates that they allow significant discrimination between the awake, sedated and anesthetized states. Two of them present a good correlation with established indexes of depth of anesthesia. The scaling behavior has been found related to the depth of anesthesia and the methodology allows real-time implementation, which enables its application in monitoring devices

Cerebral state index during propofol anesthesia : A Comparison with the Bispectral Index and the A-Line ARX Index

Background:The objective of this study was to prospectively test the Cerebral State Index designed for measuring the depth of anesthesia. The Cerebral State Index is calculated using a fuzzy logic combination of four subparameters of the electroencephalographic signal. The performance of the Cerebral State Index was compared with that of the Bispectral Index and the A-Line ARX Index. Methods:This study applied raw data from two previously published clinical protocols. The patients in protocol 1 were given a continuous propofol infusion, 300 ml/h, until 80% of burst suppression occurred. In protocol 2, a stepwise increased target-controlled infusion of propofol was administered to patients until loss of response to noxious stimuli while the Observer’s Assessment of Alertness and Sedation was registered every 4 min. The Cerebral State Index was calculated off-line from the recorded electroencephalographic data. The Spearman rank correlation coefficient between electronic indices and the effect site concentration of propofol was calculated along with the prediction probability of each index to predict the Observer’s Assessment of Alertness and Sedation level. Results:The Spearman rank correlation coefficients between the Cerebral State Index, Bispectral Index, and A-Line ARX Index and the propofol effect site concentration were −0.94, −0.89, and −0.82, respectively, in protocol 1, whereas the prediction probability values between the Cerebral State Index, Bispectral Index, and A-Line ARX Index and the Observer’s Assessment of Alertness and Sedation score in protocol 2 were 0.92, 0.93, and 0.91, respectively. Conclusion:The Cerebral State Index detects well the graduated levels of propofol anesthesia when compared with the propofol effect site concentration and the Observer’s Assessment of Alertness and Sedation score.

Comparison of conventional averaged and rapid averaged, autoregressive-based extracted auditory evoked potentials for monitoring the hypnotic level during propofol induction.

Background The extraction of the middle latency auditory evoked potentials (MLAEP) is usually done by moving time averaging (MTA) over many sweeps (often 250–1,000), which could produce a delay of more than 1 min. This problem was addressed by applying an autoregressive model with exogenous input (ARX) that enables extraction of the auditory evoked potentials (AEP) within 15 sweeps. The objective of this study was to show that an AEP could be extracted faster by ARX than by MTA and with the same reliability. Methods The MTA and ARX methods were compared with the Modified Observers Assessment of Alertness and Sedation Scale (MOAAS) in 15 patients scheduled for cardiac surgery and anesthetized with propofol. The peak amplitudes and latencies were recorded continuously for the MTA- and ARX-extracted AEP. An index, AAI, was derived from the ARX-extracted AEP as well. Results The best predictors of the awake and anesthetized states, in terms of the prediction probability, Pk, were the AAI (Pk [SE] = 0.93 [0.01]) and Na-Pa amplitude (MTA, Pk [SE] = 0.89 [0.02]; ARX, Pk [SE] = 0.87[0.02]). When comparing the AAI at the MOAAS levels 5–3 versus 2–0, significant differences were achieved. During the transitions from awake to asleep, the ARX-extracted AEP were obtained with significantly less delay than the MTA-extracted AEP (28.4 s vs. 6 s). Conclusion The authors conclude that the MLAEP peaks and the AAI correlate well to the MOAAS, whether extracted by MTA or ARX, but the ARX method produced a significantly shorter delay than the MTA.

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).

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.

Pk value does depend on the fineness of the observer scale

Sir, We read with interest the article by Kreuer et al. (1) in which the authors used the prediction probability (Pk) defined by Smith et al. (2). Since the Pk was suggested as a method for validation of depth of anesthesia monitors, it has been used extensively, with the reason being that allegedly it can be computed for any degree of coarseness or fineness of the scales being compared. This is also stated by Kreuer et al. (1), who used the Pk to assess how well the AAI and BIS indices predict the desflurane concentrations during deep anesthesia. In most articles, the Pk has been used to validate the electronic indices vs. clinical scores, such as the Ramsay or OAAS scales. In these cases the categorical variable [also termed the ‘observer scale’ by Smith et al. (2)] has approximately 4—6 levels; in which range the Pk is reliable. However, when the Pk is used to validate the ability of the monitors to predict the concentration of anesthetic drugs, the categorical variable has a higher number of levels (in theory there could be as many levels as datapoints). This will cause the Pk to drop considerably as compared to a study where anesthetic concentrations are classified into four or five categories. This is illustrated in the following example: The Cerebral State Index (3) (CSM, Danmeter A/S, Odense, Denmark a depth of anesthesia index ranging from 0 to 100) was registered along with the effect-site concentration of propofol (Ce prop) (recorded at Hospital Santa Creu i Sant Pau, Barcelona, Spain). Four data points were registered in each of 25 patients, giving a total n1⁄4 100. The Pk between cerebral state index (CSI) and Ce prop was calculated using the original pkmacro for Microsoft Excel (MicrosoftCorp., Redmond, USA). Figure 1 shows the CSI plotted against four Ce prop categories. In this case the Pk is 0.84 (in fact, the Pk is 0.16 because Ce is an increasing scale and CSI is a decreasing scale, but for comparison the value is transformed to Pk1⁄4 1-Pk, as performed by Kreuer et al.) (1). If only two levels on the observer scale are defined (0—3mgml 1 as level 1, and 3—7mgml 1 as level 2), then the Pk increases to 1. On the other hand, if 100 categories are used (each exact value of Ce defines a level on the observer scale) the Pk drops to 0.76, changing the performance from a perfect to a mediocre depth of anesthesia monitor (see Table 1).

An AEP/EEG hybrid index for monitoring the hypnotic depth during general anesthesia

The extraction of a consistent and reliable measure online and close to real time to assess the hypnotic level during anesthesia is a continuous challenge to the anesthetist and the biomedical engineer. 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). The objective of this study was to define a hybrid index (HI) derived from the Auditory Evoked Potentials (AEP) and the Electroencephalogram (EEG). The index should reliably differentiate awake and asleep states in a graduated manner. Data was required from twenty patients scheduled for elective cardiac surgery. All patients were anesthetized with propofol and no other drugs were administered during the study period. The HI had a prediction probability Pk(SD) of 0.92(0.01) between awake and anesthetized values, which was significantly larger than the Pks of the individual parameters, AEP and EEG.

Symbolic dynamics applied to EEG signal for monitoring anaesthetic depth during propofol infusion

The extraction of a consistent and reliable measure online and close to real time to assess the hypnotic level during anesthesia is a continuous challenge to the anaesthetist. Haemodynamic 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. The objective of this study was to evaluate the symbolic dynamics applied to the EEG signal while awake and while asleep. Data was required from 10 patients scheduled for elective cardiac surgery. All patients were anesthetized with propofol and no other drugs were administered during the study period. The results showed significant difference between awake and anaesthetized values, hence concluding that the complexity measure might serve as an indicator of anaesthetic depth.