Assessment of the depth of anesthesia based on symbolic dynamics of the EEG.

Methodologies based on symbolic dynamics have successfully demonstrated to reflect the nonlinear behavior of biological signals. In the present study, symbolic dynamics was applied to the electroencephalogram (EEG) in order to describe the level of depth of anesthesia. The EEG was transformed to symbol sequences. Words of three symbols were built from this symbolic series. The results obtained from the EEGs of 36 patients undergoing anesthesia showed that the probabilities of the word types were able to reflect the depth of anesthesia in a similar way to the auditory evoked potential index AAI, a commercial index.

A new method for evaluating the performance of depth-of-hypnosis indices - the D-value.

An alternative statistic, the D-value, is presented for the evaluation of the performance of EEG-based depth-of-hypnosis measures against the Observers' Assessment of Alertness/Sedation scale. The measures considered here are spectral entropy, approximate entropy, Lempel-Ziv complexity and Higuchi fractal dimension. The study is based on recordings from 45 patients, divided into three groups of 15 recordings each. Patients of Group I received no remifentanil while patients of Groups II and III received 2 and 4 ng/ml effect compartment controlled remifentanil. All the patients received stepwise increased dose of propofol. The study shows that the D-value is a promising and flexible statistic for the evaluation of the discriminative power of the EEG measures with respect to the OAA/S scale. The D-value indicates well the dependence of the performance of the measures on the EEG frequency band as well as on remifentanil concentration.

Depth of anesthesia index using cumulative power spectrum.

Over the last ten years, monitors of depth of anesthesia have progressively been integrated in the clinical practice. Based on the analysis of the electroencephalogram (EEG), these monitors deliver an index that helps the anesthesiologist to determine the state of the patient during the surgery. Although they employ different kind of algorithms, spectral parameters are always taken into account to achieve the final indexes. In this work, a new spectral parameter based on the cumulative power spectrum is proposed. When compared to the Spectral Edge Frequency (SEF), a classic spectral parameter, the Cumulative Power Spectrum Index (CPSI) presents a higher correlation with reference indexes (AAI, BIS and CePROP) and a higher prediction probability of the state of the patient. Furthermore, when compared to the reference indexes, the CPSI shows similar performances in terms of correlation and presents a higher prediction probability than two of them (BIS and CePROP).