Entropia espectral: um novo método para adequação anestésica

Rogean Rodrigues Nunes; Murilo Pereira de Almeida; James Wallace Sleigh

doi:10.1590/S0034-70942004000300013

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Entropia espectral: um novo método para adequação anestésica

Spectral entropy: a new method for anesthetic adequacy

Rogean Rodrigues Nunes; Murilo Pereira de Almeida; James Wallace Sleigh

http://dx.doi.org/10.1590/S0034-70942004000300013 Braz J Anesthesiol, vol.54, n3, p.404-422, 2004

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Resumo

JUSTIFICATIVA E OBJETIVOS: O uso de sinais clínicos para avaliar a adequação da anestesia, embora empregado universalmente, não são confiáveis. Vários equipamentos surgiram objetivando o melhor manuseio intra-operatório das drogas anestésicas, alguns deles mensurando diretamente a atividade cortical cerebral (hipnose). Entretanto, nenhum deles apresenta características diretas de avaliação da atividade sub-cortical (resposta motora). CONTEÚDO: A entropia espectral mensura a irregularidade, complexidade ou a quantidade de desordem do eletroencefalograma e tem sido sugerida como um indicador do estado anestésico. O sinal é coletado na região fronto-temporal e tratado através da equação de Shannon (H = - Sp k log p k, onde p k são as probabilidades de um evento discreto k), resultando em dois tipos de análises: 1. Entropia de estado (SE), que consiste na avaliação da atividade elétrica cortical cerebral (0,8-32Hz) e 2. Entropia de resposta (RE), que analisa as freqüências de 0,8 - 47Hz (contêm componentes eletroencefalográficos-cortical e eletromiográficos-sub-cortical). CONCLUSÕES: A ativação da musculatura frontal pode indicar inadequação do componente sub-cortical (nocicepção). Esta ativação é observada como um "gap" entre SE e RE. Deste modo, é possível avaliar diretamente tanto o componente cortical (SE), como o sub-cortical (RE), possibilitando melhor adequação dos componentes anestésicos.

Palavras-chave

MONITORIZAÇÃO, MONITORIZAÇÃO, MONITORIZAÇÃO

Abstract

BACKGROUND AND OBJECTIVES: Though universally employed, clinical signs to evaluate anesthetic adequacy are not reliable. Over the past years several pieces of equipment have been devised to improve intraoperative handling of anesthetic drugs, some of them directly measuring cerebral cortical activity (hypnosis). None of them, however, has offered the possibility of directly evaluating sub-cortical activity (motor response). CONTENTS: Spectral entropy measures irregularity, complexity or amount of EEG disorders and has been proposed as indicator of anesthetic depth. Signal is collected from the fronto-temporal region and processed according to Shannon's equation (H = - Sp k log p k, where p k represents the probability of a discrete k event), resulting in two types of analyses: 1) state entropy (SE), which evaluates cerebral cortex electrical activity (0.8 - 32Hz) and 2) response entropy (RE), containing both subcortical electromyographic and cortical electroence- phalographic components and analyzes frequencies in the range 0.8 - 47Hz. CONCLUSIONS: Frontal muscles activation may indicate inadequacy of the subcortical component (nociception). Such activation appears as a gap between SE and RE. This, it is possible to directly evaluate both cortical (SE) and subcortical (RE) components providing better anesthetic adequacy.

Keywords

MONITORING, MONITORING, MONITORING

References

Rampil IJ. A primer for EEG signal processing in anesthesia. Anesthesiology. 1998;89:980-1002.

Bruhn J, Bouillon TW, Radulescu L. Correlation of approximate entropy, bispectral index, and spectral edge frequency 95 (SEF95) with clinical signs of anesthetic depth during coadministration of propofol and remifentanil. Anesthesiology. 2003;98:621-627.

Muncaster AR, Sleigh JW, Williams M. Changes in consciousness, conceptual memory, and quantitative electroencephalographical measures during recovery from sevoflurane and remifentanil-based anesthesia. Anesth Analg. 2003;96:720-725.

Yli-Hankala AVakkun A, Sandin R. EEG entropy monitoring decreases propofol consumption and shortens early recovery times. Eur J Anaesthesiol. 2003;20:A-98.

Shannon CE. A mathematical theory of communication. Bell Syst Tech J. 1948;27:379-423, 623-656.

Huang K. Statistical Mechanics. 1987:127-142.

Stein-Ross ML. Theoretical electroencephalogram stationary spectrum for a white-noise-driven cortex: evidence for a general anesthetic-induced phase transition. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 1999;60:7299-7311.

Quiroga RQ, Arnhold J, Lehnertz K. Kulback-Leibler and renormalized entropies: applications to electroencephalograms of epilepsy patients. Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics. 2000;62:8380-8386.

Johnson RW, Shore JE. Which is the better entropy expression for speech processing: -S logS or logS?. Acoust Speech Signal Proc. 1984;ASSP-32:129-137.

Weiner JH. Statistical Mechanics of Elasticity. 1983:120-125.

Pincus S, Gladstone I. A regularity statistic for medical data analysis. J Clin Monit. 1991;7:335-345.

Bruhn J, Ropcke H, Hoeft A. Approximate entropy as an electroencephalographic measure of anesthetic drug effect during desflurane anesthesia. Anesthesiology. 2000;92:715-726.

Bruhn J, Lehmann LE, Ropcke H. Shannon entropy applied to the measurement of the electroencephalographic effects of desflurane. Anesthesiology. 2001;95:30-35.

John ER, Easton P, Isenhart R. Consciousness and cognition may be mediated by multiple independent coherent ensembles. Conscious Cogn. 1997;6:3-39.

Stam CJ, Tavy DL, Keunen RW. Quantification of alpha rhythm desynchronization using the acceleration spectrum entropy of the EEG. Clin Electroencephalogr. 1993;24:104-109.

Thomeer EC, Stam CJ, van Woerkom TC. EEG changes during mental activation. Clin Electroencephalogr. 1994;25:94-98.

Weiss V. The relationship between short-term memory capacity and EEG power spectral density. Biol Cybern. 1992;68:165-172.

Inoye K. Quantification of EEG irregularity by use of the entropy of the power spectrum. Electroencephalog Clin Neurophysiol. 1991;79:204-210.

Fell J, Roschke J, Mann K. Discrimination of sleep stages: a comparison between spectral and nonlinear EEG measures. Electroencephalogr Clin Neurophysiol. 1996;98:401-410.

Haykin S, Van Veen B. Sinais e Sistemas. 2001:163-258.

Lader MH, Mathews AM. Electromyographic studies of tension. J Psychosom Res. 1971;15:479-486.

Rosen L, Lunn JN. Consciousness and Awareness and Pain in General Anaesthesia. 1987:89-98.

Dement W, Kleitman N. The relation of eye movements during sleep to dream activity: an objective method for the study of dreaming. J Exp Psychol. 1957;53:339-346.

Edmonds Jr HL, Triantafillou T, Tsueda I. Comparison of frontalis and hypothenar EMG responses to vecuronium. Anesthesiology. 1985;63:A324.

Edmonds Jr HL, Couture LJ, Stolzy SL. Quantitative surface electromyography in anesthesia and critical care. Int J Clin Monit Comput. 1986;3:135-145.

Edmonds Jr HL, Paloheimo M. Computerized monitoring of the EMG and EEG during anesthesia: an evaluation of the anesthesia and brain activity monitor. Int J Clin Monit Comput. 1985;1:201-210.

Watt RC, Hameroff SR, Cork RC. Spontaneous EMG monitoring for anesthetic depth assessment. Proceeding of the Association of Advanced Medical Instrumentation. 1985;20:92.

Mathews DM, Kumaran KR, Neuman GG. Bispectral index-derived facial electromyography-guided fentanyl tritation in the opiate-exposed patient. Anesth Analg. 2003;94:1062-1064.

Kern SE, David PJ, Dezaire BS. Assessing the facial EMG as an indicator of response to noxious stimuli in anesthetized volunteers. ASA Meeting Abstrscts. 1999:A594.

Shander A, Qin F, Bennett H. Prediction of postoperative analgesic requirements by facial alectromyography during simultaneous BIS monitoring. Eur J Anaesthesiol. 2001;18(Suppl 21):130.

Lennon RL, Hosking MP, Daube JR. Effect of partial neuromuscular blockade on intraoperative electromyography in patients undergoing resection of acoustic neuromas. Anesth Analg. 1992;75:729-733.

Dutton RC, Smith WD, Bennett HL. Craniofacial electromyogram activation response: another indicator of anesthetic depth. J Clin Monit Comput. 1998;14:5-17.

Särkelä M, Mustola S, Seppänen T. Automatic analysis and monitoring of burst suppression in anesthesia. J Clin Monit Comput. 2002;17:125-134.

Entropia espectral: um novo método para adequação anestésica

Spectral entropy: a new method for anesthetic adequacy

Rogean Rodrigues Nunes; Murilo Pereira de Almeida; James Wallace Sleigh

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