Abstract
New methods of data processing combined with advances in computer technology have revolutionized monitoring of patients under anesthesia. The development of systems based on analysis of brain electrical activity (EEG or evoked potentials) by neural networks has provided impetus to many investigators. Though not claiming to be the end-all in patient monitoring, the potential and efficiency of the combination does indeed stand out. Various strategies are presented and discussed, as well as suggestions for further investigation.
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REFERENCES
White PF, Boyle WA. Relationship between hemodynamic and electroencephalographic changesd uring general anesthesia. Anesth Analg 1989; 68: 177–181
Belani KG, Sessler DI, Larson MD, Lopez MA, Washington DE, Ozaki M, McGuire J, Merrifield B, Schroeder M. The papillary light reflex: Effects of anesthetics and hyperthermia. Anesthesiology 1993; 79: 23–27
Ausems ME, Hugg CC Jr, Stanski DR, Burn AG. Plasma concentrations of alfentanil required to supplement nitrous oxide anesthesia for general surgery. Anesthesiology 1986; 65: 362–373
Drummond JC. Monitoring depth of anesthesia. Anesthesiology 2000; 93 (3): 876–882
Johansen JW, Sebel PS. Development and clinical application of electroencephalographic bipectrum monitoring. Anesthesiology 2000; 93 (5): 1336–1344
Bannister CF, Brosius KK, Sigl JC, Meyer BJ, Sebel PS. The e¡ect of bispectral index monitoring on anesthetic use and recovery in children anesthetized with sevoflurane in nitrousox ide. Anesth Analg 2001; 92: 877–881
Rampil I. A primer for EEG signal processing in anesthesia. Anesthesiology 1998; 89: 980–1002
O'Connor MF, Daves SM, Tung A, Cook RI, Thisted R, Apfelbaum J. BIS monitoring to prevent awareness during general anesthesia. Anesthesiology 2001; 94 (3): 520–522
Hertz, J, Kroch, A, Palmer, RG. Introduction to the theory of neural computation. Addison & Wesley Publishing Company, eds. New York, Singapore, Tokyo, Milan, Paris, 1991
Robert C, Guilpin C, Limoge A. Review of neural network applications in sleep research. J Neurosci Methods 1998; 79: 187–193
Fung KSM, Chan FMY, Lam FK, Liu JG, Poon PWF. Visual evoked potential enhancement by artificial neural network filter. Biomed Mat & Eng 1996; 6: 1–13
Jervis B W. The application of neural networks to interpret evoked potential waveforms. In: Artificial neural networks in biomedicine, Lisboa PJG et al., eds. 2000: 195–210
Gabor AJ, Seyal M. Automated interictal EEG spike detection using artificial neural networks. Electroencephalogr Clin Neurophysiol 1992; 83: 271–280
Gevins AS, Stone RK, Ragsdale SD. Differentiating the e¡ectsof three benzodiazepineso n non-REM sleep EEG spectra. Neuropsychobiol 1988; 19: 108–115
Veselis RA, Reinsel R, Sommer S, Carlon G. Use of Robert et al.: Monitoring anesthesia using neural networks: A survey neural network analysis to classify electroencephalographic patterns against depth of midazolam sedation in intensive care unit patients. J Clin Monit 1991; 7 (3): 259–267
Veselis RA, Reinsel R, Wronski M. Analytical methods to di¡erentiate similar electroencephalographic spectra: Neural network and discriminant analysis. J Clin Monit 1993; 9: 257–267
Attikiouzel Y, de Silva CJS. Applicationso f neural networksi n medicine. Australian Physical & Engineering Sciencesi n Medicine 1995; 18 (3): 158–164
Sabbatini RME. Using neural networks for processing biologic signals. Comput Brazil 1996; 13 (2): 165–172
Shuter ML, Hines EL, Williams H, Preece A. Monitoring patient awareness states via neural network interpretation of EEG signals during anaesthesia trials. Proc Int Conf Neural Networks & Expert Syst Med Health Care, Plymouth, U.K., 23–26 Aug; Ifeachor EC, Rosen KG, eds. 1994: 197–203
Krkic C, Roberts SJ, Rezek I, Jordan C. EEG-based assessment of anaesthetic depth using neural networks. Proc IEE Colloquium Artif Intel Methods Biosignal Analysis; April 1996; 100 (10): 1–6
Muthuswamy J, Roy R, Sharma A. A study of electroencephalographic descriptors and end-tidal concentration in estimating depth of anesthesia. J Clin Monit 1996; 12: 353–364
Sharma A, Roy RJ. Design of a recognition system to predict movement during anesthesia. IEEE Trans Biomed Eng 1997; 44 (6): 505–511
Lu YY, Huang JW, Roy RJ. Estimation of depth of anesthesia using the midlatency auditory evoked potentials by means of neural network based multiple classifier system. Proc 19th Ann Int Conf IEEE EngMed Biol Soc Oct 30–Nov 2, Chicago, Il, U.S.A.; 1997, 19, p. 5.1.1–e
Eckert O, Werry C, Neulinger A, Pichlmayr I. Intraoperative EEG-monitoring-A neural network approach. Biomed Technik 1997; 42: 78–84
Nayak A, Roy RJ. Anesthesia control using midlatency auditory evoked potentials. IEEE Trans Biomed Eng 1998; 45 (4): 409–421
Muthuswamy J, Roy RJ. The use of fuzzy integrals and bispectral analysis of the electroencephalogram to predict movement under anesthesia. IEEE Trans Biomed Eng 1999; 46 (3): 291–299
Nahm W, Stockmanns G, Petersen J, Gehrings H, Konecny E, Kochs HD, Kochs E. Concept for an intelligent anaesthesia EEG monitor. Med Inform 1999; 24 (1): 1–9
Huang JW, LU YY, Nayak A, Roy RJ. Depth of anesthesia estimation and control. IEEE Trans Biomed Eng 1999; 46 (1): 71–81
Zhang XS, Roy RJ. Predicting movement during anesthesia by complexity analysis of electroencephalograms. Med Biol Eng & Comput 1999; 37: 327–334
Kangas LJ, Keller PE. Neurometric assessment of adequacy of intraoperative anaesthetic. In Lisboa PJG et al., eds. Artificial neural networks in biomedicine. 2000: 81–91
Zhang XS, Roy RJ. Derived fuzzy knowledge model for estimating the depth of anesthesia. IEEE Trans Biomed Eng 2001; 48 (3): 312–323
Zhang XS, Roy R, Schwender D, Daunderer M. Discrimination of anesthetic states using Mid-Latency auditory evoked potential and artificial neural networks. Ann Biomed Eng 2001; 29: 446–453
Allen R, Smith D. Neuro-fuzzy closed-loop control of depth of anesthesia. Artif Intel Med 2001; 21: 185–191
Mac Pherson. Neuroanesthesia and intraoperative neurologicalmonitoring. In:Niedermeyer E, Lopez Da Silva F, eds. Electroencephalography, basic principles, clinical applications, and related fields. Fourth edition.Williams & Wilkins Publisher, 1999: 1092–1106
Kunkel H. On some hypotheses underlaying pharmacoelectroencephalography. In: Herrmann ed. EEG in drug research. Stuttgart-New-York: Gustave Fischer, 1982: 1–16
Thornton C, Sharpe RM. Evoked responses in anaesthesia. Br J Anaesth 1998; 81 (5): 771–781
Stockmanns G, Kochs E, Nahm W, Thornton C, Kalkman CJ. Automatic analysis of auditory evoked potentialsby means of wavelet analysis. In: Jordan C, Vaughan DJA, Newton DEF, eds. Memory and awareness in anesthesia. Cambridge Imperial College Press, 2000: 117–131
Kochs E, Stockmanns G, Thornton C, Nham W, Kalkman CJ. Wavelet analysis of middle latency auditory evoked responses. Anesthesiology 2001; 95: 1141–1150
Sebel PS, Bowles SM, Saini V, Chamoun N. EEG bispectrum predictsmove ment during thiopental/isoflurane anaesthesia. J Clin Monit 1995; 11: 83–91
Langford RM, Thomsen CE. The value to the anaesthetist of monitoring cerebral activity. Meth Inform Med 1994; 33: 133–138
Leistritz L, Jager H, Schelenz C, Witte H, Putsche P, Specht, Reinhart K. New approaches for the detection and analysis of electroencephalographic burst-suppression patterns in patients under sedation. J Clin Monit Comput 1999; 15: 357–367
Lopez da Silva F. EEG analysis: Theory and practice. In: Niedermeyer E, Lopez Da Silva F, eds. Electroencephalography, basic principles, clinical applications, and related fields, Fourth edition. Williams & Wilkins Publisher, 1999: 1135–1163
Heinrich H, Moll GH, Dickhaus H, Kolev V, Yordanova J, Rothenberger A. Time-on-task analysis using wavelet networks in an event-related potential study on attention-deficit hyperactivity disorder. Clin Neurophysiol 2001; 112: 1280–1287
Petrossian AA, Prokhorov D, Lajara-Nanson W, Schi¡er RB. Recurrent neural network-based approach for early recognition of Alzheimer's disease in EEG. Clin Neurophysiol 2001; 112: 13780–1387
Anderer P, Saletu B, Kloppel B, Semlitsch HV, Werner H. Discrimination between demented patients and normals based on topographic EEG slow wave activity: Comparison between z statistics, discriminant analysis and artificial neural network classifiers. Electroenceph Clin Neurophysiol 1994; 91: 108–117
Ozdamar O, Kalayaci T. Detection of spikes with arti¢-cial neural networks using raw EEG. Comput & Biomed Res 1998; 31: 122–142
Kissin I. General Anesthetic action: An obsolete notion? Anesth Analg 1993; 76: 215–218
Webber WRS, Litt B, Wilson K, Lesser RP. Practical detection of epileptiform discharges, eds. in the EEG using an artificial neural network: A comparison of raw and parameterized EEG data. Electroencephalograph Clin Neurophysiol 1994; 91: 194–204
Carpenter GA, Grossberg S. Adaptive Resonance Theory (ART). In: Arbib MA, ed. The handbook of brain theory and neural networks. MIT Press, Cambridge, MA, 1995: 79–82
Ozdamar O, Lopez CN, Yaylali I. Detection of transient EEG patterns with adaptive unsupervised neural networks. Proc Int Biomed Eng Days, Istanbul, Turkey. 1992: 192–197
Papadourakis G, Vourkas M, Micheloyannis S, Jervis B. Use of artificial neural networks for clinical diagnosis. Math & Comput Simulation 1996; 40: 623–635
Hart A, Wyatt J. Evaluating black-boxes as medical decision aids: Issues arising from a study of neural networks. Med Inform 1990; 15 (3): 229–236
Glass PSA. Why and how we will monitor the state of anesthesia in 2010? Acta Anaesth Belg 1999; 50 (1): 35–44
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Robert, C., Karasinski, P., Arreto, C.D. et al. Monitoring anesthesia using neural networks: A survey. J Clin Monit Comput 17, 259–267 (2002). https://doi.org/10.1023/A:1020783324797
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DOI: https://doi.org/10.1023/A:1020783324797