2001, Number 2
<< Back Next >>
Rev Mex Ing Biomed 2001; 22 (2)
Improve procedures in neurophysiological monitoring
Gutiérrez J
Language: Spanish
References: 17
Page: 67-77
PDF size: 343.34 Kb.
ABSTRACT
In recent years, technological advances have allowed to improve procedures in neurological field. Functional monitoring research has grown considerably because of the new hardware and software possibilities towards on line computerization in the operating room. The main approach to improve neuro surgical and anaesthetic procedures is neurophysiological monitoring, research in this field is mainly concerned with electroencephalogram (EEG) analysis. Recording the EEG signal is particularly helpful to measure the functioning of the central nervous system and assess anaesthetic depth during surgical procedures, or to prevent neurological damage in endovascular embolization or to detect epileptic foci whether a patient with intractable epilepsy is undergoing a surgical resection. Today, there exist some effective signal processing algorithms applying in neurophysiological signals. We can mentioned spectral analysis, correlation methods, temporal characteristics, Time-Frequency Distributions, topographic brain mapping by interpolation techniques, or multiresolution analysis using wavelet transform. The main subject in this report is reviews some of the most powerful and effective algorithms used in the signal processing to get on line electroencephalogram signal analysis.
REFERENCES
Nuwer MR. Neuromonitoring during surgery. Report of an IFCN comité. Electroenceph Clin Neurophysiol. 1993, 87: 263-276.
Jasper HH. The ten-twenty electrode system of the international Federation. Electroencephalogr Clin Neurophysiol. 1958, 10: 371-375.
Adams R, Victor M. Principles of Neurology. McGraw-Hill, 1993.
Velde MV, Cluitmans PJM. EEG Analisis for Monitoring of Anesthetic Depth. Eindhoven University of Technology, Faculty of Electrical Engineering 1991, Ed. Eindhoven.
Bickford RG, Berger L. Automation of clinical electroencephalography. Raven Press, New York, 1979: 55-64.
Nuwer MR, Quantitative EEG. Techniques and Problems of Frequency Analysis and Topographic Mapping. J Clinical Neurophy 1988; 5: 1-41.
Perrin F, Bertrand O, Pernier J. Scalp current density mapping: value and estimation from potential data. IEEE Trans Biomed Eng. 1987; 34: 283-288.
Duffy FH. Topographic display of potentials: clinical applications of brain electrical activity mapping. Ann NY Acad Sci 1982; 388: 183-196.
Gutiérrez J, Igartua L, Medina V, Raquel V. Advantages of 3D over 2D Brain Mapping in the detection of Central Nervous System Tumors. 17TH Annual International Conference IEEE Engineering in Medicine and Biology Society. Septiembre 1995; CD-IEEE 2.1.6.3.
Wyler A, Richey E, Atkinson R, Hermann B. Strip Electrodes in Acute Electrocorticography. J Epilepsy 1988; 1: 95-97.
Alcántara R, Gutiérrez J, Alvarez L, Igartua L. Métodos Paramétricos y Tiempo Frecuencia en la Representación del Electroencefalograma. Memorias Primer Congreso Latinoamericano de Ingeniería Biomédica. 1998: CD-PDS31, 159-162.
Cohen L. Time Frequency Analysis. Prentice Hall PTR, New Jersey, 1995: 93-110.
Zaveri H, Williams W y cols. Time-Frequency Representation of Electrocorticograms in Temporal Lobe Epilepsy. IEEE Transactions of Biomedical Engineering, 1992; 38(5): 502-508.
Choi H, Williams W. Improved Time-Frequency Representation of Multicomponent Signals Using Exponential Kernels. IEEE Transactions on Acoustics, Speech and Signal Processing. 1989; 37(6): 862-871.
Akay M. Wavelets in Biomedical Engineering. Annals of Biomedical Engineering 1995; 23: 531-542.
Daubechies I. Ten Lectures on Wavelets. Society for Industrial and Applied Mathematics, Philadelphia PA. 1992: 357.
Vetterli M, Kovacevic J. Wavelets and Subband Coding. Prentice Hall PTR, 1996.