Abstract
The analysis of stereoelectroencephalographic (intracerebral recording) signals provides information on the electrical activity of brain structures implied in epileptic seizures. A simple nonparametric adaptive segmentation method, based on a physiologically relevant parameter, is presented and compared with three methods reported in the literature. The comparative frame allows us to objectively test methods for their performances on the same basis. Results show that the proposed method is robust with respect to the types of change studied and easier to conduct, even if it is less accurate about the estimation of instants of change than another method presented in this study. Signals are segmented throughout the duration of seizures without parameter readjustment and generate instants of change in accordance with those interactively delimited by the clinician.
Similar content being viewed by others
References
Appel, U., and A. Brandt. Adaptive sequential segmentation of piecewise stationary time series.Information Sci. 29:27–56, 1983.
Appel, U., and A. Brandt. A comparative study of three sequential time series segmentation algorithms.Sign. Process. 6:45–60, 1984.
Babloyantz, A., and A. Destesche, Non-linear analysis and modelling of cortical activity. In: Mathematics applied to biology and medicine, edited by J. Demongeot and V. Capasso. 1993, pp. 35–48.
Barlow, J. S. Methods of analysis of non-stationary EEG's, with emphasis on segmentation techniques: a comparative review.J. Clin. Neurophysiol. 2:267–304, 1985.
Basseville, M., and A. Benveniste. Detection of abrupt changes in signals and dynamical systems. In: Lectures notes in control and information sciences, vol. 77, edited by M. Thoma and A. Wyner. New York: Springer-Verlag, 1986.
Basseville, M. Detecting changes in signals and systems—a survey.Automatica 24:309–326, 1988.
Basseville, M., and I. V. Nikiforov. Detection of Abrupt Changes: Theory and Application. Englewood Cliffs, NJ: Prentice-Hall, 1993.
Bland, B. H., P. Andersen, T. Ganes, and O. Sveen. Automated analysis of rhythmicity of physiologically identified hippocampal formation neurons.,Exp. Brain Res. 38:205–219, 1980.
Bodenstein, G., and H. M. Praetorius. Feature extraction of the electroencephalogram by adaptive segmentation.Proc. IEEE 65:642–652, 1977.
Borodkin, L. I., and V. V. Mottl. Algorithm for finding the jump times of random process equation parameter.Automation and Remote-Control 6:23–32, 1976.
Carrault, G., J.J. Bellanger, and J.M. Badier. Segmentation vectorielle de signaux EEG.13ème Colloque Gretsi 2:767–770, 1993.
Fenwick, P.B.C., P. Michie, J. Dollimore, and G.W. Fenton. Mathematical simulation of the electroencephalogram using an autoregressive series.Int. J. Biomed. Comput. 2: 281–307, 1971.
Gath, I., and E. Bar-On. Computerized method of scoring of polygraphic sleep recordings.Comp. Prog. Biomed. 11:217–223, 1980.
Gath, I., and B. Geva. Unsupervised optimal fuzzy clustering.IEEE Trans. Patt. Anal. Mach. Intell. 2:773–781, 1989.
Gersch, W. Spectral analysis of EEGs by autoregressive decomposition of time series.Math. Biosci. 7:205–222, 1970.
Gray, A. H., and J. D. Markel. Distance measures for speech processing.IEEE Trans. Acoustics Speech, Sign. Process. 24:380–391, 1976.
Hinkley, D. V. Inference about the change point from cumulative sum tests.Biometrika 58:509–523, 1971.
Jansen, B. H. Quantitative EEG analysis in renal disease. Clinical applications of computer analysis of EEG and other neurophysiological signals.Handbook EEG Clin. Neurophysiol. 2:239–260, 1987.
Jazaerli, M. S. Contribution à la Décomposition des Algorithmes de Moindres Carrés Vectoriels Normalisés. Application au traitement d'antennes. Rennes, France: Université de Rennes, Ph.D. Thesis, 1987.
Krajca, V., S. Petranek, I. Patakova, and A. Varri. Automatic identification of significant graphoelements in multichannel EEG recordings by adaptive segmentation and fuzzy clustering.Int. J. Biomed. Comp. 28:71–89, 1991.
Lopes da Silva, F.H., A. Dijk, H. Smith, and L. H. Zetterberg. Automatic detection and pattern recognition of epileptic spikes from surface and depth recording in man. In: Die quantifizierung des elektroenzephalogramms, edited by G. K. Schenk, 1973, pp. 425–436.
Lopes Da Silva, F. H., A. Dijk, and H. Smits. Detection of non-stationarities in EEGs using the autoregressive model—an application to EEGS of epileptics. In: CEAN—computerized EEG analysis, edited by Dolce and Kunkel. Stuttgart: Fischer-Verlag, 1975, pp. 180–199.
Lopes Da Silva, F. H., W. Ten Broeke, K. Van Hulten, and J. G. Lommen. EEG non-stationarities detected by inverse filtering in scalp and cortical recordings of epileptics: statistical analysis and spatial displays. In: Quantitative analytic studies in epilepsy. New York, Raven Press, 1976, pp. 375–388.
Lopes Da Silva, F. H., K. Van Hulten, J. G. Lommen, W. Storm Van Leeuwen, C. W. M. Van Veelen, and W. Vliegenthart. Automatic detection of epileptic foci.Electroenceph. Clin. Neurophysiol. 43:1–13, 1977.
Michael, D., and J. Houchin. Automatic EEG analysis: a segmentation procedure based on the autocorrelation function.Electroenceph. Clin. Neurophysiol. 46:232–235, 1979.
Page, E. S. Continuous inspection schemes.Biometrika 41:100–115, 1954.
Pijn, J. P., J. V. Neerven, A. Noest, and F. H. Lopes da Silva. Chaos or noise in EEG signals; dependence on state and brain site.Electroenceph. Clin Neurophysiol. 79:371–381, 1991.
Praetorius, H. M., G. Bodenstein, and O. D. Creutzfeld. Adaptive segmentation of EEG records: a new approach to automatic EEG analysis.Electroenceph. Clin. Neurophysiol. 42:84–94, 1977.
Rappelsberger, P., and H. Petsche. Spectral analysis of the EEG by means of an autoregression. In: CEAN—computerized EEG analysis, edited by Dolce and Kunkel. Stuttgart: Fischer, 1975, pp. 27–40.
Segen, J., and A. C. Sanderson. Detecting changes in a time series.IEEE Trans. Inf. Theory 2:249–255, 1980.
Stoica, P. Performance evaluation of some methods for off-line detection of changes in autoregressive signals.Sign. Process. 19:301–310, 1990.
Talairach, J., and J. Bancaud. Stereotaxic exploration and therapy in epilepsy. In: Handbook of clinical neurology, the epilepsies, vol. 15, edited by Vinken and Brown, Amsterdam: North Holland, 1974, pp. 758–782.
Varri, A. Digital Processing of Epilepsy. Tampere, Finland: University of Technology, Licentiate Thesis, 1988.
Vozel, B. Etude Comparative d'Algorithmes Récursifs de Détection de Ruptures Spectrales. Nantes, France: Université de Nantes, PhD Thesis 1994.
Wendling, F., J.J. Bellanger, J. M. Badier, and J. L. Coatrieux. Extraction of spatio-temporal signatures from depth EEG seizure signals based on objective matching in warped vectorial observations.IEEE Trans. Biomed. Eng. 43:990–1000, 1996.
Wendling, F. Mise en correspondance d'observations EEG de profondeur pour la reconnaissance de signatures spatiotemporelles dans les crises d'épilepsie. Rennes, France: Université de Rennes, Ph.D. Thesis, 1996.
Willsky, A. S., and H. L. Jones. A generalized likelihood ratio approach to the detection and estimation of jumps in linear systems.IEEE Trans. Autom. Cont. 21:108–112, 1976.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Wendling, F., Carrault, G. & Badier, J.M. Segmentation of depth-EEG seizure signals: Method based on a physiological parameter and comparative study. Ann Biomed Eng 25, 1026–1039 (1997). https://doi.org/10.1007/BF02684138
Received:
Revised:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF02684138