Abstract
Following recent studies, the automatic analysis of intracranial pressure pulses (ICP) seems to be a promising tool for forecasting critical intracranial and cerebrovascular pathophysiological variations during the management of many neurological disorders. MOCAIP algorithm has recently been developed to automatically extract ICP morphological features. The algorithm is able to enhance the quality of ICP signals, to segment ICP pulses, and to recognize the three peaks occurring in a ICP pulse. This paper extends MOCAIP by introducing a generic framework to perform robust peak recognition. The method is local in the sense that it exploits subwindows that are randomly extracted from ICP pulses. The recognition process combines recently developed machine learning algorithms. The experimental evaluations are performed on a database built from several hundreds of hours of ICP recordings. They indicate that the proposed extension increases the robustness of the peak recognition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hu, X., Xu, P., Scalzo, F., Miller, C., Vespa, P., Bergsneider, M.: Morphological Clustering and Analysis of Continuous Intracranial Pressure. IEEE Transactions on Biomedical Engineering (submitted, 2008)
Takizawa, H., Gabra-Sanders, T., Miller, J.D.: Changes in the cerebrospinal fluid pulse wave spectrum associated with raised intracranial pressure. Neurosurgery 20(3), 355–361 (1987)
Cardoso, E.R., Rowan, J.O., Galbraith, S.: Analysis of the cerebrospinal fluid pulse wave in intracranial pressure. J. Neurosurg. 59(5), 817–821 (1983)
Scalzo, F., Xu, P., Bergsneider, M., Hu, X.: Nonlinear regression for sub-peak detection of intracranial pressure signals. In: IEEE Int. Conf. Engineering and Biology Society (EMBC) (2008)
Hu, X., Xu, P., Lee, D., Vespa, P., Bergsneider, M.: An algorithm of extracting intracranial pressure latency relative to electrocardiogram r wave. Physiological Measurement (2008)
Afonso, V.X., Tompkins, W.J., Nguyen, T.Q., Luo, S.: Ecg beat detection using filter banks. IEEE Trans. Biomed. Eng. 46(2), 192–202 (1999)
Kaufman, L., Rousseeuw, P.J.: Finding groups in data: an introduction to cluster analysis. Wiley series in probability and mathematical statistics. Wiley, Hoboken (2005); Leonard Kaufman, Peter J. Rousseeuw
Mare, R., Geurts, P., Piater, J., Wehenkel, L.: Random subwindows for robust image classification. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 1, pp. 34–40 (2005)
Silverman, B.W.: Density Estimation for Statistics and Data Analysis. Chapman & Hall/CRC, Boca Raton (1986)
Geurts, P., Ernst, D., Wehenkel, L.: Extremely randomized trees. Mach. Learn. 63(1), 3–42 (2006)
Knerr, S., Personnaz, L., Dreyfus, G.: A stepwise procedure for building and training a neural network. Springer, Heidelberg (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Scalzo, F., Xu, P., Bergsneider, M., Hu, X. (2008). Random Subwindows for Robust Peak Recognition in Intracranial Pressure Signals. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89639-5_36
Download citation
DOI: https://doi.org/10.1007/978-3-540-89639-5_36
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-89638-8
Online ISBN: 978-3-540-89639-5
eBook Packages: Computer ScienceComputer Science (R0)