Random Subwindows for Robust Peak Recognition in Intracranial Pressure Signals
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.
KeywordsSupport Vector Machine Near Neighbor Kernel Density Estimation Probability Density Function Average Prediction Error
Unable to display preview. Download preview PDF.
- 1.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)Google Scholar
- 4.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)Google Scholar
- 5.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)Google Scholar
- 7.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. RousseeuwGoogle Scholar
- 8.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)Google Scholar
- 11.Knerr, S., Personnaz, L., Dreyfus, G.: A stepwise procedure for building and training a neural network. Springer, Heidelberg (1990)Google Scholar