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.