Nonlinear regression for sub-peak detection of intracranial pressure signals

The management of many neurological disorders such as traumatic brain injuries relies on the continuous measurement of intracranial pressure (ICP). Following recent studies, the automatic analysis of ICP pulse seems to be a promising tool for forecasting intracranial and cerebrovascular pathophysiological changes. MOCAIP algorithm has recently been developed to automatically extract ICP morphological features in real time. The algorithm is capable of enhancing ICP signal quality, recognizing legitimate ICP pulses, and designating the three peaks in an ICP pulse. This paper extends MOCAIP by using a regression model instead of Gaussian priors during the peak designation to improve the accuracy of the process. The experimental evaluations of the proposed algorithm are performed on a ICP signal database built from 700 hours of recordings from 66 neurosurgical patients. They indicate that the use of a regression model significantly increases the peak designation accuracy.