Artifact correction with robust statistics for non-stationary intracranial pressure signal monitoring

To enhance ICP monitoring of Traumatic Brain Injury (TBI) patients, much research effort has been attracted to the development auto-alarming systems and forecasting methods to predict impending intracranial hypertension episodes. Nevertheless, the performance of the proposed methods are often limited by the presence of artifacts in the ICP signal. To address this bottleneck, we propose novel artifact correction methods. A scale-based filter is proposed to identify the artifacts. For the proposed filter, instead of classic statistics, robust statistics is employed to estimate the scale parameter. Thus, our proposed methods are robust against undesirable influences from artifacts. Since the ICP signal is non-stationary, non-stationary signal processing techniques, the empirical mode decomposition (EMD), wavelet transformation and median filter, are also employed. The effectiveness of proposed methods are evaluated experimentally. Experimental results demonstrate that, with the proposed artifact correction methods, significant performance gains can be achieved.