Artifact removal for intracranial pressure monitoring signals: A robust solution with signal decomposition

Intracranial Pressure (ICP) monitoring signal collected in Neuro Intensive Care Units often contains large amount of artifacts. The artifacts not only directly lead to false alarms in automatic Intracranial Hypertension (IH) alert systems, and they also severely contaminate the characteristics of the underlying signal, which makes accurate forecasting of impending IH impossible. Therefore, in this paper, we propose a novel solution to effectively remove artifacts from ICP monitoring signals. The proposed method effectively detects artifacts by decomposing the ICP monitoring signal with Empirical Mode Decomposition (EMD) method. An iterative filtering method is also proposed to extract artifacts from the decomposed components of ICP signals. The proposed filter is robust. That is, the parameters of the iterative filter are estimated with robust statistics, which ensures the performance of the proposed filter will not be unduly affected by artifacts. The detected artifacts are then imputed based on the Auto-Regressive Moving Average (ARMA) model to preserve the original characteristics of the ICP signal. The effectiveness of the proposed artifact removal method is experimentally justified based on the ICP monitoring signals of 59 patients.