Empirical Mode Decomposition-Based Method for Artefact Removal in Raw Intracranial Pressure Signals

Abstract

Intracranial pressure (ICP) signals are often contaminated by artefacts and segments of missing values. Some of these artefacts can be observed as very high and short spikes with a physiologically impossible high slope. The presence of these spikes reduces the accuracy of pattern recognition techniques. Thus, we propose a modified empirical mode decomposition (EMD) method for spike removal in raw ICP signals. The EMD breaks down the signal into 16 intrinsic mode functions (IMFs), combines the first 4 to localize spikes using adaptive thresholding, and then either removes or imputes the identified ICP spikes.