A data wavelets approach to deriving trends in historical ICU monitor data

Medical staff in the Intensive Care Unit (ICU) are confronted with large volumes of continuous noisy data from several physiological sources that require interpretation. Rather than reasoning quantitatively on a point by point basis, especially in the context of other signals, we believe that Medical Staff could benefit with assistance in the interpretation of the ICU data by providing qualitative summaries. We propose data wavelets as an approach to analysing historical ICU data for deriving trends for summarization. In this paper we will show that wavelets are particularly effective for representing various aspects of non-stationary data such as trends, cycles and discontinuities.