Detection of Acute Hypotensive Episodes via Empirical Mode Decomposition and Genetic Programming

Big data time series in the Intensive Care Unit (ICU) is now touted as a solution to help clinicians to diagnose the case of the physiological disorder and select proper treatment based on this diagnosis. Acute Hypotensive Episodes (AHE) is one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. This study presented a methodology to predict AHE for ICU patients based on big data time series. Empirical Mode Decomposition (EMD) was used to calculate patient´s Mean Arterial Pressure (MAP) time series and some features, which are bandwidth of the amplitude modulation, frequency modulation and power of Intrinsic Mode Function (IMF) were extracted. Then, the Genetic Programming (GP) is used to build the classification model for detection of AHE. The methodology was applied in the datasets of the 10th Physio Net and Computers Cardiology Challenge in 2009 and Multi-parameter Intelligent Monitoring for Intensive Care (MIMIC-II). We achieve the accuracy of 83.33% in the training set and 91.89% in the testing set of the 2009 challenge´s dataset, and the 83.37% in the training set and 80.64% in the testing set of the MIMIC-II dataset.