Abstract
Acute hypotensive episodes (AHEs) are one of the hemodynamic instabilities with high mortality rate that is frequent among many groups of patients. This study presents a methodology to predict AHE for ICU patients based on big data time series. The experimental data we used is mean arterial pressure (MAP), which is transformed from arterial blood pressure (ABP) data. Then, the Hilbert-Huang transform method was used to calculate patient's MAP time series and some features, which are the bandwidth of the amplitude modulation, the frequency modulation, and the power of intrinsic mode function (IMF), were extracted. Finally, the multiple genetic programming (Multi-GP) is used to build the classification models for detection of AHE. The methodology is applied in the datasets of the 10th PhysioNet and Computers Cardiology Challenge in 2009 and Multiparameter 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 84.13% in the training set and 82.41% in the testing set of the MIMIC-II dataset.
Highlights
The acute hypotensive episodes (AHEs) are defined for an hour at any time of 30 minutes or more during which at least 90% of the mean arterial pressure (MAP) signal measurements are at or below 60 mmHg
The research of predicting AHE can be categorized into two types, which are only arterial blood pressure (ABP) or MAP signal analysis and ABP with other physiological information analyses
The amplitude modulation bandwidth (AMB) and frequency modulation bandwidth (FMB) in D are the features of the high frequency components of the intrinsic mode function (IMF), and power of the last IMF is the feature of the low frequency components
Summary
The acute hypotensive episodes (AHEs) are defined for an hour at any time of 30 minutes or more during which at least 90% of the MAP signal measurements are at or below 60 mmHg. The research of predicting AHE can be categorized into two types, which are only ABP or MAP signal analysis and ABP with other physiological information analyses. Saeed introduced a temporal similarity metric, which applied a wavelet decomposition to characterize time series dynamics at multiple time scales to utilize classical information retrieval algorithms based on a vector-space model. This algorithm was used to identify similar physiologic patterns in hemodynamic time series from ICU patients by the detection of imminent hemodynamic deterioration [2].
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