Predicting acute hypotensive episode by using hybrid features and a neuro-fuzzy network

Authors : Marzieh ABBASINIA, Fardad FAROKHI, Shahram JAVADI

Pages : 3335-3344

View : 13 | Download : 10

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :This paper presents an approach for acute hypotensive episode insert ignore into journalissuearticles values(AHE); time series forecasting based on hybrid feature space and a neuro-fuzzy network. Prediction was accomplished through a combination of time domain and wavelet features by using six vital time series of each patient, obtained from MIMIC-II and available in the context of the Physionet-Computers in Cardiology 2009 Challenge. At first, statistical time domain features were used and then the wavelet coefficient was utilized for extracting time scale features. Further UTA feature selection was applied and 30 effective features were determined and achieved to predict AHE with 96.30 accuracy 1.5 h before AHE onset.
Keywords : Acute hypotensive episode AHE, prediction, neuro fuzzy network NF, wavelet transform, feature selection, mean arterial blood pressure insert ignore into journalis