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

被引:0
作者
Abbasinia, Marzieh [1 ]
Farokhi, Fardad [1 ]
Javadi, Shahram [1 ]
机构
[1] Islamic Azad Univ, Dept Elect Engn, Cent Tehran Branch, Tehran, Iran
关键词
Acute hypotensive episode (AHE); prediction; neuro-fuzzy network (NF); wavelet transform; feature selection; mean arterial blood pressure (MAP);
D O I
10.3906/elk-1403-117
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents an approach for acute hypotensive episode (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.
引用
收藏
页码:3335 / 3344
页数:10
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