Key result
A neuro-fuzzy network using 30 selected hybrid features predicted acute hypotensive episodes with 96.30% accuracy 1.5 hours before onset.
Why the study?
Can a neuro-fuzzy network using hybrid features accurately predict acute hypotensive episodes 1.5 hours before onset?
Can a neuro-fuzzy network using hybrid features accurately predict acute hypotensive episodes 1.5 hours before onset?
A neuro-fuzzy network using hybrid features from vital time series can predict acute hypotensive episodes with high accuracy 1.5 hours prior to onset.
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.
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Abbasinia et al. (2016) studied Acute hypotensive episode. Hybrid feature space and a neuro-fuzzy network was evaluated on Prediction of acute hypotensive episode (AHE). A neuro-fuzzy network using 30 selected hybrid features predicted acute hypotensive episodes with 96.30% accuracy 1.5 hours before onset.
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