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

Marzieh ABBASINIA; Fardad FAROKHI; Shahram JAVADI

Makale detayları

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

Yazarlar:

Marzieh ABBASINIA | Fardad FAROKHI | Shahram JAVADI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2016 Vol.24 Issue 5, pp.3335-3344

Belge Tipi:

Makale

Yayın Tarihi:

9/2016

Belge Dili:

Türkçe

Document ID (Org No.):

2000741440

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632

Özet:

 

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.

Makale detayları

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

Yazarlar: Marzieh ABBASINIA | Fardad FAROKHI | Shahram JAVADI

Yayıncı: TUBITAK

Dergi: Turkish Journal of Electrical Engineering and Computer Science 2016 Vol.24 Issue 5, pp.3335-3344

Belge Tipi: Makale

Yayın Tarihi: 9/2016

Belge Dili: Türkçe

Document ID (Org No.): 2000741440

Sıklık: İki Aylık

eISSN: 1303-6203

pISSN: 1300-0632

Sayı

Yazarlar:

Marzieh ABBASINIA | Fardad FAROKHI | Shahram JAVADI

Yayıncı:

TUBITAK

Dergi:

Turkish Journal of Electrical Engineering and Computer Science 2016 Vol.24 Issue 5, pp.3335-3344

Belge Tipi:

Makale

Yayın Tarihi:

9/2016

Belge Dili:

Türkçe

Document ID (Org No.):

2000741440

Sıklık:

İki Aylık

eISSN:

1303-6203

pISSN:

1300-0632