Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records.
In this study, we proposed an approach able to predict whether a pregnant woman with contractions would give birth earlier than expected (i.e., before the 37th week of gestation (WG)). It only processes non-invasive electrohysterographic (EHG) signals fully automatically without assistance of an exp...
| Publicado en: | Journal of Medical Systems Vol. 41; no. 12; pp. 1 - 14 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2017
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134757450&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134757450 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Dec2017 vid: 41 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134757450 10.1007/s10916-017-0847-8 134757450 ppf: 1 ppct: 13 formats: fmt: @attributes: type: P tig: atl: Relevant Features Selection for Automatic Prediction of Preterm Deliveries from Pregnancy ElectroHysterograhic (EHG) records. aug: au: Sadi-Ahmed, Nafissa Kacha, Baya Taleb, Hamza Kedir-Talha, Malika affil: Faculty of electronics and informatics, University of Sciences and Technology Houari Boumediene (USTHB), PB 32 El Alia, Bab Ezzouar, Algiers, 16111, Algeria. sug: ab: In this study, we proposed an approach able to predict whether a pregnant woman with contractions would give birth earlier than expected (i.e., before the 37th week of gestation (WG)). It only processes non-invasive electrohysterographic (EHG) signals fully automatically without assistance of an expert or an additional medical system. We used term and preterm EHG signals of 30-minutes duration collected between the 27th and the 32nd WG. Preterm deliveries (<37W G) had occurred in average 4.00 ± 1.88 weeks since recording dates. Each recording contains three bipolar channels. Using the Huang-Hilbert transform (HHT), we obtained up to twelve intrinsic mode functions (IMFs) for each signal. We found that the most relevant IMFs for both term and preterm records were IMF3 and IMF6. From these two IMFs, we extracted 8 most relevant features targeting EHG signal specificities. We investigated features classifications using support vector machine (SVM) for the 3 single-channels and for all their possible combinations. High discrimination power between term and preterm EHG records was obtained with linear-SVM classifiers. For almost all the cases, mean areas under curves (AUC) exceeded 0.92. A two-channel combination (7 features) achieved the best mean results with Accuracy = 95.70%, Sensitivity = 98.40%, Specificity = 93.00% and AUC = 0.95. Results of the three-channel combination (9 features) were Accuracy = 92.30%, Sensitivity = 93.00%, Specificity = 91.60% and AUC = 0.96. The best single-channel (8 features) gave the mean values: Accuracy = 90.40%, Sensitivity = 93.60% and AUC = 0.94. Thus, the advantage of our approach is the high diagnostic performance at low computational cost. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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