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A computer-aided approach to detect the fetal behavioral states using multi-sensor Magnetocardiographic recordings.
Vairavan, S; Ulusar, U D; Eswaran, H; Preissl, H; Wilson, J D; Mckelvey, S S; Lowery, C L; Govindan, R B.
Affiliation
  • Vairavan S; Graduate Institute of Technology, University of Arkansas at Little Rock, AR, USA.
  • Ulusar UD; Computer Engineering Department, Akdeniz University, Antalya, Turkey.
  • Eswaran H; Department of Obstetrics and Gynecology, University of Arkansas for Medical Sciences, AR, USA; Division of Biomedical Informatics, University of Arkansas for Medical Sciences, AR, USA.
  • Preissl H; Division of Biomedical Informatics, University of Arkansas for Medical Sciences, AR, USA; MEG Center, University of Tubingen, Tubingen, Germany.
  • Wilson JD; Graduate Institute of Technology, University of Arkansas at Little Rock, AR, USA.
  • Mckelvey SS; Department of Obstetrics and Gynecology, University of Arkansas for Medical Sciences, AR, USA.
  • Lowery CL; Department of Obstetrics and Gynecology, University of Arkansas for Medical Sciences, AR, USA.
  • Govindan RB; Division of Fetal and Transitional Medicine, Fetal Medicine Institute, Children׳s National Health System, 111 Michigan Ave, NW Washington, DC 20010, USA. Electronic address: rgovinda@childrensnational.org.
Comput Biol Med ; 69: 44-51, 2016 Feb 01.
Article in En | MEDLINE | ID: mdl-26717240
ABSTRACT
We propose a novel computational approach to automatically identify the fetal heart rate patterns (fHRPs), which are reflective of sleep/awake states. By combining these patterns with presence or absence of movements, a fetal behavioral state (fBS) was determined. The expert scores were used as the gold standard and objective thresholds for the detection procedure were obtained using Receiver Operating Characteristics (ROC) analysis. To assess the performance, intraclass correlation was computed between the proposed approach and the mutually agreed expert scores. The detected fHRPs were then associated to their corresponding fBS based on the fetal movement obtained from fetal magnetocardiogaphic (fMCG) signals. This approach may aid clinicians in objectively assessing the fBS and monitoring fetal wellbeing.
Subject(s)
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Pregnancy Trimester, Third / Signal Processing, Computer-Assisted / Pregnancy / Magnetocardiography / Fetus Type of study: Prognostic_studies Limits: Female / Humans Language: En Journal: Comput Biol Med Year: 2016 Document type: Article Affiliation country: Estados Unidos

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Pregnancy Trimester, Third / Signal Processing, Computer-Assisted / Pregnancy / Magnetocardiography / Fetus Type of study: Prognostic_studies Limits: Female / Humans Language: En Journal: Comput Biol Med Year: 2016 Document type: Article Affiliation country: Estados Unidos
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