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University of Suceava
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Print ISSN: 1582-7445
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WorldCat: 643243560
doi: 10.4316/AECE


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  3/2015 - 23
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Classification of Parameters Extracted from Cardiotocographic Signals for Early Detection of Metabolic Acidemia in Newborns

ROTARIU, C. See more information about ROTARIU, C. on SCOPUS See more information about ROTARIU, C. on IEEExplore See more information about ROTARIU, C. on Web of Science, COSTIN, H. See more information about  COSTIN, H. on SCOPUS See more information about  COSTIN, H. on SCOPUS See more information about COSTIN, H. on Web of Science, PASARICA, A. See more information about  PASARICA, A. on SCOPUS See more information about  PASARICA, A. on SCOPUS See more information about PASARICA, A. on Web of Science, NEMESCU, D. See more information about NEMESCU, D. on SCOPUS See more information about NEMESCU, D. on SCOPUS See more information about NEMESCU, D. on Web of Science
 
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Download PDF pdficon (1,256 KB) | Citation | Downloads: 555 | Views: 2,113

Author keywords
cardiotocographic signals, fetal heart rate monitoring, metabolic acidemia detection, pattern classification, spectral analysis

References keywords
fetal(16), rate(10), heart(10), neonatal(6), analysis(5), prediction(4), obstretics(4), monitoring(4), gynecology(4)
No common words between the references section and the paper title.

About this article
Date of Publication: 2015-08-31
Volume 15, Issue 3, Year 2015, On page(s): 161 - 166
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2015.03023
Web of Science Accession Number: 000360171500023
SCOPUS ID: 84951088832

Abstract
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Fetal acidosis is reflected by the values of umbilical cord pH and base deficit (BDecf): normal recordings (pH over 7.2 and BDecf under 8 mmol/l) and abnormal recordings (pH under 7.2 and BDecf over 8 mmol/l). The purpose of this paper is to present the implementation of an automated system for detecting fetal acidosis in cardiotocographic recordings. The method uses spectral analysis of medium (0.07-0.13 Hz) and high (0.13-1 Hz) frequency spectrum. We implement the algorithm for segments of the recordings without signal loss for better classification. We determined the normalized medium and high frequency components and mid to high frequency ratio. The recordings in the database are divided into a control group (100 normal recordings) and a test group (431 normal or abnormal recordings). A t-test with the p value under 0.05 between the two groups is used to classify the test group. The classification is improved by including the presence of late and prolonged decelerations in the classification process, obtaining the final results, which are comparable to the best ones in current literature.


References | Cited By  «-- Click to see who has cited this paper

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[CrossRef] [Web of Science Times Cited 48] [SCOPUS Times Cited 61]


[2] E. Soncini, S. Paganelli, C. Vezzani, G. Gargano, G. Battista, "Inatrapartum fetal heart rate monitoring: evaluation of standardized system of interpretation for prediction of metabolic acidosis at delivery and neonatal neurological morbidity," The Journal of Maternal-Fetal & Neonatal Medicine, vol. 27, no. 14, pp. 1465-1469, Sept. 2014.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 13]


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[CrossRef] [Web of Science Times Cited 35] [SCOPUS Times Cited 34]


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[CrossRef] [Web of Science Times Cited 20] [SCOPUS Times Cited 19]


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[CrossRef] [Web of Science Times Cited 34] [SCOPUS Times Cited 39]


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[CrossRef] [Web of Science Times Cited 65] [SCOPUS Times Cited 61]


[9] S. Siira, "Intrapartum hypoxia and power spectral analysis of fetal heart rate variability," Uniprint Suomen Yliopistopaino Oy - Oulu, Finland, pp. 33-42, 2012

[10] J. Spilka, V. Chudacek, M. Koucky, M. Huptych, P. Janku, G. Georgoulas, C. Stylios, "Using nonlinear features for fetal heart rate classification," Biomedical Signal Processing and Control, vol. 7, Issue 4, pp. 350-357, July 2012.
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[CrossRef] [Web of Science Times Cited 46] [SCOPUS Times Cited 52]


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[CrossRef] [Web of Science Times Cited 121] [SCOPUS Times Cited 137]


[13] G. S. Dawes, M. Lobb, M. Moulden, C.W. Redman, T. Wheeler, "Antenatal cardiotocogram quality and interpretation using computers," BJOG: An International Journal of Obstretics & Gynaecology, vol. 99, Issue 10, pp. 791-797, Aug. 2005.
[CrossRef] [Web of Science Times Cited 55] [SCOPUS Times Cited 59]


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[CrossRef] [Web of Science Times Cited 76] [SCOPUS Times Cited 93]


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[16] C-Y. Chen, C. Yu, C-C. Chang, C-W. Lin, "Comparison of a Novel Computerized Analysis Program and Visual Interpretation of Cardiotocography," PLoS ONE, vol. 9, Issue 12, Dec. 2014.
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[CrossRef] [Web of Science Times Cited 58] [SCOPUS Times Cited 63]


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References Weight

Web of Science® Citations for all references: 761 TCR
SCOPUS® Citations for all references: 893 TCR

Web of Science® Average Citations per reference: 30 ACR
SCOPUS® Average Citations per reference: 36 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-06-28 20:44 in 108 seconds.




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