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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
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: 735 | Views: 2,608

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

[1] A. C. Gjerris, J. Staer-Jensen, J. S. Jorgensen, T. Bergholt, C. Nickelsen, "Umbilical cord blood lactate: A valuable tool in the assessement of fetal blood acidosis," European Journal of Obstretics & Gynecology and Reproductive Biology, vol. 139, Issue 1, pp. 16-20, Jan. 2008.
[CrossRef] [Web of Science Times Cited 52]


[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 11]


[3] E. Chandraharan, "Fetal scalp blood sampling during labor: is it a useful diagnostic test or a historical test that no longer has a place in modern clinical obstretics?," BJOG: An international Journal of Obstreticts & Gynaecology, vol. 121, Issue 9, pp. 1056-1062, Aug. 2014.
[CrossRef] [Web of Science Times Cited 43]


[4] J. Y.Kwon, I. Y. Park, J.C. Shin, J. Song, R. Tafreshi, J. Lim, "Specific changes in spectral power of fetal heart rate variability related to fetal acidemia during labor: Comparison between preterm and term fetuses," Early Human Development, vol. 88, Issue 4, pp. 203-207, April 2012.
[CrossRef] [Web of Science Times Cited 21]


[5] M. P. Nageotte, "Featl heart rate monitoring," Seminars in Fetal & Neonatal Medicine, vol. 20, pp. 1-5, Mar. 2015.
[CrossRef] [Web of Science Times Cited 47]


[6] A. Indrayan, "Medical Biostatistics, Third Edition", Chapman & Hall/CRC Press, USA, pp. 280-283, 2012

[7] A. Costa, D. Ayres-de-Campos, F. Costa, C. Santos, J. Bernardes "Prediction of neonatal acidemia by computer analysis of fetal heart rate and ST event signals", American Journal of Obstetrics and Gynecology, vol. 201, pp. 464-452, Nov. 2009.
[CrossRef] [Web of Science Times Cited 56]


[8] C. Elliott, P. Warrick, E. Graham, E. Hamilton, "Graded classification of fetal heart rate tracings: association with neonatal metabolic acidosis and neurologic morbidity," American Journal of Obstetrics and Gynecology, vol. 202, no. 3, pp. 258.e1-258.e8, Mar. 2010.
[CrossRef] [Web of Science Times Cited 70]


[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.
[CrossRef] [Web of Science Times Cited 100]


[11] A. Georgieva, S. J. Payne, M. Moulden, C. W. G. Redman. "Artificial neural networks applied to fetal monitoring in labour,". Neural Computing and Applications, vol. 22, pp. :85-93, Jan. 2013.
[CrossRef] [Web of Science Times Cited 57]


[12] V. Chudácek, J. Spilka, M. Burša, et al. , "Open access intrapartum CTG database", BMC Pregnancy and Childbirth, pp. 14:16, Jan. 2014.
[CrossRef] [Web of Science Times Cited 159]


[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 60]


[14] P. A. Warrick, E.Ff Hamilton, D. Precup, R. Kearney, "Classification of normal and hypoxic fetuses from systems modeling of intrapartum cardiotocography," IEEE Transactions on Biomedical Engineering, vol. 57, Issue 4, pp. 771-779, April 2010.
[CrossRef] [Web of Science Times Cited 84]


[15] E. M. Graatsma, "Monitoring of Fetal Heart Rate and Uterine Activity", Ridderprint BV, Amsterdam, Holland, pp. 39-55, 2010

[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.
[CrossRef] [Web of Science Times Cited 12]


[17] U. Schneider, E. Schleussner, A. Friedler, S. Jaekel, .M. Liehr, J. Haueisen, D. Hoyer, "Fetal heart rate variability reveals defferential dynamics in the intrauterine development of the sympathetic and parasympathetic branches of the autonomic nervous system," Physiologcal Measurements, vol. 30, no. 2, pp. 215-226, Jan. 2009.
[CrossRef] [Web of Science Times Cited 64]


[18] V. Munteanu, D. Tarniceriu, "Estimation theory and optimal filtering," Ed. Technopress, Iasi, Romania, pp. 306-310, 2005

[19] V. Maier, S. G. Pavel, C. D. Maier, I. Birou, "Correct Application of the Discrete Fourier Transform in Harmonics," Advances in Electrical and Computer Engineering, vol. 8, no. 1, pp. 26-30, 2008,
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[20] M. Jezewski, R. Czabanski, J. Wrobel, K. Horoba, "Analysis of extracted cardiotocographic signal features to improve automated prediction of fetal outcome", Biocybernetics and Biomedical Cardiology, vol. 30, no.4, pp. 29-47, Feb. 2010.

[21] A. G. Cahill, K. A. Roehl, A. O. Odibo, G. A. Macones, "Association and prediction of neonatal acidemia," American Journal of Obstretics and Gynecology, vol. 207, Issue 3, pp. 206.e1-206.e8, Sept. 2012.
[CrossRef] [Web of Science Times Cited 36]


[22] Y. Hatakeyama, H. Kataoka, N. Nakajima, T. Watabe, Y. Okuhara, "Level evaluation system for cardiotocography," 15th International Symposium on Soft Computing and Intelligence Systems, pp. 265-269, Dec. 2014.
[CrossRef]


[23] L. Jimenez, R. Gonzalez, M. J. Gaitan, S. Carrasco, C. Vargas, "Computerized algorithm for baseline estimation of fetal heart rate," Computers in Cardiology, vol. 29, pp. 477-480, Sept. 2002.
[CrossRef] [Web of Science Times Cited 16]


[24] H. J. Seltman, "Experimental design and analysis," Carnegie Melon University, Chapter 6, pp. 141-161, Nov. 2014



References Weight

Web of Science® Citations for all references: 897 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 36 ACR
SCOPUS® Average Citations per reference: 0

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 2024-03-20 04:41 in 112 seconds.




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