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Classification of Parameters Extracted from Cardiotocographic Signals for Early Detection of Metabolic Acidemia in NewbornsROTARIU, C. , COSTIN, H. , PASARICA, A. , NEMESCU, D.
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cardiotocographic signals, fetal heart rate monitoring, metabolic acidemia detection, pattern classification, spectral analysis
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
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.
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 Antepartum Fetal Monitoring and Spectral Analysis of Preterm Birth Risk, Păsăricără, Alexandru, Nemescu, Dragoş, Arotăriţei, Dragoş, Rotariu, Cristian, Journal of Physics: Conference Series, ISSN 1742-6588, Issue , 2017.
Digital Object Identifier: 10.1088/1742-6596/931/1/012009 [CrossRef]
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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania
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