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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
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Next issue: Nov 2018
Avg review time: 83 days


PUBLISHER

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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2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

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  3/2015 - 23
View TOC | « Previous Article | Next Article »

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
 
Click to see author's profile in See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (1,256 KB) | Citation | Downloads: 292 | Views: 1,391

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.


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[1] 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
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Copyright ©2001-2018
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

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