Classification of fetal movement accelerometry through time-frequency features

Layeghy, Siamak, Azemi, Ghasem, Colditz, Paul and Boashash, Boualem (2014). Classification of fetal movement accelerometry through time-frequency features. In: Tadeusz A. Wysocki and Beata J. Wysocki, 8th International Conference on Signal Processing and Communication Systems (ICSPCS), 2014. International Conference on Signal Processing and Communication Systems (ICSPCS), Gold Coast, QLD, Australia, (). 15-17 December 2014. doi:10.1109/ICSPCS.2014.7021055

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Author Layeghy, Siamak
Azemi, Ghasem
Colditz, Paul
Boashash, Boualem
Title of paper Classification of fetal movement accelerometry through time-frequency features
Conference name International Conference on Signal Processing and Communication Systems (ICSPCS)
Conference location Gold Coast, QLD, Australia
Conference dates 15-17 December 2014
Proceedings title 8th International Conference on Signal Processing and Communication Systems (ICSPCS), 2014
Journal name 2014, 8th International Conference on Signal Processing and Communication Systems, ICSPCS 2014 - Proceedings
Place of Publication Piscataway, NJ, United States
Publisher IEEE
Publication Year 2014
Sub-type Fully published paper
DOI 10.1109/ICSPCS.2014.7021055
Open Access Status
ISBN 9781479952557
Editor Tadeusz A. Wysocki
Beata J. Wysocki
Total pages 6
Collection year 2015
Language eng
Abstract/Summary This paper presents a time-frequency approach for fetal movement monitoring which is based on classification of accelerometry signals collected from pregnant women's abdomen. Features extracted from time-frequency distribution of these signals were supplied into statistical analysis to generate feature-measure mixtures. Four various classes subjectively are recognized in accelerometry data by means of objective tools such as ultrasound sonography. These include strong and weak fetal movement, artefact, and background. Receiver operating characteristic analysis utilized to compute the performance of feature-measures for the comparison between various classes. Next, a feature selection applied to reduce the feature space dimension by means of principal component analysis. The selected feature-measures then employed in support vector machine classifiers to classify artefact and fetal movement in different subsets of available classes. The results indicate the fetal movement events are identified with an accuracy of 92.19%.
Keyword Fetal movement
Feature extraction
Classification
Accelerometry
Time frequency analysis
Q-Index Code E1
Q-Index Status Confirmed Code
Institutional Status UQ

 
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Created: Fri, 06 Feb 2015, 12:03:33 EST by Siamak Layeghy on behalf of School of Medicine