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Proceedings Paper

Detection of segments with fetal QRS complex from abdominal maternal ECG recordings using support vector machine
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Paper Abstract

This paper introduces a robust method based on the Support Vector Machine (SVM) algorithm to detect the presence of Fetal QRS (fQRS) complexes in electrocardiogram (ECG) recordings provided by the PhysioNet/CinC challenge 2013. ECG signals are first segmented into contiguous frames of 250 ms duration and then labeled in six classes. Fetal segments are tagged according to the position of fQRS complex within each one. Next, segment features extraction and dimensionality reduction are obtained by applying principal component analysis on Haar-wavelet transform. After that, two sub-datasets are generated to separate representative segments from atypical ones. Imbalanced class problem is dealt by applying sampling without replacement on each sub-dataset. Finally, two SVMs are trained and cross-validated using the two balanced sub-datasets separately. Experimental results show that the proposed approach achieves high performance rates in fetal heartbeats detection that reach up to 90.95% of accuracy, 92.16% of sensitivity, 88.51% of specificity, 94.13% of positive predictive value and 84.96% of negative predictive value. A comparative study is also carried out to show the performance of other two machine learning algorithms for fQRS complex estimation, which are K-nearest neighborhood and Bayesian network.

Paper Details

Date Published: 22 December 2015
PDF: 10 pages
Proc. SPIE 9681, 11th International Symposium on Medical Information Processing and Analysis, 96811A (22 December 2015); doi: 10.1117/12.2207830
Show Author Affiliations
Juan A. Delgado, Univ. Simón Bolívar (Venezuela)
Miguel Altuve, Pontifical Bolivarian Univ. (Colombia)
Masun Nabhan Homsi, Univ. Simón Bolívar (Venezuela)

Published in SPIE Proceedings Vol. 9681:
11th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore; Juan D. García-Arteaga; Jorge Brieva, Editor(s)

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