Classification of Fetal Heart Rate using Scale Dependent Features and Support Vector Machines
| Authors: | Georgoulas George, Laboratory for Automation and Robotics, University of Patras, Greece
Stylios Chrysostomos, Dept. of Communications, Informatics and Management, TEI of Epirus,, Greece
Groumpos Peter, Laboratory for Automation and Robotics, University of Patras, Greece |
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| Topic: | 8.2 Modelling & Control of Biomedical Systems |
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| Session: | Biomedical Engineering / Biomedical Signal Processing I |
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| Keywords: | Discrete Wavelet Transform, Support Vector Machines, Fetal Heart Rate. |
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Abstract
One new approach for the problem of feature extraction and classification of Fetal Heart Rate signal is introduced in this paper. It considers the use of the Discrete Wavelet Transformation to extract scale-dependent features of Fetal Heart Rate (FHR) signal and the use of Support Vector Machines for classification of FHR. The proposed methodology is tested on real data acquired just before delivery. The results proved the viability of the approach and its potential for further application by achieving an overall classification performance of 90%.
