Blind Separation of Abdominal Electrocardiogram Sources through Dynamic Neural Network

DOI

10.2991/itsmssm-16.2016.67How to use a DOI?

Keywords

dynamic neural network, vanishing gradient, blind source separation, fetal electrocardiogram, resilient propagation

Abstract

Cardiovascular system of the fetus is biological critical infrastructure. Fetal electrocardiogram and its characteristics such as heart ratio, waveform and dynamic behavior overall include vital information about health state, development and possible deviations from normal fetation. Thus fetal heart ratio monitoring is mandatory during pregnancy. Widespread Doppler ultrasound diagnostics can guarantee obtaining accurate results but is not suitable for long-term monitoring. Non-invasive fetal electrocardiography proposes to extract fetal signal from maternal abdominal electrocardiogram. This approach is applicable for long-term monitoring, but because of amplitude of maternal R-peaks is significantly larger than fetal it is a challenge to extract fetal signal. In this paper we propose using dynamic neural networks for extracting fetal components and demonstrate its advantages compared to blind source separation though independent component analysis. The training algorithm is a combination of backpropagation through time and resilient propagation. The proposed approach accuracy of R-peak detection is 97%. Statistical analysis proved that developed algorithm can process even non-stationary signals with loss of accuracy and no additional training is required.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)

TY  - CONF
AU  - Dmitriy Devyatykh
AU  - Olga Gerget
PY  - 2016/05
DA  - 2016/05
TI  - Blind Separation of Abdominal Electrocardiogram Sources through Dynamic Neural Network
BT  - Proceedings of the 2016 Conference on Information Technologies in Science, Management, Social Sphere and Medicine
PB  - Atlantis Press
SP  - 330
EP  - 335
SN  - 2352-538X
UR  - https://doi.org/10.2991/itsmssm-16.2016.67
DO  - 10.2991/itsmssm-16.2016.67
ID  - Devyatykh2016/05
ER  -