TY - GEN
T1 - Neurodynamic non-invasive fetal electrocardiogram extraction
AU - Devyatykh, Dmitriy
AU - Gerget, Olga
N1 - Publisher Copyright:
© 2016 IEEE.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016/12/14
Y1 - 2016/12/14
N2 - Fetal electrocardiography in contrary to adult is not that well represented in publications, yet circulatory system of the fetus is probably the most valuable and crucial biological infrastructure. Fetal heart ratio, form of QRS-wave and dynamics of cardiovascular system activity allow estimating fetus state, maturity, possibilities of heart abnormality occasion. This information can be received with guaranteed accuracy through Doppler-ultrasound procedure, however duration of such kind of monitoring is limited. Fetal electrocardiogram is an obvious source of information about fetal heart activity. However, because of low signal-to-noise ratio and prevailing of maternal component, non-invasive ways of acquiring this signal do not guarantee absolute accuracy. Problems of non-invasive electrocardiography demand complex mathematical approaches because maternal and fetal R-peaks overlap in time and frequency domains and have similar morphological structure of heart waves. In this paper we propose approach for extracting fetal electrocardiography from abdominal signal, which is based on dynamic neural network. The common problem for both dynamic and deep learning is caused by linearity of backpropagation and thus vanishing or exploding of gradients occurs. We proposed resilient propagation through time approach that unites training based on sign of derivative and parallel unfolding. We compared developed algorithm with blind source separation through independent component analysis and noted several important advantages that our model delivers - accuracy does not depend on: length of signal; amount of independent channels.
AB - Fetal electrocardiography in contrary to adult is not that well represented in publications, yet circulatory system of the fetus is probably the most valuable and crucial biological infrastructure. Fetal heart ratio, form of QRS-wave and dynamics of cardiovascular system activity allow estimating fetus state, maturity, possibilities of heart abnormality occasion. This information can be received with guaranteed accuracy through Doppler-ultrasound procedure, however duration of such kind of monitoring is limited. Fetal electrocardiogram is an obvious source of information about fetal heart activity. However, because of low signal-to-noise ratio and prevailing of maternal component, non-invasive ways of acquiring this signal do not guarantee absolute accuracy. Problems of non-invasive electrocardiography demand complex mathematical approaches because maternal and fetal R-peaks overlap in time and frequency domains and have similar morphological structure of heart waves. In this paper we propose approach for extracting fetal electrocardiography from abdominal signal, which is based on dynamic neural network. The common problem for both dynamic and deep learning is caused by linearity of backpropagation and thus vanishing or exploding of gradients occurs. We proposed resilient propagation through time approach that unites training based on sign of derivative and parallel unfolding. We compared developed algorithm with blind source separation through independent component analysis and noted several important advantages that our model delivers - accuracy does not depend on: length of signal; amount of independent channels.
KW - blind source separation
KW - dynamic neural network
KW - fetal electrocardiogram
KW - resilient propagation
KW - vanishing gradient
UR - http://www.scopus.com/inward/record.url?scp=85013157825&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85013157825&partnerID=8YFLogxK
U2 - 10.1109/IISA.2016.7785333
DO - 10.1109/IISA.2016.7785333
M3 - Conference contribution
AN - SCOPUS:85013157825
T3 - IISA 2016 - 7th International Conference on Information, Intelligence, Systems and Applications
BT - IISA 2016 - 7th International Conference on Information, Intelligence, Systems and Applications
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on Information, Intelligence, Systems and Applications, IISA 2016
Y2 - 13 July 2016 through 15 July 2016
ER -