000			04555nlm0a2200481 4500
001			655951
005			20231030041408.0
035			_a(RuTPU)RU\TPU\network\22291
035			_aRU\TPU\network\16431
090			_a655951
100			_a20171013d2016 k y0engy50 ba
101	0		_aeng
102			_aGR
105			_ay z 100zy
135			_adrcn ---uucaa
181		0	_ai
182		0	_ab
200	1		_aNeurodynamic non-invasive fetal electrocardiogram extraction _fD. V. Devyatykh, O. M. Gerget
203			_aText _celectronic
300			_aTitle screen
320			_a[References: 3 tit.]
330			_aFetal 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.
333			_aРежим доступа: по договору с организацией-держателем ресурса
463			_tInformation, Intelligence, Systems and Applications (IISA) _oInternational Conference, 13-15 July 2016, Chalkidiki, Greece _v[16544037] _oproceedings _d2016
610	1		_aэлектронный ресурс
610	1		_aтруды учёных ТПУ
610	1		_aresilient propagation
610	1		_adynamic neural network
610	1		_avanishing gradient
610	1		_ablind source separation
610	1		_afetal electrocardiogram
610	1		_aнейронные сети
610	1		_aисчезающие градиенты
610	1		_aэмбриональная электрокардиограмма
700		1	_aDevyatykh _bD. V. _cspecialist in the field of informatics and computer technology _cprogrammer of Tomsk Polytechnic University _f1989- _gDmitry Vladimirovich _2stltpush _3(RuTPU)RU\TPU\pers\37832
701		1	_aGerget _bO. M. _cSpecialist in the field of informatics and computer technology _cAssociate Professor of Tomsk Polytechnic University, Candidate of technical sciences _f1974- _gOlga Mikhailovna _2stltpush _3(RuTPU)RU\TPU\pers\31430
712	0	2	_aНациональный исследовательский Томский политехнический университет (ТПУ) _bИнститут кибернетики (ИК) _bКафедра прикладной математики (ПМ) _h130 _2stltpush _3(RuTPU)RU\TPU\col\18700
712	0	2	_aНациональный исследовательский Томский политехнический университет (ТПУ) _bИнститут кибернетики (ИК) _bКафедра программной инженерии (ПИ) _h7751 _2stltpush _3(RuTPU)RU\TPU\col\22918
712	0	2	_aНациональный исследовательский Томский политехнический университет (ТПУ) _bИнститут неразрушающего контроля (ИНК) _bУчебно-методический отдел (УМО) _h6588 _2stltpush _3(RuTPU)RU\TPU\col\18986
801		1	_aRU _b63413507 _c20141010
801		2	_aRU _b63413507 _c20171013 _gRCR
856	4		_uhttps://doi.org/10.1109/IISA.2016.7785333
942			_cCF