Blind Separation of Abdominal Electrocardiogram Sources through Dynamic Neural Network; Advances in Computer Science Research; Vol. 51 : Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016)

Blind Separation of Abdominal Electrocardiogram Sources through Dynamic Neural Network; Advances in Computer Science Research; Vol. 51 : Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016)

Dades bibliogràfiques
Parent link:Advances in Computer Science Research
Vol. 51 : Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016).— 2016.— [P. 14-15]
Autor principal: Devyatykh D. V. Dmitry Vladimirovich
Autor corporatiu: Национальный исследовательский Томский политехнический университет (ТПУ) Управление проректора по научной работе и инновациям (НРиИ) Центр RASA в Томске Лаборатория дизайна медицинских изделий (Лаб. ДМИ)
Altres autors: Gerget O. M. Olga Mikhailovna
Sumari:Title screen
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.
Idioma:anglès
Publicat: 2016
Matèries:
электронный ресурс
труды учёных ТПУ
нейронные сети
градиенты
ЭКГ
Accés en línia:http://dx.doi.org/10.2991/itsmssm-16.2016.67
Format: Electrònic Capítol de llibre
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=652838

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