The segregation between maternal and fetal heart signals by a convolution deep learning based on image processing
محل انتشار: دومین کنفرانس بین المللی پژوهش های نوین در مهندسی برق، کامپیوتر، مکانیک و مکاترونیک در ایران و جهان اسلام
سال انتشار: 1400
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 374
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شناسه ملی سند علمی:
ICECM02_070
تاریخ نمایه سازی: 29 تیر 1400
چکیده مقاله:
Nowadays, the fetal heart monitoring is an increasingly important issue in medicine, because statistics show that ۱ out of ۱۲۵ neonates are born with congestive heart failure. This paper proposes a deep learning approach based on convolution neural network to separate maternal electrocardiogram signals from fetal without isolating maternal ECG signal. The method put forward in the present research showed how to monitor fetal heart rate signals and separate them from that of mother via the deep neural system. With regard to the marked role of separating fetal heart rate from that of mother in order to determine embryo's abnormalities and finding a treatment for them, it is necessary to take an accurate measurement of fetal heart rate and analyze its morphology. Despite some limitation, like mother's fast heart rate or other available noises, a deep neural system-centric method was tested successfully. By combining signals received from mother and fetal heart rate and without the necessity of extracting features or doing pre-process steps, it could successfully monitor fetal heart rate than other methods. It not only did not feel the need to taking steps of feature extraction, but also it showed high precision. Finally, by comparing this works with earlier ones, its efficiency was conformed.
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نویسندگان
Meysam Rahimi
Department of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran