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Pregled bibliografske jedinice broj: 902355

Relevance of Empirical Mode Decomposition for Fetal Heartbeat Detection on Smartphone Devices


Vican, Ivan; Kreković, Gordan
Relevance of Empirical Mode Decomposition for Fetal Heartbeat Detection on Smartphone Devices // Proceedings of the 25th European Signal Processing Conference (EUSIPCO)
Kos, Grčka, 2017. str. 455-459 (poster, međunarodna recenzija, neobjavljeni rad, znanstveni)


CROSBI ID: 902355 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Relevance of Empirical Mode Decomposition for Fetal Heartbeat Detection on Smartphone Devices

Autori
Vican, Ivan ; Kreković, Gordan

Vrsta, podvrsta i kategorija rada
Sažeci sa skupova, neobjavljeni rad, znanstveni

Izvornik
Proceedings of the 25th European Signal Processing Conference (EUSIPCO) / - , 2017, 455-459

ISBN
978-0-9928626-8-8

Skup
European Signal Processing Conference (25 ; 2017))

Mjesto i datum
Kos, Grčka, 28.08.2017. - 09.09.2017

Vrsta sudjelovanja
Poster

Vrsta recenzije
Međunarodna recenzija

Ključne riječi
phonocardiography ; fetal heartbeat ; feature extraction ; feature ranking ; feature selection ; machine learning ; prenatal care

Sažetak
Fetal phonocardiography is a re-emerging method for extracting fetal heartbeat signals with a strong potential to be used as an easily accessible system in prenatal monitoring, especially if employed in conjunction with widespread electronic hardware. Since smartphone devices are going through rapid development of their processing power, sensory capabilities and network connectivity, they are becoming a powerful yet underutilized biomedical tool. Within this study we propose novel features for automatic fetal heartbeat detection based on intrinsic mode functions (IMF) gained through empirical mode decomposition. In order to show that more accurate detection can be achieved with IMF- based features added to the conventional set of audio features, we assessed feature relevance and usefulness using ranking and selection techniques. The results suggest that IMF-based features are relevant for the classification task and can improve prediction accuracy by 3.28%.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo



POVEZANOST RADA



Citiraj ovu publikaciju:

Vican, Ivan; Kreković, Gordan
Relevance of Empirical Mode Decomposition for Fetal Heartbeat Detection on Smartphone Devices // Proceedings of the 25th European Signal Processing Conference (EUSIPCO)
Kos, Grčka, 2017. str. 455-459 (poster, međunarodna recenzija, neobjavljeni rad, znanstveni)
Vican, I. & Kreković, G. (2017) Relevance of Empirical Mode Decomposition for Fetal Heartbeat Detection on Smartphone Devices. U: Proceedings of the 25th European Signal Processing Conference (EUSIPCO).
@article{article, author = {Vican, Ivan and Krekovi\'{c}, Gordan}, year = {2017}, pages = {455-459}, keywords = {phonocardiography, fetal heartbeat, feature extraction, feature ranking, feature selection, machine learning, prenatal care}, isbn = {978-0-9928626-8-8}, title = {Relevance of Empirical Mode Decomposition for Fetal Heartbeat Detection on Smartphone Devices}, keyword = {phonocardiography, fetal heartbeat, feature extraction, feature ranking, feature selection, machine learning, prenatal care}, publisherplace = {Kos, Gr\v{c}ka} }
@article{article, author = {Vican, Ivan and Krekovi\'{c}, Gordan}, year = {2017}, pages = {455-459}, keywords = {phonocardiography, fetal heartbeat, feature extraction, feature ranking, feature selection, machine learning, prenatal care}, isbn = {978-0-9928626-8-8}, title = {Relevance of Empirical Mode Decomposition for Fetal Heartbeat Detection on Smartphone Devices}, keyword = {phonocardiography, fetal heartbeat, feature extraction, feature ranking, feature selection, machine learning, prenatal care}, publisherplace = {Kos, Gr\v{c}ka} }




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