Pregled bibliografske jedinice broj: 902355
Relevance of Empirical Mode Decomposition for Fetal Heartbeat Detection on Smartphone Devices
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