B. Hurezeanu, G. Ungureanu, D. Taralunga, R. Strungaru, I. Gussi, W. Wolf
{"title":"FECG delineation from abdominal signals using wavelet transform","authors":"B. Hurezeanu, G. Ungureanu, D. Taralunga, R. Strungaru, I. Gussi, W. Wolf","doi":"10.1109/ICEPE.2014.6969962","DOIUrl":null,"url":null,"abstract":"Analysis of the fetal electrocardiogram (fECG) obtained from abdominal signals can be a difficult task as the signal of interest is a weak signal, buried in several other signal components. Automatic assessment of fetus condition using abdominal signals (ADS) can be an important tool for clinicians, but, due to the high noise and considering the variation of the fECG morphology, its implementation is rather difficult. The current study proposes a wavelet approach for fECG analysis, which identifies its cyclic features.","PeriodicalId":271843,"journal":{"name":"2014 International Conference and Exposition on Electrical and Power Engineering (EPE)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 International Conference and Exposition on Electrical and Power Engineering (EPE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICEPE.2014.6969962","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
Analysis of the fetal electrocardiogram (fECG) obtained from abdominal signals can be a difficult task as the signal of interest is a weak signal, buried in several other signal components. Automatic assessment of fetus condition using abdominal signals (ADS) can be an important tool for clinicians, but, due to the high noise and considering the variation of the fECG morphology, its implementation is rather difficult. The current study proposes a wavelet approach for fECG analysis, which identifies its cyclic features.
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