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Please use this identifier to cite or link to this item: https://epub.vgu.edu.vn/handle/dlibvgu/1268
DC FieldValueLanguage
dc.contributor.authorTran Phuong Namen_US
dc.date.accessioned2022-01-18T04:00:41Z-
dc.date.available2022-01-18T04:00:41Z-
dc.date.issued2021-
dc.identifier.urihttp://epub.vgu.edu.vn/handle/dlibvgu/1268-
dc.description.abstractCardiotocography (CTG) traces, comprised of the fetal heart rate (FHR) and the uterine contraction (UC), are used and interpreted by gynecologists and obstetricians to visually present, measure, and assess the wellbeing of the foetus during the antenatal period by following the International Federation of Gynaecology and Obstetrics (FIGO) guidelines. The research is an attempt to train a model using the CTG values as input to predict the outcome of umbilical artery pH. This process involves using the built-in Long short-term memory (LSTM) deep learning model of Liquid time-constant networks (LTCs)[1]. Several windowing strategies are applied with 2 types of output: regression prediction of pH value and classification. With best accuracy metrics of MAPE 1.16% for regression and 96.43% for classification. There are also several related works for comparison and referenceen_US
dc.language.isoenen_US
dc.publisherVietnamese-German University-
dc.rightsAttribution-NonCommercial 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/*
dc.subjectLong short-term memoryen_US
dc.subjectDeep learningen_US
dc.subjectMachine learningen_US
dc.subjectCardiocotographen_US
dc.subjectRegressionen_US
dc.titleAnomaly detection in cardiotocograph signal using deep neural networken_US
dc.typeThesisen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
item.languageiso639-1other-
Appears in Collections:Computer Science (CS)
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