TY - GEN
T1 - Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal
AU - Ma'sum, Muhammad Anwar
AU - Riskyana Dewi Intan, P.
AU - Jatmiko, Wisnu
AU - Krisnadhi, Adila Alfa
AU - Setiawan, Noor Akhmad
AU - Made Agus Dwi Suarjaya, I.
PY - 2019/10
Y1 - 2019/10
N2 - One of the stage that performed during a maternal and fetal health monitoring is the calculation of fetal heart rate and uterine contraction using Cardiotocography (CTG). The aim of fetal health using CTG is to avoid morbidity and mortality in fetus at risk of hypoxia. This paper propose a hypoxia detection by using classification. In this study, we improve deep learning method in order to increase its capability in detecting hypoxia. The improvement is conducted by several strategies i.e. input representation, data-scaling, data up-sampling, and adjusting classifier layers. The improvement is conducted by several strategies i.e. input representation, data-scaling, data up-sampling, and increasing classifier layers. For whole dataset that achieved by proposed method is 81%. The improved DenseNet achieved 50%, 43%, 46% precision, recall and f1-score respectively for hypoxia class that is not achieved by standard DenseNet.
AB - One of the stage that performed during a maternal and fetal health monitoring is the calculation of fetal heart rate and uterine contraction using Cardiotocography (CTG). The aim of fetal health using CTG is to avoid morbidity and mortality in fetus at risk of hypoxia. This paper propose a hypoxia detection by using classification. In this study, we improve deep learning method in order to increase its capability in detecting hypoxia. The improvement is conducted by several strategies i.e. input representation, data-scaling, data up-sampling, and adjusting classifier layers. The improvement is conducted by several strategies i.e. input representation, data-scaling, data up-sampling, and increasing classifier layers. For whole dataset that achieved by proposed method is 81%. The improved DenseNet achieved 50%, 43%, 46% precision, recall and f1-score respectively for hypoxia class that is not achieved by standard DenseNet.
KW - Cardiotocography
KW - Deep Learning
KW - Hypoxia
KW - Improvement
UR - http://www.scopus.com/inward/record.url?scp=85078027361&partnerID=8YFLogxK
U2 - 10.1109/IWBIS.2019.8935835
DO - 10.1109/IWBIS.2019.8935835
M3 - Conference contribution
T3 - 2019 International Workshop on Big Data and Information Security, IWBIS 2019
SP - 51
EP - 56
BT - 2019 International Workshop on Big Data and Information Security, IWBIS 2019
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2019 International Workshop on Big Data and Information Security, IWBIS 2019
Y2 - 11 October 2019
ER -