Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal

Muhammad Anwar  Ma'sum; P. Riskyana Dewi Intan; Wisnu Jatmiko; Adila Alfa Krisnadhi; Noor Akhmad Setiawan; I. Made Agus Dwi Suarjaya

doi:10.1109/IWBIS.2019.8935835

Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal

Muhammad Anwar Ma'sum
, P. Riskyana Dewi Intan
, Wisnu Jatmiko
, Adila Alfa Krisnadhi
, Noor Akhmad Setiawan
, I. Made Agus Dwi Suarjaya

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

15 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2019 International Workshop on Big Data and Information Security, IWBIS 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	51-56
Number of pages	6
ISBN (Electronic)	9781728153476
DOIs	https://doi.org/10.1109/IWBIS.2019.8935835
Publication status	Published - Oct 2019
Event	2019 International Workshop on Big Data and Information Security, IWBIS 2019 - Bali, Indonesia Duration: 11 Oct 2019 → …

Publication series

Name	2019 International Workshop on Big Data and Information Security, IWBIS 2019

Conference

Conference	2019 International Workshop on Big Data and Information Security, IWBIS 2019
Country/Territory	Indonesia
City	Bali
Period	11/10/19 → …

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Cardiotocography
Deep Learning
Hypoxia
Improvement

Access to Document

10.1109/IWBIS.2019.8935835

Cite this

Ma'sum, M. A., Riskyana Dewi Intan, P., Jatmiko, W., Krisnadhi, A. A., Setiawan, N. A., & Made Agus Dwi Suarjaya, I. (2019). Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal. In 2019 International Workshop on Big Data and Information Security, IWBIS 2019 (pp. 51-56). Article 8935835 (2019 International Workshop on Big Data and Information Security, IWBIS 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IWBIS.2019.8935835

Ma'sum, Muhammad Anwar ; Riskyana Dewi Intan, P. ; Jatmiko, Wisnu et al. / Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal. 2019 International Workshop on Big Data and Information Security, IWBIS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 51-56 (2019 International Workshop on Big Data and Information Security, IWBIS 2019).

@inproceedings{66a6a8dfbfaa4663b6d42a7115c312ed,

title = "Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal",

abstract = "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.",

keywords = "Cardiotocography, Deep Learning, Hypoxia, Improvement",

author = "Ma'sum, \{Muhammad Anwar\} and \{Riskyana Dewi Intan\}, P. and Wisnu Jatmiko and Krisnadhi, \{Adila Alfa\} and Setiawan, \{Noor Akhmad\} and \{Made Agus Dwi Suarjaya\}, I.",

year = "2019",

month = oct,

doi = "10.1109/IWBIS.2019.8935835",

language = "English",

series = "2019 International Workshop on Big Data and Information Security, IWBIS 2019",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "51--56",

booktitle = "2019 International Workshop on Big Data and Information Security, IWBIS 2019",

address = "United States",

note = "2019 International Workshop on Big Data and Information Security, IWBIS 2019 ; Conference date: 11-10-2019",

}

Ma'sum, MA, Riskyana Dewi Intan, P, Jatmiko, W , Krisnadhi, AA, Setiawan, NA & Made Agus Dwi Suarjaya, I 2019, Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal. in 2019 International Workshop on Big Data and Information Security, IWBIS 2019., 8935835, 2019 International Workshop on Big Data and Information Security, IWBIS 2019, Institute of Electrical and Electronics Engineers Inc., pp. 51-56, 2019 International Workshop on Big Data and Information Security, IWBIS 2019, Bali, Indonesia, 11/10/19. https://doi.org/10.1109/IWBIS.2019.8935835

Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal. / Ma'sum, Muhammad Anwar ; Riskyana Dewi Intan, P.; Jatmiko, Wisnu et al.
2019 International Workshop on Big Data and Information Security, IWBIS 2019. Institute of Electrical and Electronics Engineers Inc., 2019. p. 51-56 8935835 (2019 International Workshop on Big Data and Information Security, IWBIS 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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

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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

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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

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ER -

Ma'sum MA, Riskyana Dewi Intan P, Jatmiko W , Krisnadhi AA, Setiawan NA, Made Agus Dwi Suarjaya I. Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal. In 2019 International Workshop on Big Data and Information Security, IWBIS 2019. Institute of Electrical and Electronics Engineers Inc. 2019. p. 51-56. 8935835. (2019 International Workshop on Big Data and Information Security, IWBIS 2019). doi: 10.1109/IWBIS.2019.8935835

Improving Deep Learning Classifier for Fetus Hypoxia Detection in Cardiotocography Signal

Abstract

Publication series

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UN SDGs

Keywords

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