Generative adversarial networks for unbalanced fetal heart rate signal classification

Riskyana Dewi Intan Puspitasari; M. Anwar Ma'sum; Machmud R. Alhamidi; Kurnianingsih; Wisnu Jatmiko

doi:10.1016/j.icte.2021.06.007

Generative adversarial networks for unbalanced fetal heart rate signal classification

Riskyana Dewi Intan Puspitasari, M. Anwar Ma'sum, Machmud R. Alhamidi, Kurnianingsih, Wisnu Jatmiko

Research output: Contribution to journal › Article › peer-review

5 Citations (Scopus)

Abstract

Deep Learning Classification is often used to analyze biomedical data. One of them is to analyze the Fetal Heart Rate (FHR) signal data used to check and monitor maternal and fetal health and prevent mobility and mortality in fetuses at risk of developing hypoxia. The problem that often occurs in the data is data unbalance. Time Series Generative Adversarial Networks (TSGAN) solves data imbalance in the FHR signal and generate more data and better classification performance. Augmentation using the GAN model in this study obtained an increase in the Quality Index of 3%–44% from other models.

Original language	English
Pages (from-to)	239-243
Number of pages	5
Journal	ICT Express
Volume	8
Issue number	2
DOIs	https://doi.org/10.1016/j.icte.2021.06.007
Publication status	Published - Jun 2022

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10.1016/j.icte.2021.06.007

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title = "Generative adversarial networks for unbalanced fetal heart rate signal classification",

abstract = "Deep Learning Classification is often used to analyze biomedical data. One of them is to analyze the Fetal Heart Rate (FHR) signal data used to check and monitor maternal and fetal health and prevent mobility and mortality in fetuses at risk of developing hypoxia. The problem that often occurs in the data is data unbalance. Time Series Generative Adversarial Networks (TSGAN) solves data imbalance in the FHR signal and generate more data and better classification performance. Augmentation using the GAN model in this study obtained an increase in the Quality Index of 3%–44% from other models.",

author = "Puspitasari, {Riskyana Dewi Intan} and Ma'sum, {M. Anwar} and Alhamidi, {Machmud R.} and Kurnianingsih and Wisnu Jatmiko",

note = "Funding Information: We would like to show our gratitude forPublikasi Terindeks Internasional (PUTI) Prosiding Grant 2020 from Universitas Indonesia with grant number: NKB-4065/UN2.RST/HKP.05.00/2020 for supporting this paper. Publisher Copyright: {\textcopyright} 2021 The Korean Institute of Communications and Information Sciences (KICS)",

year = "2022",

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doi = "10.1016/j.icte.2021.06.007",

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AU - Puspitasari, Riskyana Dewi Intan

AU - Ma'sum, M. Anwar

AU - Alhamidi, Machmud R.

AU - Kurnianingsih,

AU - Jatmiko, Wisnu

N1 - Funding Information: We would like to show our gratitude forPublikasi Terindeks Internasional (PUTI) Prosiding Grant 2020 from Universitas Indonesia with grant number: NKB-4065/UN2.RST/HKP.05.00/2020 for supporting this paper. Publisher Copyright: © 2021 The Korean Institute of Communications and Information Sciences (KICS)

PY - 2022/6

Y1 - 2022/6

N2 - Deep Learning Classification is often used to analyze biomedical data. One of them is to analyze the Fetal Heart Rate (FHR) signal data used to check and monitor maternal and fetal health and prevent mobility and mortality in fetuses at risk of developing hypoxia. The problem that often occurs in the data is data unbalance. Time Series Generative Adversarial Networks (TSGAN) solves data imbalance in the FHR signal and generate more data and better classification performance. Augmentation using the GAN model in this study obtained an increase in the Quality Index of 3%–44% from other models.

AB - Deep Learning Classification is often used to analyze biomedical data. One of them is to analyze the Fetal Heart Rate (FHR) signal data used to check and monitor maternal and fetal health and prevent mobility and mortality in fetuses at risk of developing hypoxia. The problem that often occurs in the data is data unbalance. Time Series Generative Adversarial Networks (TSGAN) solves data imbalance in the FHR signal and generate more data and better classification performance. Augmentation using the GAN model in this study obtained an increase in the Quality Index of 3%–44% from other models.

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Generative adversarial networks for unbalanced fetal heart rate signal classification

Abstract

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