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 journalArticlepeer-review

9 Citations (Scopus)


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 languageEnglish
Pages (from-to)239-243
Number of pages5
JournalICT Express
Issue number2
Publication statusPublished - Jun 2022


Dive into the research topics of 'Generative adversarial networks for unbalanced fetal heart rate signal classification'. Together they form a unique fingerprint.

Cite this