Generative adversarial networks for unbalanced fetal heart rate signal classification
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 th...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-06-01
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| Series: | ICT Express |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405959521000837 |
| _version_ | 1811244564079443968 |
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| author | Riskyana Dewi Intan Puspitasari M. Anwar Ma’sum Machmud R. Alhamidi Kurnianingsih Wisnu Jatmiko |
| author_facet | Riskyana Dewi Intan Puspitasari M. Anwar Ma’sum Machmud R. Alhamidi Kurnianingsih Wisnu Jatmiko |
| author_sort | Riskyana Dewi Intan Puspitasari |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T14:27:23Z |
| format | Article |
| id | doaj.art-428b03f826c146b9b2ee60fd8f944866 |
| institution | Directory Open Access Journal |
| issn | 2405-9595 |
| language | English |
| last_indexed | 2024-04-12T14:27:23Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | ICT Express |
| spelling | doaj.art-428b03f826c146b9b2ee60fd8f9448662022-12-22T03:29:24ZengElsevierICT Express2405-95952022-06-0182239243Generative adversarial networks for unbalanced fetal heart rate signal classificationRiskyana Dewi Intan Puspitasari0M. Anwar Ma’sum1Machmud R. Alhamidi2 Kurnianingsih3Wisnu Jatmiko4Faculty of Computer Science, Universitas Indonesia, Depok, Indonesia; Corresponding authors.Faculty of Computer Science, Universitas Indonesia, Depok, IndonesiaFaculty of Computer Science, Universitas Indonesia, Depok, IndonesiaDepartment of Electrical Engineering, Politeknik Negeri Semarang, Semarang, IndonesiaFaculty of Computer Science, Universitas Indonesia, Depok, Indonesia; Corresponding authors.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.http://www.sciencedirect.com/science/article/pii/S2405959521000837 |
| spellingShingle | Riskyana Dewi Intan Puspitasari M. Anwar Ma’sum Machmud R. Alhamidi Kurnianingsih Wisnu Jatmiko Generative adversarial networks for unbalanced fetal heart rate signal classification ICT Express |
| title | Generative adversarial networks for unbalanced fetal heart rate signal classification |
| title_full | Generative adversarial networks for unbalanced fetal heart rate signal classification |
| title_fullStr | Generative adversarial networks for unbalanced fetal heart rate signal classification |
| title_full_unstemmed | Generative adversarial networks for unbalanced fetal heart rate signal classification |
| title_short | Generative adversarial networks for unbalanced fetal heart rate signal classification |
| title_sort | generative adversarial networks for unbalanced fetal heart rate signal classification |
| url | http://www.sciencedirect.com/science/article/pii/S2405959521000837 |
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