胎心率
生成语法
对抗制
计算机科学
信号(编程语言)
人工智能
数据质量
机器学习
模式识别(心理学)
数据挖掘
医学
心率
内科学
工程类
血压
公制(单位)
程序设计语言
运营管理
作者
Riskyana Dewi Intan Puspitasari,M. Anwar Ma’sum,Machmud Roby Alhamidi,Kurnianingsih Kurnianingsih,Wisnu Jatmiko
出处
期刊:ICT Express
[Elsevier]
日期:2022-06-01
卷期号:8 (2): 239-243
被引量:5
标识
DOI:10.1016/j.icte.2021.06.007
摘要
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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