Abstract
A major problem in CTG analysis is that detection
of a suspicious pattern in short intervals so that one can reduce
the damage caused by a delay of an automatic monitoring
system. In this paper, we aim for improving intrapartum
surveillance based on signal processing and machine learning
techniques. We evaluate a classification method on a real data
set.
of a suspicious pattern in short intervals so that one can reduce
the damage caused by a delay of an automatic monitoring
system. In this paper, we aim for improving intrapartum
surveillance based on signal processing and machine learning
techniques. We evaluate a classification method on a real data
set.
| Original language | English |
|---|---|
| Number of pages | 1 |
| Publication status | Published - 2017 Jul |
| Event | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - JUNGMUN Sightseeing Complex, SEOGWIPO City, Korea, Republic of Duration: 2017 Jul 11 → 2017 Jul 15 https://embc.embs.org/2017/ |
Conference
| Conference | 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 |
|---|---|
| Country/Territory | Korea, Republic of |
| City | SEOGWIPO City |
| Period | 2017/07/11 → 2017/07/15 |
| Internet address |
Subject classification (UKÄ)
- Computer Vision and Robotics (Autonomous Systems)