AI Article Synopsis
- Fetal heart rate (FHR) is monitored externally during labor using Doppler ultrasound, but sometimes data is missing due to movement.
- A new method using deep Gaussian processes is suggested for estimating these missing FHR samples by leveraging similarities in the data's state space.
- The proposed approach outperformed traditional interpolation methods in experiments with real FHR recordings, indicating it provides more reliable data recovery.
Article Abstract
During labor, fetal heart rate (FHR) is monitored externally using Doppler ultrasound. This is done continuously, but for various reasons (e.g., fetal or maternal movements) the system does not record any samples for varying periods of time. In many settings, it would be quite beneficial to estimate the missing samples. In this paper, we propose a (deep) Gaussian process-based approach for estimation of consecutively missing samples in FHR recordings. The method relies on similarities in the state space and on exploiting the concept of attractor manifolds. The proposed approach was tested on a short segment of real FHR recordings. The experimental results indicate that the proposed approach is able to provide more reliable results in comparison to several interpolation methods that are commonly applied for processing of FHR signals.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7887835 | PMC |
| http://dx.doi.org/10.23919/eusipco47968.2020.9287490 | DOI Listing |
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