Abstract
Fetal Heart Rate (FHR), an important recording in Cardiotocography (CTG)-based fetal health status monitoring, is the only information that clinical obstetricians can directly obtain and use. A challenge, however, is that missing samples are very common in FHR due to various causes such as fetal movements and sensor malfunctions. The aim is the development of an inpainting tool which is suitable for different missing lengths q and various total missing percentages Q, as well as for use in online mode. This study focused on two major impediments to existing inpainting methods: the longer the missing length, the more difficult it is to recover with mathematical methods; the reliance on tens of thousands of training samples, and the computational burden caused by full batch-based dictionary learning algorithms. We present a regularized minimization approach to signal recovery, which combines a L0.6 - norm minimized sparse dictionary learning algorithm (MSDL) and a model optimization strategy for using a mini-batch version for signal recovery. Using 100 FHR recordings with 2 protocols designed to simulate missing clinical data scenarios, the combined method performed favorably in terms of 5 data analysis metrics and 3 clinical indicators. Comparing 4 inpainting methods, we were able to prove the superiority of the proposed algorithm for both large q and large Q. The experimental results showed the lowest values (2.64 (MAE), 4.68 (RMSE)) when Q = 5% with short interval lengths. The developed architecture provides a reference value for the practical application of recovering missing samples online.
Highlights
F ETAL distress and hypoxia are the main causes of adverse events such as neonatal asphyxia and disability [1], whichManuscript received January 16, 2021; revised April 9, 2021 and May 24, 2021; accepted June 24, 2021
On the basis of the guidelines provided by the International Federation of Gynecology and Obstetrics (FIGO) [2], Fetal Heart Rate (FHR) signals can provide valuable information about fetal homeostasis during the critical period of late pregnancy and labor, and are the only information directly available to clinicians to make a professional diagnosis by naked-eye inspection
We carried out a large number of experiments using the Czech Technical University-University Hospital in Brno (CTUUHB) Intrapartum Cardiotocography Database [24], [25]
Summary
F ETAL distress and hypoxia are the main causes of adverse events such as neonatal asphyxia and disability [1], whichManuscript received January 16, 2021; revised April 9, 2021 and May 24, 2021; accepted June 24, 2021. Date of publication June 30, 2021; date of current version January 5, 2022. It is crucial, to monitor fetal safety in the womb in late pregnancy. A typical CTG recording consists of two simultaneously acquired signals, namely Fetal Heart Rate (FHR) and Uterine Contraction (UC), in which FHR is usually obtained by Doppler ultrasound probe or fetal scalp electrode. The former is a non-invasive prenatal diagnosis technique, while the latter is an invasive technique. A very challenging objective is development of artificial intelligence auxiliary diagnosis (AIAD) tools for the extraction of meaningful features from FHR recordings that could be reliably used to point out possible fetal and neonatal pathologic conditions
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