ArticleSensors (Basel, Switzerland)2022
Adaptive Filtering for the Maternal Respiration Signal Attenuation in the Uterine Electromyogram.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Analysis of electrohysterogram signals for predicting obstetric outcome using machine learning methods: a scoping review.BMC pregnancy and childbirth · 2026Article
- An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery.Scientific data · 2023Article
- Prediction of Preterm Labor from the Electrohysterogram Signals Based on Different Gestational Weeks.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
8 authors at 3 institutions in 1 country.
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Abstract
The electrohysterogram (EHG) is the uterine muscle electromyogram recorded at the abdominal surface of pregnant or non-pregnant woman. The maternal respiration electromyographic signal (MR-EMG) is one of the most relevant interferences present in an EHG. Alvarez (Alv) waves are components of the EHG that have been indicated as having the potential for preterm and term birth prediction. The MR-EMG component in the EHG represents an issue, regarding Alv wave application for pregnancy monitoring, for instance, in preterm birth prediction, a subject of great research interest. Therefore, the Alv waves denoising method should be designed to include the interference MR-EMG attenuation, without compromising the original waves. Adaptive filter properties make them suitable for this task. However, selecting the optimal adaptive filter and its parameters is an important task for the success of the filtering operation. In this work, an algorithm is presented for the automatic adaptive filter and parameter selection using synthetic data. The filter selection pool comprised sixteen candidates, from which, the Wiener, recursive least squares (RLS), householder recursive least squares (HRLS), and QR-decomposition recursive least squares (QRD-RLS) were the best performers. The optimized parameters were L = 2 (filter length) for all of them and λ = 1 (forgetting factor) for the last three. The developed optimization algorithm may be of interest to other applications. The optimized filters were applied to real data. The result was the attenuation of the MR-EMG in Alv waves power. For the Wiener filter, power reductions for quartile 1, median, and quartile 3 were found to be -16.74%, -20.32%, and -15.78%, respectively (
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Registered trials
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