Fetal QRS Detection in Noninvasive Abdominal Electrocardiograms Using Principal Component Analysis and Discrete Wavelet Transforms with Signal Quality Estimation

Mollakazemi, Mohammad Javad; Asadi, Fatemeh; Tajnesaei, Mahsa; Ghaffari, Ali

doi:10.31661/jbpe.v0i0.397

Cited by 5 publications

(2 citation statements)

References 14 publications

(10 reference statements)

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“…Other works use e.g. derivative-based approaches, adaptive thresholds, or neural-networks for detecting the fetal QRS peaks in the signal (Varanini et al 2014, Mollakazemi et al 2021, Jebastine 2023.…”

Section: B1 Pregnancy B2 Labourmentioning

confidence: 99%

Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings

Jaeger,

Nissen,

Rahm

et al. 2024

Physiol. Meas.

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Objective. Perinatal asphyxia poses a significant risk to neonatal health, necessitating accurate fetal heart rate monitoring for effective detection and management. The current gold standard, cardiotocography, has inherent limitations, highlighting the need for alternative approaches. The emerging technology of non-invasive fetal electrocardiography shows promise as a new sensing technology for fetal cardiac activity, offering potential advancements in the detection and management of perinatal asphyxia. Although algorithms for fetal QRS detection have been developed in the past, only a few of them demonstrate accurate performance in the presence of noise and artifacts. Approach. In this work, we propose Power-MF, a new algorithm for fetal QRS detection combining power spectral density and matched filter techniques. We benchmark Power-MF against three open-source algorithms on two recently published datasets (Abdominal and Direct Fetal ECG Database: ADFECG, subsets B1 Pregnancy and B2 Labour; Non-invasive Multimodal Foetal ECG-Doppler Dataset for Antenatal Cardiology Research: NInFEA). Main results. Our results show that Power-MF outperforms state-of-the-art algorithms on ADFECG (B1 Pregnancy: 99.5 % ± 0.5 % F1-score, B2 Labour: 98.0 % ± 3.0 % F1-score) and on NInFEA in three of six electrode configurations by being more robust against noise. Significance. Through this work, we contribute to improving the accuracy and reliability of fetal cardiac monitoring, an essential step toward early detection of perinatal asphyxia with the long-term goal of reducing costs and making prenatal care more accessible.

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Section: B1 Pregnancy B2 Labourmentioning

confidence: 99%

Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings

Jaeger,

Nissen,

Rahm

et al. 2024

Physiol. Meas.

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“…MAE: this is the average of the absolute error values between the predicted and actual values. As with the MSE, a lower MAE indicates better performance [25]. R²: it measures the proportion of variance in the dependent variable (your prediction) that the model explains.…”

Section: Performance Evaluationmentioning

confidence: 99%

Fetal electrocardiogram prediction using machine learning: a random forest-based approach

Moutaib,

Fattah,

Farhaoui

et al. 2024

IJEECS

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<p class="IOPText">Monitoring fetal health during pregnancy ensures safe delivery and the newborn’s well-being. The fetal electrocardiogram (fetal ECG) is a valuable tool for assessing fetal cardiac health, but interpretation of ECG data can be challenging due to its complexity and variability. In this work, we explore the application of machine learning, particularly random forest, to predict and analyze fetal ECGs. With its ability to manage large datasets and provide precise insights, random forest is a promising solution for this challenge. By comparing our random forest-based approach with other standard machine learning techniques such as artificial neural network (ANN), support vector machines (SVM), and recurrent neural networks (RNN), we observed that our solution outperformed these methods in accuracy, robustness, and reliability. This article details the methodology used, the implementation of the algorithm, as well as the comparative results obtained. Emphasis is placed on the benefits of random forest in this specific medical context, highlighting its potential as a future tool for fetal ECG prediction. Ultimately, our research suggests a shift toward random forest-based solutions for more efficient and accurate analysis of fetal ECGs, with direct implications for clinical practice and fetal well-being.</p>

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Fetal ECG Extraction and QRS Detection using Independent Component Analysis

Mirza

Bhole

Singh

2020

2020 16th IEEE International Colloquium on Signal Processing &Amp; Its Applications (CSPA)

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Fetal QRS Detection in Noninvasive Abdominal Electrocardiograms Using Principal Component Analysis and Discrete Wavelet Transforms with Signal Quality Estimation

Cited by 5 publications

References 14 publications

Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings

Power-MF: robust fetal QRS detection from non-invasive fetal electrocardiogram recordings

Fetal electrocardiogram prediction using machine learning: a random forest-based approach

Fetal ECG Extraction and QRS Detection using Independent Component Analysis

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