Machine Learning-based Detection of In-Utero Fetal Presentation from Non-Invasive Fetal ECG

Jäger, Katharina; Nissen, Michael; Richer, Robert; Rahm, Simone; Titzmann, Adriana; Fasching, Peter A.; Eskofier, Bjoern M.; Leutheuser, Heike

doi:10.1109/bhi56158.2022.9926804

Cited by 2 publications

(2 citation statements)

References 19 publications

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“…DI is calculated to determine the exact ratio of the true region (fetal) to pixels. The predicted fetal pixels and background pixels are calculated in equation (19). Jaccard index (JI) measures the similarity between two finite samples by dividing intersection sizes by union sizes.…”

Section: Resultsmentioning

confidence: 99%

FOE NET: Segmentation of Fetal in Ultrasound Images Using V-NET

Pregitha R.,

Kumar R. S.,

Selvakumar C.

2023

Int. j. electr. comput. eng. syst. (Online)

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Ultrasound is a non-invasive method to diagnose and treat medical conditions. It is becoming increasingly popular to use portable ultrasound scanning devices to reduce patient wait times and make healthcare more convenient for patients. By using ultrasound imaging, you will be able to obtain images with better quality and also gain information about soft tissues. The interference caused by tissues reflected in ultrasound waves resulted in intensified speckle sound, complicating imaging. In this paper, a novel Foe-Net has been proposed for segmenting the fetal in ultrasound images. Initially, the input US images are noise removal phase using two different filters Adaptive Gaussian Filter (AGF) and Adaptive Bilateral Filter (ABF) used to reduce the noise artifacts. Then, the US images are enhanced using CLAHE and MSR for smoothing to enhance the image quality. Finally, the denoised images are input to the V-net is used to segment the fetal in the US images. The experimental outcomes of the proposed Multi-Scale Retinex (MSR) is an image enhancement technique that improves image quality by adjusting its illumination and enhancing details. Foe-Net was measured by specific parameters such as specificity, precision, and accuracy. The proposed Foe-Net achieves an overall accuracy of 99.48%, specificity of 98.56 %, and precision of 96.82 % for segmented fetal in ultrasound images. The proposed Foe-Net attains better pre-processing outcomes at low error rates and, high SNR, high PSNR, and high SSIM values.

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Section: Resultsmentioning

confidence: 99%

FOE NET: Segmentation of Fetal in Ultrasound Images Using V-NET

Pregitha R.,

Kumar R. S.,

Selvakumar C.

2023

Int. j. electr. comput. eng. syst. (Online)

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“…Although NI-fECG technology is gaining acceptance, and its potential beyond fetal heart rate monitoring is being explored (Jaeger et al 2022), its implementation in clinical practice is still limited (Wakefield et al 2022). The proof of reliable heart rate extraction under real conditions is yet to be established, and there is currently no definition of normative values (Smith et al 2018).…”

Section: Introductionmentioning

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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Machine Learning-based Detection of In-Utero Fetal Presentation from Non-Invasive Fetal ECG

Cited by 2 publications

References 19 publications

FOE NET: Segmentation of Fetal in Ultrasound Images Using V-NET

FOE NET: Segmentation of Fetal in Ultrasound Images Using V-NET

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

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