Performance Evaluation of Fetal ECG Extraction Algorithms

Kaleem, Abdullah Mohammed; Kokate, Rajendra D.

doi:10.1007/978-981-13-5802-9_17

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Clinically, when one uses the abdominal electrode method to collect mixed ECG signals, there are often many artifacts or noises due to maternal and fetal movement or collector displacement [ 22 ]. Therefore, we used the data preprocessing method proposed by Dessì et al [ 23 ] to suppress baseline drift, power–frequency interference, and pulse artifacts.…”

Section: Methodsmentioning

confidence: 99%

Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition

Hao

Yang

Zhou

et al. 2022

Sensors

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Fetal electrocardiograms (FECGs) provide important clinical information for early diagnosis and intervention. However, FECG signals are extremely weak and are greatly influenced by noises. FECG signal extraction and detection are still challenging. In this work, we combined the fast independent component analysis (FastICA) algorithm with singular value decomposition (SVD) to extract FECG signals. The improved wavelet mode maximum method was applied to detect QRS waves and ST segments of FECG signals. We used the abdominal and direct fetal ECG database (ADFECGDB) and the Cardiology Challenge Database (PhysioNet2013) to verify the proposed algorithm. The signal-to-noise ratio of the best channel signal reached 45.028 dB and the issue of missing waveforms was addressed. The sensitivity, positive predictive value and F1 score of fetal QRS wave detection were 96.90%, 98.23%, and 95.24%, respectively. The proposed algorithm may be used as a new method for FECG signal extraction and detection.

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Section: Methodsmentioning

confidence: 99%

Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition

Hao

Yang

Zhou

et al. 2022

Sensors

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“…The division is illustrated in Table 2. The methods for acquiring NI-FECG signals involve notable sources of interference and noise [32]. Accurately extracting the FECG signal necessitates obtaining an AECG signal with a high signal-to-noise ratio.…”

Section: Database Descriptionmentioning

confidence: 99%

Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN–BiLSTM Architecture

Yang,

Chen,

2024

Sensors

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The fetal electrocardiogram (FECG) records changes in the graph of fetal cardiac action potential during conduction, reflecting the developmental status of the fetus in utero and its physiological cardiac activity. Morphological alterations in the FECG can indicate intrauterine hypoxia, fetal distress, and neonatal asphyxia early on, enhancing maternal and fetal safety through prompt clinical intervention, thereby reducing neonatal morbidity and mortality. To reconstruct FECG signals with clear morphological information, this paper proposes a novel deep learning model, CBLS-CycleGAN. The model’s generator combines spatial features extracted by the CNN with temporal features extracted by the BiLSTM network, thus ensuring that the reconstructed signals possess combined features with spatial and temporal dependencies. The model’s discriminator utilizes PatchGAN, employing small segments of the signal as discriminative inputs to concentrate the training process on capturing signal details. Evaluating the model using two real FECG signal databases, namely “Abdominal and Direct Fetal ECG Database” and “Fetal Electrocardiograms, Direct and Abdominal with Reference Heartbeat Annotations”, resulted in a mean MSE and MAE of 0.019 and 0.006, respectively. It detects the FQRS compound wave with a sensitivity, positive predictive value, and F1 of 99.51%, 99.57%, and 99.54%, respectively. This paper’s model effectively preserves the morphological information of FECG signals, capturing not only the FQRS compound wave but also the fetal P-wave, T-wave, P-R interval, and ST segment information, providing clinicians with crucial diagnostic insights and a scientific foundation for developing rational treatment protocols.

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Non-invasive Single Channel integration model for fetal ECG extraction and sustainable fetal healthcare using wavelet framework

Singh

Rajpal

Mehta

2022

Multimed Tools Appl

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Performance Evaluation of Fetal ECG Extraction Algorithms

Cited by 4 publications

References 9 publications

Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition

Fetal Electrocardiogram Signal Extraction Based on Fast Independent Component Analysis and Singular Value Decomposition

Enhancing Fetal Electrocardiogram Signal Extraction Accuracy through a CycleGAN Utilizing Combined CNN–BiLSTM Architecture

Non-invasive Single Channel integration model for fetal ECG extraction and sustainable fetal healthcare using wavelet framework

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