Joint time-frequency analysis and non-linear estimation for fetal ECG extraction

Krupa, Abel Jaba Deva; Dhanalakshmi, Samiappan; Kumar, Ritesh

doi:10.1016/j.bspc.2022.103569

Cited by 13 publications

(3 citation statements)

References 45 publications

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“…Next, a low-pass Butterworth filter with a cut-off frequency of 100 Hz was applied to remove the high-frequency noise including electromyographic noise [ 59 ]. This filter was used to eliminate any high-frequency noise that could have been introduced during the signal acquisition ( Figure 1 c) [ 60 ]. Power-line interference is a common problem in ECG signal acquisition and can cause significant artifacts in the signal [ 48 ].…”

Section: Methodsmentioning

confidence: 99%

Feasibility Analysis of ECG-Based pH Estimation for Asphyxia Detection in Neonates

Hussain,

Amin,

McDermott

et al. 2024

Sensors

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Birth asphyxia is a potential cause of death that is also associated with acute and chronic morbidities. The traditional and immediate approach for monitoring birth asphyxia (i.e., arterial blood gas analysis) is highly invasive and intermittent. Additionally, alternative noninvasive approaches such as pulse oximeters can be problematic, due to the possibility of false and erroneous measurements. Therefore, further research is needed to explore alternative noninvasive and accurate monitoring methods for asphyxiated neonates. This study aims to investigate the prominent ECG features based on pH estimation that could potentially be used to explore the noninvasive, accurate, and continuous monitoring of asphyxiated neonates. The dataset used contained 274 segments of ECG and pH values recorded simultaneously. After preprocessing the data, principal component analysis and the Pan–Tompkins algorithm were used for each segment to determine the most significant ECG cycle and to compute the ECG features. Descriptive statistics were performed to describe the main properties of the processed dataset. A Kruskal–Wallis nonparametric test was then used to analyze differences between the asphyxiated and non-asphyxiated groups. Finally, a Dunn–Šidák post hoc test was used for individual comparison among the mean ranks of all groups. The findings of this study showed that ECG features (T/QRS, T Amplitude, Tslope, Tslope/T, Tslope/|T|, HR, QT, and QTc) based on pH estimation differed significantly (p < 0.05) in asphyxiated neonates. All these key ECG features were also found to be significantly different between the two groups.

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Section: Methodsmentioning

confidence: 99%

Feasibility Analysis of ECG-Based pH Estimation for Asphyxia Detection in Neonates

Hussain,

Amin,

McDermott

et al. 2024

Sensors

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“…As shown in Table I, values of Tn presented. Parameters of classification can be calculated, as shown in equations (27)(28)(29)(30)(31)(32)(33). Where t method is MMFCC using MST instead of Where p equals 1 in equation ( 6).…”

Section: Figure 1 Steps Of Feature Extraction Usingmentioning

confidence: 99%

Gender of Fetus Identification Using Modified Mel-Frequency Cepstral Coefficients Based on Fractional Discrete Cosine Transform

Azmy

2024

IEEE Access

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Fetal heart sounds is measured to follow the developing status of fetus. The used database of fetal heart sounds is obtained from Physionet challenge. In this paper, novel models are created to extract features from fetal heart sounds; to identify the gender of fetus (male or female), using modified Mel-Frequency Cepstral Coefficients (MMFCC). The high pass filter which is used in the pre-emphasis step of MMFCC, used to remove noise from fetal heart sounds. These models are compared; to obtain high classification parameters (i.e. the accuracy rate and the area under curve). Classification method is based on deep learning, using recurrent neural networks (RNN) based on bidirectional long short-term memory (BiLSTM). The programming code of 13 models is created by the author, using Matlab software environment. Both accuracy rate and area under curve (AUC) are obtained using MMFCC based on fractional discrete cosine transform (FRDCT), overperform the same results of other models. So, this model is selected to extract features of fetal heart sounds. The contribution here is using novel model. FRDCT is new fractional transform, which is decomposed from fractional Fourier transform (FRFT) and discrete cosine transform (DCT). The secret behind selecting FRDCT based on MMFCC as a model; FRDCT decomposes signals perfectly in time-frequency domain. The best model can be programmed on a portable device; to know the gender of fetus by a caregiver in rural areas, without needing to use ultrasound.

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“…In comparison with the traditional CTG method, the FECG signals stands out for its capacity to more comprehensively depict the overall scenario of fetal heart activity. This capability positions the FECG signals as the developmental trend in fetal monitoring for the future [7].…”

Section: Introductionmentioning

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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Joint time-frequency analysis and non-linear estimation for fetal ECG extraction

Cited by 13 publications

References 45 publications

Feasibility Analysis of ECG-Based pH Estimation for Asphyxia Detection in Neonates

Feasibility Analysis of ECG-Based pH Estimation for Asphyxia Detection in Neonates

Gender of Fetus Identification Using Modified Mel-Frequency Cepstral Coefficients Based on Fractional Discrete Cosine Transform

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

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