A New Approach for Extracting and Characterizing Fetal Electrocardiogram

Ziani, Said; Hassouani, Y. El

doi:10.18280/ts.370304

Cited by 15 publications

(5 citation statements)

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“…Compared with other recent works in the field [32][33][34][35][36] , the presented approach is much more reliable and accurate. On the one hand, the process is based on very subtle mathematics tools that can be developed numerically even for twins and are easily implemented in real-time using embedded systems such as DSP or Raspberry.…”

Section: Discussionmentioning

confidence: 80%

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Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

Ziani

Farhaoui

Moutaib

2023

Big Data Min. Anal.

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This paper deals with detecting fetal electrocardiogram FECG signals from single-channel abdominal lead.It is based on the Convolutional Neural Network (CNN) combined with advanced mathematical methods, such as Independent Component Analysis (ICA), Singular Value Decomposition (SVD), and a dimension-reduction technique like Nonnegative Matrix Factorization (NMF). Due to the highly disproportionate frequency of the fetus's heart rate compared to the mother's, the time-scale representation clearly distinguishes the fetal electrical activity in terms of energy. Furthermore, we can disentangle the various components of fetal ECG, which serve as inputs to the CNN model to optimize the actual FECG signal, denoted by FECGr, which is recovered using the SVD-ICA process. The findings demonstrate the efficiency of this innovative approach, which may be deployed in real-time.

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Section: Discussionmentioning

confidence: 80%

“…10. Y 4 .t/ is the temporal representation of Y .t / after applying the data-adaptive multiresolution technique like Empirical Mode Decomposition (EMD) [32] of submatrix Y . Therefore, we can generate the required number of TSI images by determining the modulus of CWT of each Y i .…”

Section: Generation Of Datamentioning

confidence: 99%

Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

Ziani

Farhaoui

Moutaib

2023

Big Data Min. Anal.

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“…Another criterion that is very important in this comparison is the prediction in real time, since our system is supposed to be used in VR systems [23] for example, so we made a test with an external camera to see the performance of all the models in real time, and from this test we concluded that the ResNet50 model has a better performance in the prediction in real time of all the emotion classes. As indicated in the aforementioned literature [24][25][26], the method provided in this paper may be designed and implemented utilizing image segmentation techniques based on wavelet transformations.…”

Section: Accuracy = True Positive True Positive + False Positivementioning

confidence: 99%

Improved Methods for Automatic Facial Expression Recognition

Echoukairi¹,

Ghmary²,

Ziani³

et al. 2023

Int. J. Interact. Mob. Technol.

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Facial expressions constitute one of the most effective and instinctive methods that allow people to communicate their emotions and intentions. In this context, the both Machine Learning (ML) and Convolutional Neural Networks (CNNs) have been used for emotion recognition. Efficient recognition systems are required for good human-computer interaction. However, facial expression recognition is related to several methods that impact the performance of facial recognition systems. In this paper, we demonstrate a state-of-the-art of 65% accuracy on the FER2013 dataset by leveraging numerous techniques from recent research and we also proposed some new methods for improving accuracy by combining CNN architectures such as VGG-16 and Resnet-50 with auxiliary datasets such as JAFFE and CK. To predict emotions, we used a second approach based on geometric features and facial landmarks to calculate and transmit the feature vector to the SVM model. The results show that the ResNet50 model outperforms all other emotion prediction models in real time by maximizing.

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“…However, obtaining a clean and reliable FECG signal is challenging due to the presence of maternal electrocardiogram (MECG) contamination and various types of noise. Extensive research has attempted to address this separating challenge since 1980; they may be roughly divided into two categories: those who employ multi-channel techniques, such as independent component analysis (ICA) and PCA, as stated in [1][2], and those who use a single channel, such as ICA-NMF, ICA-EMD, and ICA-SVD [3][4].…”

Section: Introductionmentioning

confidence: 99%

Integration of Time-Frequency Analysis and Regularization Technique for Improved Identification of Fetal Electrocardiogram

Ziani,

El Ghmary,

Achmad Rizal

2023

Int. J. Onl. Eng.

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This research article presents a novel methodology for effectively extracting the fetal electrocardiogram (FECG) from a single-channel signal acquired on the maternal abdomen. The signal comprises a mixture of the FECG, maternal electrocardiogram (MECG), and ambient noise. The central concept involves projecting the signal into higher-dimensional spaces and leveraging the assumption of statistical independence among the constituent components to achieve their separation from the mixture. To accomplish this, singular value decomposition (SVD) is initially applied to the spectrogram, followed by an iterative application of independent component analysis (ICA) on the principal components. The SVD technique contributes to the enhanced separability of each individual component, while ICA facilitates the promotion of statistical independence between the fetal and maternal ECGs. Furthermore, we refine and customize the aforementioned approach specifically for ECG signals by incorporating knowledge of the frequency distribution of the MECG and other inherent ECG characteristics. The effectiveness of the proposed methodology is validated through comprehensive experimental studies, demonstrating its superior accuracy and performance compared to existing techniques.

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A New Approach for Extracting and Characterizing Fetal Electrocardiogram

Cited by 15 publications

References 0 publications

Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

Extraction of Fetal Electrocardiogram by Combining Deep Learning and SVD-ICA-NMF Methods

Improved Methods for Automatic Facial Expression Recognition

Integration of Time-Frequency Analysis and Regularization Technique for Improved Identification of Fetal Electrocardiogram

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