ECG signal denoising by Functional Link Artificial Neural Network (FLANN)

Dey, Nibedit; Dash, Tripada Prasad; Dash, Sriram

doi:10.1504/ijbet.2011.044416

Cited by 18 publications

(4 citation statements)

References 17 publications

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“…Methods for fECG SNR improvement are described in [87] where a Functional Link Artificial Neural Network (FLANN) is proposed to remove the Gaussian and baseline wander noise. Zhang and Benveniste [88] and Poungponsri and Yu [89] use NN combined with Wavelet transform for better results.…”

Section: Methodsmentioning

confidence: 99%

“…The classical application of NN in cardiac signals processing is the classification of ECG signals, pattern recognition [ 84 , 85 ], and fECG extraction from ADS [ 86 ]. Methods for fECG SNR improvement are described in [ 87 ] where a Functional Link Artificial Neural Network (FLANN) is proposed to remove the Gaussian and baseline wander noise. Zhang and Benveniste [ 88 ] and Poungponsri and Yu [ 89 ] use NN combined with Wavelet transform for better results.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics

Ţarălungă

Ungureanu

Gussi

et al. 2014

Computational and Mathematical Methods in Medicine

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Interference of power line (PLI) (fundamental frequency and its harmonics) is usually present in biopotential measurements. Despite all countermeasures, the PLI still corrupts physiological signals, for example, electromyograms (EMG), electroencephalograms (EEG), and electrocardiograms (ECG). When analyzing the fetal ECG (fECG) recorded on the maternal abdomen, the PLI represents a particular strong noise component, being sometimes 10 times greater than the fECG signal, and thus impairing the extraction of any useful information regarding the fetal health state. Many signal processing methods for cancelling the PLI from biopotentials are available in the literature. In this review study, six different principles are analyzed and discussed, and their performance is evaluated on simulated data (three different scenarios), based on five quantitative performance indices.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics

Ţarălungă

Ungureanu

Gussi

et al. 2014

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

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“…Deep learning has attracted more and more in-depth research studies in the field of ECG noise reduction. The representative ones are the Functional Link Neural Network (FLNN) [20], Wavelet Neural Network (WNN) [21], and Denoising Autoencoder (DAE) [22]. Among them, FLNN and WNN are the most widely used, but they can remove only one type of ECG signal.…”

Section: Introductionmentioning

confidence: 99%

An ECG Denoising Method Based on the Generative Adversarial Residual Network

Liu

Shu

et al. 2021

Computational and Mathematical Methods in Medicine

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High-quality and high-fidelity removal of noise in the Electrocardiogram (ECG) signal is of great significance to the auxiliary diagnosis of ECG diseases. In view of the single function of traditional denoising methods and the insufficient performance of signal details after denoising, a new method of ECG denoising based on the combination of the Generative Adversarial Network (GAN) and Residual Network is proposed. The method adopted in this paper is based on the GAN structure, and it restructures the generator and discriminator. In the generator network, residual blocks and Skip-Connecting are used to deepen the network structure and better capture the in-depth information in the ECG signal. In the discriminator network, the ResNet framework is used. In order to optimize the noise reduction process and solve the lack of local relevance considering the global ECG problem, the differential function and overall function of the maximum local difference are added in the loss function in this paper. The experimental results prove that the method used in this article has better performance than the current excellent S-Transform (S-T) algorithm, Wavelet Transform (WT) algorithm, Stacked Denoising Autoencoder (S-DAE) algorithm, and Improved Denoising Autoencoder (I-DAE) algorithm. Experiments show that the Root Mean Square Error (RMSE) of this method in the Massachusetts Institute of Technology and Beth Israel Hospital (MIT-BIH) noise pressure database is 0.0102, and the Signal-to-Noise Ratio (SNR) is 40.8526 dB, which is compared with that of the most advanced experimental methods. Our method improves the SNR by 88.57% on average. Besides the three noise intensities for comparison experiments, additional noise reduction experiments are also performed under four noise intensities in our paper. The experimental results verify the scientific nature of the model, which is that our method can effectively retain the important information conveyed by the original signal.

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“…Salehizadeh et al reconstructed the heart rate signal corrupted by motion artifacts based on a novel time-varying spectral filtering algorithm [20]. Dey et al [21] and Poungponsri et al [22] used the artificial neural network approach to suppress various noises in ECG signals. All of the above three methods can suppress motion artifacts to a certain extent, where the first method is based on the generation mechanism of motion artifacts and does not need a complex algorithm or processing circuit.…”

Section: Introductionmentioning

confidence: 99%

Electrode Humidification Design for Artifact Reduction in Capacitive ECG Measurements

Tang

Chang

Zhang

et al. 2020

Sensors

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For wearable capacitive electrocardiogram (ECG) acquisition, capacitive electrodes may cause severe motion artifacts due to the relatively large friction between the electrodes and the dielectrics. In some studies, water can effectively suppress motion artifacts, but these studies lack a complete analysis of how water can suppress motion artifacts. In this paper, the effect of water on charge decay of textile electrode is studied systematically, and an electrode controllable humidification design using ultrasonic atomization is proposed to suppress motion artifacts. Compared with the existing electrode humidification designs, the proposed electrode humidification design can be controlled by a program to suppress motion artifacts at different ambient humidity, and can be highly integrated for wearable application. Firstly, the charge decay mode of the textile electrode is given and it is found that the process of free water evaporation at an appropriate free water content can be the dominant way of triboelectric charge dissipation. Secondly, theoretical analysis and experiment verification both illustrate that water contained in electrodes can accelerate the decay of triboelectric charge through the free water evaporation path. Finally, a capacitive electrode controllable humidification design is proposed by applying integrated ultrasonic atomization to generate atomized drops and spray them onto textile electrodes to accelerate the decay of triboelectric charge and suppress motion artifacts. The performance of the proposed design is verified by the experiment results, which shows that the proposed design can effectively suppress motion artifacts and maintain the stability of signal quality at both low and high ambient humidity. The signal-to-noise ratio of the proposed design is 33.32 dB higher than that of the non-humidified design at 25% relative humidity and is 22.67 dB higher than that of non-humidified electrodes at 65% relative humidity.

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ECG signal denoising by Functional Link Artificial Neural Network (FLANN)

Cited by 18 publications

References 17 publications

Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics

Fetal ECG Extraction from Abdominal Signals: A Review on Suppression of Fundamental Power Line Interference Component and Its Harmonics

An ECG Denoising Method Based on the Generative Adversarial Residual Network

Electrode Humidification Design for Artifact Reduction in Capacitive ECG Measurements

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