Deep Convolutional Encoder-Decoder Framework for Fetal ECG Signal Denoising

Fotiadou, E.; Konopczyński, Tomasz; Hesser, Jürgen; Vullings, Rik

doi:10.22489/cinc.2019.015

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…In the field of deep learning, an ECG denoising method based on the autoencoder model [22] was more popular. An autoencoder composed of eight convolution blocks and eight deconvolution blocks was proposed by Eleni et al [23], which can effectively learn the characteristics of ECGs and remove noise. As one of the many breakthroughs in deep learning technology, a generative adversarial network (GAN) had been widely used.…”

Section: The Traditional Ecg Denoising Methodsmentioning

confidence: 99%

ECG Signal Denoising Method Based on Disentangled Autoencoder

Lin

Liu

2023

Electronics

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The electrocardiogram (ECG) is widely used in medicine because it can provide basic information about different types of heart disease. However, ECG data are usually disturbed by various types of noise, which can lead to errors in diagnosis by doctors. To address this problem, this study proposes a method for denoising ECG based on disentangled autoencoders. A disentangled autoencoder is an improved autoencoder suitable for denoising ECG data. In our proposed method, we use a disentangled autoencoder model based on a fully convolutional neural network to effectively separate the clean ECG data from the noise. Unlike conventional autoencoders, we disentangle the features of the coding hidden layer to separate the signal-coding features from the noise-coding features. We performed simulation experiments on the MIT-BIH Arrhythmia Database and found that the algorithm had better noise reduction results when dealing with four different types of noise. In particular, using our method, the average improved signal-to-noise ratios for the three noises in the MIT-BIH Noise Stress Test Database were 27.45 db for baseline wander, 25.72 db for muscle artefacts, and 29.91 db for electrode motion artefacts. Compared to a denoising autoencoder based on a fully convolutional neural network (FCN), the signal-to-noise ratio was improved by an average of 12.57%. We can conclude that the model has scientific validity. At the same time, our noise reduction method can effectively remove noise while preserving the important information conveyed by the original signal.

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Section: The Traditional Ecg Denoising Methodsmentioning

confidence: 99%

ECG Signal Denoising Method Based on Disentangled Autoencoder

Lin

Liu

2023

Electronics

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“…A residual convolutional encoder-decoder network was employed to extract features and estimate fetal ECG signals. In another work, Fotiadou et al [12], [13] used a deep learning auto-encoder model on multi-channel data in order to denoise ECG signals. Fotiadou et al [12], [13] used deep learning to extract FECG signals or denoise AECG signals, and they do not need the exact location of maternal or fetal QRS in ECG signals.…”

Section: Related Workmentioning

confidence: 99%

A Dual Attention-Based Autoencoder Model for Fetal ECG Extraction From Abdominal Signals

Ghonchi

Abolghasemi

2022

IEEE Sensors J.

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Fetal electrocardiogram (FECG) signals contain important information about the conditions of the fetus during pregnancy. Currently, pure FECG signals can only be obtained through an invasive acquisition process which is life-threatening to both mother and fetus. In this study, single-channel ECG signals from the mother's abdomen are analysed with the aim of extracting the clean FECG waveform. This is a challenging task due to the very low amplitude of the FECG, various noises involved in the signal acquisition, and the overlap of R waves. To address this problem, we propose a novel convolutional auto-encoder network architecture to learn and extract the FECG patterns. The proposed model is equipped with a dual attention model, composed of squeeze-and-excitation and channel-wise modules, in the encoder and decoder blocks, respectively. It also benefits from a bidirectional long short-term memory (LSTM) layer. This combination allows the proposed network to accurately attend to and extract FECG signals from abdominal data. Three well-established datasets are considered in our experiments. The obtained results of FECG extraction are promising and confirm the effectiveness of using attention modules within the deep learning model. The results also suggest that the proposed auto-encoder network can accurately extract the fetal ECG signals where no information about maternal ECG is available.

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“…Motivated by the successful application of DAE in diverse areas such as speech recognition [43][44][45][46], human activity recognition [47], analysis of biomedical data such as ECGs [48][49][50], music source separation [51], energy load forecasting [52] and computer vision [53][54][55][56], a number of scholars in the PHM community started to adopt DAE to denoise sensor data. For example, Meng et al [57] proposed a modified version of the classical DAE for rolling bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%