Fetal QRS Detection Based on Convolutional Neural Networks in Noninvasive Fetal Electrocardiogram

Lee, Jun Seong; Seo, Minseok; Kim, Sang Woo; Choi, M. Y.

doi:10.1109/icfsp.2018.8552074

Cited by 29 publications

(19 citation statements)

References 11 publications

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“…The CNN is a specialized deep neural network for processing 1D time series and 2D images [24]. In this study, the CNN consisted of convolutional (Conv), max-pooling, fully connected (FC), local response normalization (LRN), dropout, and softmax layers and a rectified liner unit (ReLU), as shown in Figure 4.…”

Section: Methodsmentioning

confidence: 99%

“…Every Conv and every FC were followed by a ReLU [24, 30] which could be activated to speed up the training process. Behind a ReLU, the LRN layer detected high-frequency features and assigned them with large weights [31].…”

Section: Methodsmentioning

confidence: 99%

“…The convolutional neural network (CNN) has recently been applied to obstetrics and gynecology [21] for classification of the fetal heart rate [22], electromyography [23], and electrocardiogram signals [24]. The CNN is a type of machine learning which can classify images and time series without additional feature extraction and selection and produce state-of-the-art recognition results.…”

Section: Introductionmentioning

confidence: 99%

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Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks

Jin

Hao

Liu

et al. 2019

BioMed Research International

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Background Uterine contraction (UC) is the tightening and shortening of the uterine muscles which can indicate the progress of pregnancy towards delivery. Electrohysterogram (EHG), which reflects uterine electrical activities, has recently been studied for UC monitoring. In this paper, we aimed to evaluate different EHG segments for recognizing UCs using the convolutional neural network (CNN). Materials and Methods In the open-access Icelandic 16-electrode EHG database (122 recordings from 45 pregnant women), 7136 UC and 7136 non-UC EHG segments with the duration of 60 s were manually extracted from 107 recordings of 40 pregnant women to develop a CNN model. A fivefold cross-validation was applied to evaluate the CNN based on sensitivity (SE), specificity (SP), and accuracy (ACC). Then, 1056 UC and 1056 non-UC EHG segments were extracted from the other 15 recordings of 5 pregnant women. Furthermore, the developed CNN model was applied to identify UCs using different EHG segments with the durations of 10 s, 20 s, and 30 s. Results The CNN achieved the average SE, SP, and ACC of 0.82, 0.93, and 0.88 for a 60 s EHG segment. The EHG segments of 10 s, 20 s, and 30 s around the TOCO peak achieved higher SE and ACC than the other segments with the same duration. The values of SE from 20 s EHG segments around the TOCO peak were higher than those from 10 s to 30 s EHG segments on the same side of the TOCO peak. Conclusion The proposed method could be used to determine the efficient EHG segments for recognizing UC with the CNN.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks

Jin

Hao

Liu

et al. 2019

BioMed Research International

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“…CNNs detect fetal QRS (fQRS) complexes based only on non-invasive fetal ECG. Zhong et al [10] designed a 2-D CNN model with three convolutional layers to extract features from single-channel fetal ECG signals, while the model developed by Lee et al [11] has a deeper architecture and uses multi-channel signals, which lead to the performance improvement. Besides, the time-frequency representation of the abdominal ECG recordings is also applied to feed to a CNN model [12].…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram

Rahmani

et al. 2020

Sensors

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The invasive method of fetal electrocardiogram (fECG) monitoring is widely used with electrodes directly attached to the fetal scalp. There are potential risks such as infection and, thus, it is usually carried out during labor in rare cases. Recent advances in electronics and technologies have enabled fECG monitoring from the early stages of pregnancy through fECG extraction from the combined fetal/maternal ECG (f/mECG) signal recorded non-invasively in the abdominal area of the mother. However, cumbersome algorithms that require the reference maternal ECG as well as heavy feature crafting makes out-of-clinics fECG monitoring in daily life not yet feasible. To address these challenges, we proposed a pure end-to-end deep learning model to detect fetal QRS complexes (i.e., the main spikes observed on a fetal ECG waveform). Additionally, the model has the residual network (ResNet) architecture that adopts the novel 1-D octave convolution (OctConv) for learning multiple temporal frequency features, which in turn reduce memory and computational cost. Importantly, the model is capable of highlighting the contribution of regions that are more prominent for the detection. To evaluate our approach, data from the PhysioNet 2013 Challenge with labeled QRS complex annotations were used in the original form, and the data were then modified with Gaussian and motion noise, mimicking real-world scenarios. The model can achieve a F1 score of 91.1% while being able to save more than 50% computing cost with less than 2% performance degradation, demonstrating the effectiveness of our method.

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“…Recently, deep neural network models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and stacked denoising autoencoders have been successfully applied for a variety of purposes including signal and image denoising (9)(10)(11)(12)(13). Moreover, few works reported adult ECG signal denoising (14,15), fetal QRS detection (16,17), and fetal ECG signal reconstruction (18). Zhong et al (19) presented a deep convolutional encoder-decoder framework for preprocessing abdominal recordings to remove noise.…”

Section: Introductionmentioning

confidence: 99%

Multi-Channel Fetal ECG Denoising With Deep Convolutional Neural Networks

Fotiadou

Vullings

2020

Front. Pediatr.

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Non-invasive fetal electrocardiography represents a valuable alternative continuous fetal monitoring method that has recently received considerable attention in assessing fetal health. However, the non-invasive fetal electrocardiogram (ECG) is typically severely contaminated by a considerable amount of various noise sources, rendering fetal ECG denoising a very challenging task. This work employs a deep learning approach for removing the residual noise from multi-channel fetal ECG after the maternal ECG has been suppressed. We propose a deep convolutional encoder-decoder network with symmetric skip-layer connections, learning end-to-end mappings from noise-corrupted fetal ECG signals to clean ones. Experiments on simulated data show an average signal-to-noise ratio (SNR) improvement of 9.5 dB for fetal ECG signals with input SNR ranging between −20 and 20 dB. The method is additionally evaluated on a large set of real signals, demonstrating that it can provide significant quality improvement of the noisy fetal ECG signals. We further show that employment of multi-channel signal information by the network provides superior and more reliable performance as opposed to its single-channel network counterpart. The presented method is able to preserve beat-to-beat morphological variations and does not require any prior information on the power spectra of the noise or the pulse location.

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Fetal QRS Detection Based on Convolutional Neural Networks in Noninvasive Fetal Electrocardiogram

Cited by 29 publications

References 11 publications

Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks

Preliminary Study on the Efficient Electrohysterogram Segments for Recognizing Uterine Contractions with Convolutional Neural Networks

An Efficient and Robust Deep Learning Method with 1-D Octave Convolution to Extract Fetal Electrocardiogram

Multi-Channel Fetal ECG Denoising With Deep Convolutional Neural Networks

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