Dual-modal and multi-scale deep neural networks for sleep staging using EEG and ECG signals

BACKGROUND: The distribution of pulmonary vessels in computed tomography (CT) and computed tomography angiography (CTA) images of lung is important for diagnosing disease, formulating surgical plans and pulmonary research. PURPOSE: Based on the pulmonary vascular segmentation task of International Symposium on Image Computing and Digital Medicine 2020 challenge, this paper reviews 12 different pulmonary vascular segmentation algorithms of lung CT and CTA images and then objectively evaluates and compares their performances. METHODS: First, we present the annotated reference dataset of lung CT and CTA images. A subset of the dataset consisting 7,307 slices for training and 3,888 slices for testing was made available for participants. Second, by analyzing the performance comparison of different convolutional neural networks from 12 different institutions for pulmonary vascular segmentation, the reasons for some defects and improvements are summarized. The models are mainly based on U-Net, Attention, GAN, and multi-scale fusion network. The performance is measured in terms of Dice coefficient, over segmentation ratio and under segmentation rate. Finally, we discuss several proposed methods to improve the pulmonary vessel segmentation results using deep neural networks. RESULTS: By comparing with the annotated ground truth from both lung CT and CTA images, most of 12 deep neural network algorithms do an admirable job in pulmonary vascular extraction and segmentation with the dice coefficients ranging from 0.70 to 0.85. The dice coefficients for the top three algorithms are about 0.80. CONCLUSIONS: Study results show that integrating methods that consider spatial information, fuse multi-scale feature map, or have an excellent post-processing to deep neural network training and optimization process are significant for further improving the accuracy of pulmonary vascular segmentation.

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An End-to-End Sleep Staging Simulator Based on Mixed Deep Neural Networks

10.1109/bibm52615.2021.9669384 ◽

2021 ◽

Author(s):

Zheng Chen ◽

Ziwei Yang ◽

Dong Wang ◽

Ming Huang ◽

Naoaki Ono ◽

...

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Sleep Staging ◽

End To End

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Regional Multi-Scale Approach for Visually Pleasing Explanations of Deep Neural Networks

IEEE Access ◽

10.1109/access.2019.2963055 ◽

2020 ◽

Vol 8 ◽

pp. 8572-8582 ◽

Cited By ~ 3

Author(s):

Dasom Seo ◽

Kanghan Oh ◽

Il-Seok Oh

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

Multi Scale

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Multi-Scale, Multi-Object and Real-Time Face Detection and Head Pose Estimation Using Deep Neural Networks

The Journal of Korea Robotics Society ◽

10.7746/jkros.2017.12.3.313 ◽

2017 ◽

Vol 12 (3) ◽

pp. 313-321 ◽

Cited By ~ 1

Author(s):

Byungtae Ahn ◽

◽

Dong-Geol Choi ◽

In So Kweon

Keyword(s):

Neural Networks ◽

Real Time ◽

Face Detection ◽

Pose Estimation ◽

Deep Neural Networks ◽

Head Pose Estimation ◽

Head Pose ◽

Multi Scale

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Automated Recognition of Arrhythmia Using Deep Neural Networks for 12-Lead Electrocardiograms with Fractional Time–Frequency Domain Extension

Journal of Medical Imaging and Health Informatics ◽

10.1166/jmihi.2020.3212 ◽

2020 ◽

Vol 10 (11) ◽

pp. 2764-2767

Author(s):

Chuanbin Ge ◽

Di Liu ◽

Juan Liu ◽

Bingshuai Liu ◽

Yi Xin

Keyword(s):

Neural Networks ◽

Frequency Domain ◽

Deep Neural Networks ◽

Fractional Fourier Transform ◽

Training Dataset ◽

Clinical Tool ◽

Time Frequency ◽

Ecg Signals ◽

Physiological Signal ◽

Ecg Data

Arrhythmia is a group of conditions in which the heartbeat is irregular. There are many types of arrhythmia. Some can be life-threatening. Electrocardiogram (ECG) is an effective clinical tool used to diagnosis arrhythmia. Automatic recognition of different arrhythmia types in ECG signals has become an important and challenging issue. In this article, we proposed an algorithm to detect arrhythmia in 12-lead ECG signals and classify signals into 9 categories. Two 19-layer deep neural networks combining convolutional neural network and gated recurrent unit were proposed to realize this work. The first one was trained directly with the raw 12-lead ECG data while the other one was trained with an 18-"lead" ECG data, where the six extra leads containing morphology information in fractional time–frequency domain were generated utilizing fractional Fourier transform (FRFT). Overall detection results were obtained by fusing the output of these two networks and the final classification results on the testing dataset reports our proposed algorithm obtained a F1 score of 0.855. Furthermore, with our proposed algorithm, a better F1 score 0.81 was attained using training dataset provided by the China Physiological Signal Challenge held in 2018.

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Integrating ECG Monitoring and Classification via IoT and Deep Neural Networks

Biosensors ◽

10.3390/bios11060188 ◽

2021 ◽

Vol 11 (6) ◽

pp. 188

Author(s):

Li-Ren Yeh ◽

Wei-Chin Chen ◽

Hua-Yan Chan ◽

Nan-Han Lu ◽

Chi-Yuan Wang ◽

...

Keyword(s):

Neural Networks ◽

Real Time ◽

Deep Neural Networks ◽

Network Models ◽

Kappa Statistics ◽

Neural Network Models ◽

Ecg Signals ◽

St Depression ◽

Deep Model ◽

St Elevation

Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG) signals to assess the patient’s condition and give appropriate medications. However, it is not easy to interpret the ECG signals. Even physicians with more than 10 years of clinical experience may still misjudge. Therefore, this study uses convolutional neural networks to classify ECG image types to assist in anesthesia assessment. The research uses Internet of Things (IoT) technology to develop ECG signal measurement prototypes. At the same time, it classifies signal types through deep neural networks, divided into QRS widening, sinus rhythm, ST depression, and ST elevation. Three models, ResNet, AlexNet, and SqueezeNet, are developed with 50% of the training set and test set. Finally, the accuracy and kappa statistics of ResNet, AlexNet, and SqueezeNet in ECG waveform classification were (0.97, 0.96), (0.96, 0.95), and (0.75, 0.67), respectively. This research shows that it is feasible to measure ECG in real time through IoT and then distinguish four types through deep neural network models. In the future, more types of ECG images will be added, which can improve the real-time classification practicality of the deep model.

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Deep neural networks to downscale ocean climate models

10.5194/egusphere-egu2020-20177 ◽

2020 ◽

Author(s):

Marie Déchelle-Marquet ◽

Marina Levy ◽

Patrick Gallinari ◽

Michel Crepon ◽

Sylvie Thiria

Keyword(s):

Neural Networks ◽

Deep Learning ◽

High Resolution ◽

Deep Neural Networks ◽

Climate Models ◽

Ocean Currents ◽

Sea Surface ◽

Low Resolution ◽

Ocean Climate ◽

Multi Scale

<p>Ocean currents are a major source of impact on climate variability, through the heat transport they induce for instance. Ocean climate models have quite low resolution of about 50 km. Several dynamical processes such as instabilities and filaments which have a scale of 1km have a strong influence on the ocean state. We propose to observe and model these fine scale effects by a combination of satellite high resolution SST observations (1km resolution, daily observations) and mesoscale resolution altimetry observations (10km resolution, weekly observations) with deep neural networks. Whereas the downscaling of climate models has been commonly addressed with assimilation approaches, in the last few years neural networks emerged as powerful multi-scale analysis method. Besides, the large amount of available oceanic data makes attractive the use of deep learning to bridge the gap between scales variability.</p><p>This study aims at reconstructing the multi-scale variability of oceanic fields, based on the high resolution NATL60 model of ocean observations at different spatial resolutions: low-resolution sea surface height (SSH) and high resolution SST. As the link between residual neural networks and dynamical systems has recently been established, such a network is trained in a supervised way to reconstruct the high variability of SSH and ocean currents at submesoscale (a few kilometers). To ensure the conservation of physical aspects in the model outputs, physical knowledge is incorporated into the deep learning models training. Different validation methods are investigated and the model outputs are tested with regards to their physical plausibility. The method performance is discussed and compared to other baselines (namely convolutional neural network). The generalization of the proposed method on different ocean variables such as sea surface chlorophyll or sea surface salinity is also examined.</p>

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Expert-level sleep scoring with deep neural networks

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocy131 ◽

2018 ◽

Vol 25 (12) ◽

pp. 1643-1650 ◽

Cited By ~ 49

Author(s):

Siddharth Biswal ◽

Haoqi Sun ◽

Balaji Goparaju ◽

M Brandon Westover ◽

Jimeng Sun ◽

...

Keyword(s):

Neural Networks ◽

Sleep Apnea ◽

Deep Neural Networks ◽

Training Data ◽

Sleep Stages ◽

Apnea Hypopnea Index ◽

Sleep Staging ◽

Limb Movements ◽

Sleep Scoring ◽

At Home

Abstract Objectives Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the complexity of PSG signals and physiological heterogeneity between patients. Deep neural networks, which have recently achieved expert-level performance for other complex medical tasks, are ideally suited to PSG scoring, given sufficient training data. Methods We used a combination of deep recurrent and convolutional neural networks (RCNN) for supervised learning of clinical labels designating sleep stages, sleep apnea events, and limb movements. The data for testing and training were derived from 10 000 clinical PSGs and 5804 research PSGs. Results When trained on the clinical dataset, the RCNN reproduces PSG diagnostic scoring for sleep staging, sleep apnea, and limb movements with accuracies of 87.6%, 88.2% and 84.7% on held-out test data, a level of performance comparable to human experts. The RCNN model performs equally well when tested on the independent research PSG database. Only small reductions in accuracy were noted when training on limited channels to mimic at-home monitoring devices: frontal leads only for sleep staging, and thoracic belt signals only for the apnea-hypopnea index. Conclusions By creating accurate deep learning models for sleep scoring, our work opens the path toward broader and more timely access to sleep diagnostics. Accurate scoring automation can improve the utility and efficiency of in-lab and at-home approaches to sleep diagnostics, potentially extending the reach of sleep expertise beyond specialty clinics.

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