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DeepSleepNet: A Model for Automatic Sleep Stage Scoring Based on Raw Single-Channel EEG
- A. Supratak, Hao Dong, Chao Wu, Yike Guo
- Computer Science, MathematicsIEEE Transactions on Neural Systems and…
- 12 March 2017
This paper proposes a deep learning model, named DeepSleepNet, for automatic sleep stage scoring based on raw single-channel EEG, and utilizes convolutional neural networks to extract time-invariant features, and bidirectional-long short-term memory to learn transition rules among sleep stages automatically from EEG epochs.
Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks
- Orestis Tsinalis, P. Matthews, Yike Guo, S. Zafeiriou
- Computer Science, MathematicsArXiv
- 5 October 2016
It is shown that, without using prior domain knowledge, a CNN can automatically learn to distinguish among different normal sleep stages, and these results are comparable to state-of-the-art methods with hand-engineered features.
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction
- Guang Yang, Simiao Yu, +8 authors D. Firmin
- Computer Science, MedicineIEEE Transactions on Medical Imaging
- 1 June 2018
This paper provides a deep learning-based strategy for reconstruction of CS-MRI, and bridges a substantial gap between conventional non-learning methods working only on data from a single image, and prior knowledge from large training data sets.
Semantic Image Synthesis via Adversarial Learning
- Hao Dong, Simiao Yu, Chao Wu, Yike Guo
- Computer ScienceIEEE International Conference on Computer Vision…
- 21 July 2017
An end-to-end neural architecture that leverages adversarial learning to automatically learn implicit loss functions, which are optimized to fulfill the aforementioned two requirements of being realistic while matching the target text description.
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
This study proposes a fully automatic method for brain tumor segmentation, which is developed using U-Net based deep convolutional networks, which was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, showing that it can obtain promising segmentation efficiently.
Lightweight Resource Scaling for Cloud Applications
- Rui Han, Li Guo, M. Ghanem, Yike Guo
- Computer Science12th IEEE/ACM International Symposium on Cluster…
- 13 May 2012
This paper proposes a lightweight approach to enable cost-effective elasticity for cloud applications that operates fine-grained scaling at the resource level itself (CPUs, memory, I/O, etc) in addition to VM-level scaling.
Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders
- Orestis Tsinalis, P. Matthews, Yike Guo
- Computer Science, MedicineAnnals of Biomedical Engineering
- 13 October 2015
A machine learning methodology for automatic sleep stage scoring that has both high overall accuracy and high mean $$F_1$$F1-score and mean accuracy across individual sleep stages over all subjects and is a suitable candidate for longitudinal monitoring using wearable EEG in real-world settings.
Deep Sequence Learning with Auxiliary Information for Traffic Prediction
This paper intends to improve traffic prediction by appropriate integration of three kinds of implicit but essential factors encoded in auxiliary information within an encoder-decoder sequence learning framework that integrates the following data: offline geographical and social attributes.
GDPR-Compliant Personal Data Management: A Blockchain-based Solution
The goals of the platform are to provide decentralised mechanisms to both service providers and data owners for processing personal data; meanwhile, empower data provenance and transparency by leveraging advanced features of the blockchain technology.
Mixed Neural Network Approach for Temporal Sleep Stage Classification
- Hao Dong, A. Supratak, W. Pan, Chao Wu, P. Matthews, Yike Guo
- Psychology, BiologyIEEE Transactions on Neural Systems and…
- 15 October 2016
A comfortable configuration of a single-channel EEG on the forehead is found and it can be integrated with additional electrodes for simultaneous recording of the electro-oculogram, and use of this recording configuration with neural network deconvolution promises to make clinically indicated home sleep studies practical.