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Electrical status epilepticus during sleep electroencephalogram waveform identification and analysis based on a graph convolutional neural network

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2022年第0期77卷 000-000页

作者： Meng, Lu Hu, Jinzhou Deng, Yu Hu, Yue Northeastern Univ Coll Informat Sci & Engn Shenyang Peoples R China Chongqing Med Univ Chongqing Peoples R China

Electrical status epilepticus during sleep (ESES) is an epileptic syndrome in which neurons in the brain continue to discharge during the sleep phase and is common in mid-childhood. Affected patients often experience a decline in cognition, learning ability, memory, and expressive language skills. Therefore, timely and accurate diagnosis can effectively protect the health of a patient. Currently, the identification and analysis of ESES activities mainly rely on manual detection or traditional matching learning algorithms, such as morphology and template matching. These algorithms are time-consuming or have low accuracy. Therefore, in this paper, we propose a graph convolutional neural network that can automatically and accurately identify ESES activity from non-ESES activity. We divide the whole EEG signal into small segments, each of which covers one second of the EEG data. Then, we construct a graph according to each segment of the EEG data and train a graph convolutional neural network to classify the graph into two categories: ESES or non-ESES. Compared with other state-of-the-art algorithms, for the proposed algorithm, the accuracy, F1-Score, Area Under Curve(AUC) and sensitivity reaches 91.2%, 95.0%, 96.5%, and 91.3%, respectively, and outperforms the other algorithms.

关键词： ESES Epilepsy graph convolution graph convolution neural network Automatic recognition Pearson correlation

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Point Cloud Classification network Based on graph convolution and Fusion Attention Mechanism

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Journal of Computer and Communications 2022年第9期10卷 81-95页

作者： Tengteng Song Zhao Li Zhenguo Liu Yizhi He School of Computer Science and Technology Shandong University of Technology Zibo China

The classification of point cloud data is the key technology of point cloud data information acquisition and 3D reconstruction, which has a wide range of applications. However, the existing point cloud classification methods have some shortcomings when extracting point cloud features, such as insufficient extraction of local information and overlooking the information in other neighborhood features in the point cloud, and not focusing on the point cloud channel information and spatial information. To solve the above problems, a point cloud classification network based on graph convolution and fusion attention mechanism is proposed to achieve more accurate classification results. Firstly, the point cloud is regarded as a node on the graph, the k-nearest neighbor algorithm is used to compose the graph and the information between points is dynamically captured by stacking multiple graph convolution layers;then, with the assistance of 2D experience of attention mechanism, an attention mechanism which has the capability to integrate more attention to point cloud spatial and channel information is introduced to increase the feature information of point cloud, aggregate local useful features and suppress useless features. Through the classification experiments on ModelNet40 dataset, the experimental results show that compared with PointNet network without considering the local feature information of the point cloud, the average classification accuracy of the proposed model has a 4.4% improvement and the overall classification accuracy has a 4.4% improvement. Compared with other networks, the classification accuracy of the proposed model has also been improved.

关键词： graph convolution neural network Attention Mechanism Modelnet40 Point Cloud Classification

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Identifying Experts in Community Question Answering Website Based on graph convolutional neural network

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IEEE ACCESS 2020年 8卷 137799-137811页

作者： Liu, Chen Hao, Yuchen Shan, Wei Dai, Zhihong Univ Shanghai Sci & Technol Sch Business Shanghai 200093 Peoples R China Beihang Univ Sch Econ & Management Beijing 100191 Peoples R China Minist Educ Key Lab Complex Syst Anal & Management Decis Beijing 100191 Peoples R China Shanghai Univ Finance & Econ Sch Informat Management & Engn Shanghai 200433 Peoples R China

High-quality answers, usually given by experts, play an important role in community question answering (CQA) websites. Therefore, experts in these websites are defined as those who provide high-quality answers. We propose two semi-supervised learning models based on the graph convolution neural network (GCN) to identify them. Both models comprehensively extract features from the social behavior network, user profiles, and question and answer text. Specifically, we construct a social behavior network according to the co-answering relationship among answerers, which means every two answerers are connected if they answer the same questions. The difference between these two models is the methods of extracting text features. One model named GCN-Doc uses Doc2vec to get text vectors before training. The other model named GCN-Lstm with a long short term memory (LSTM) network extracts text features while training. Experiments using real-world data from ***, one of the largest Chinese CQA websites, show that both GCN-Doc and GCN-Lstm can identify experts effectively comparing with baselines of PageRank and other GCN based neural network models. Besides, GCN-Lstm performs better than GCN-Doc.

关键词： Feature extraction Social network services Computational modeling Biological system modeling neural networks Knowledge engineering Data models Community question answering deep neural network expert identification graph convolution neural network

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Vision-Based Freezing of Gait Detection With Anatomic Directed graph Representation

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IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS 2020年第4期24卷 1215-1225页

作者： Hu, Kun Wang, Zhiyong Mei, Shaohui Martens, Kaylena A. Ehgoetz Yao, Tingting Lewis, Simon J. G. Feng, David Dagan Univ Sydney Sch Comp Sci Sydney NSW 2006 Australia Northwestern Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Univ Sydney Parkinsons Dis Res Clin Brain & Mind Ctr Sydney NSW 2050 Australia Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116000 Peoples R China

Parkinson's disease significantly impacts the life quality of millions of people around the world. While freezing of gait (FoG) is one of the most common symptoms of the disease, it is time consuming and subjective to assess FoG for well-trained experts. Therefore, it is highly desirable to devise computer-aided FoG detection methods for the purpose of objective and time-efficient assessment. In this paper, in line with the gold standard of FoG clinical assessment, which requires video or direct observation, we propose one of the first vision-based methods for automatic FoG detection. To better characterize FoG patterns, instead of learning an overall representation of a video, we propose a novel architecture of graph convolution neural network and represent each video as a directed graph where FoG related candidate regions are the vertices. A weakly-supervised learning strategy and a weighted adjacency matrix estimation layer are proposed to eliminate the resource expensive data annotation required for fully supervised learning. As a result, the interference of visual information irrelevant to FoG, such as gait motion of supporting staff involved in clinical assessments, has been reduced to improve FoG detection performance by identifying the vertices contributing to FoG events. To further improve the performance, the global context of a clinical video is also considered and several fusion strategies with graph predictions are investigated. Experimental results on more than 100 videos collected from 45 patients during a clinical assessment demonstrated promising performance of our proposed method with an AUC of 0.887.

关键词： Deep learning convolution Feature extraction Diseases Task analysis Computer architecture Estimation Parkinson's disease freezing of gait detection deep learning graph convolution neural network

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Geolocation using GAT with Multiview Learning

Geolocation using GAT with Multiview Learning

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IEEE International Conference on Smart Data Services (SMDS)

作者： Wang, Zhanyu Ye, Chunyang Zhou, Hui Hainan Univ Sch Comp Sci & Cyberspace Secur Haikou Hainan Peoples R China

ISBN: (纸本)9781728187778

Information in social networks plays an important role in many fields such as event detection, disaster warning, etc. However, due to the lack of geographic metadata, the information is often unusable. Therefore, the geolocation using social network data has gradually become a hot research topic. Existing methods mainly use textual contents, and thus poorly exploit the available data, especially the hidden information in the link. To address this issue, we propose two Multiview learning models, M-GAT and M-GCN, based on the graph Attention and graph convolution network to fuse both the text and link information. By extracting the text features from multiple angles to extend the feature space, our models achieve the best results on the baseline dataset. The visual display of representations collected from a hidden layer illustrates the validity of our models. Experiments on different feature combination show the effectiveness of our proposal.

关键词： graph Attention neural network graph convolution neural network Multiview learning Geolocation Social network

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GFCN: A NEW graph convolutionAL network BASED ON PARALLEL FLOWS

GFCN: A NEW GRAPH CONVOLUTIONAL NETWORK BASED ON PARALLEL FL...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Ji, Feng Yang, Jielong Zhang, Qiang Tay, Wee Peng Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

ISBN: (纸本)9781509066315

In view of the huge success of convolution neural networks (CNN) for image classification and object recognition, there have been attempts to generalize the method to general graph-structured data. One major direction is based on spectral graph theory. In this paper, we study the problem from a different perspective, by introducing parallel flow decomposition of graphs. The essential idea is to decompose a graph into families of non-intersecting one dimensional (1D) paths, after which, we may apply a 1D CNN along each family of paths. We demonstrate that the our method, which we call GFCN (graph flow convolutional network), is able to transfer CNN architectures to general graphs. We demonstrate effectiveness of the method with synthetic and real applications.

关键词： graph convolution neural network parallel flow graph signals

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HyGCN: A GCN Accelerator with Hybrid Architecture 26

HyGCN: A GCN Accelerator with Hybrid Architecture

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26th IEEE International Symposium on High Performance Computer Architecture (HPCA)

作者： Yan, Mingyu Deng, Lei Hu, Xing Liang, Ling Feng, Yujing Ye, Xiaochun Zhang, Zhimin Fan, Dongrui Xie, Yuan Chinese Acad Sci State Key Lab Comp Architecture Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing Peoples R China Univ Calif Santa Barbara Santa Barbara CA 93106 USA

ISBN: (纸本)9781728161495

Inspired by the great success of neural networks, graph convolutional neural networks (GCNs) are proposed to analyze graph data. GCNs mainly include two phases with distinct execution patterns. The Aggregation phase, behaves as graph processing, showing a dynamic and irregular execution pattern. The Combination phase, acts more like the neural networks, presenting a static and regular execution pattern. The hybrid execution patterns of GCNs require a design that alleviates irregularity and exploits regularity. Moreover, to achieve higher performance and energy efficiency, the design needs to leverage the high intra-vertex parallelism in Aggregation phase, the highly reusable inter-vertex data in Combination phase, and the opportunity to fuse phase-by-phase execution introduced by the new features of GCNs. However, existing architectures fail to address these demands. In this work, we first characterize the hybrid execution patterns of GCNs on Intel Xeon CPU. Guided by the characterization, we design a GCN accelerator, HyGCN, using a hybrid architecture to efficiently perform GCNs. Specifically, first, we build a new programming model to exploit the fine-grained parallelism for our hardware design. Second, we propose a hardware design with two efficient processing engines to alleviate the irregularity of Aggregation phase and leverage the regularity of Combination phase. Besides, these engines can exploit various parallelism and reuse highly reusable data efficiently. Third, we optimize the overall system via inter-engine pipeline for inter-phase fusion and priority based off-chip memory access coordination to improve offchip bandwidth utilization. Compared to the state-of-the-art software framework running on Intel Xeon CPU and NVIDIA V100 GPU, our work achieves on average 1509 x speedup with 2500 x energy reduction and average 6.5 x speedup with 10 x energy reduction, respectively.

关键词： Hardware Accelerator graph convolution neural network graph Processing Hybrid Execution Pattern

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Traffic Flow Prediction based on Dynamic Spatio-temporal convolution network Model of Reachability Matrix

International Core Journal of Engineering

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International Core Journal of Engineering 2021年第5期7卷 321-333页

作者： Leilei Zhang Wei Sun Wei Dong

The application of intelligent transportation system in urban transportation is more and more deeply. To achieve accurate traffic volume prediction has important practical significance for traffic service, traffic engineering planning and traffic risk prevention. Although the traffic volume in the road network has periodicity and correlation, traffic accidents and bad weather will cause the change of the road network structure, and the adjacent nodes show time correlation. In this paper, a graph convolution neural network model of dynamic spatio-temporal based on reachability matrix is proposed from the point of view of dynamic reachability between nodes in traffic network. Based on the convolution network of spatiotemporal graph, the dynamic reachability matrix module of parameter learning is added to express the dynamic and spatio-temporal correlation of road network. The experimental results on the open data set PEMs show that the model can effectively capture the dynamics of the road network, improve the accuracy of traffic volume prediction, and is superior to the existing baseline model.

关键词： Traffic Flow Prediction Dynamic Reachability Matrix graph convolution neural network Spatio-temporal Correlation Topological Structure

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Forecasting Retweet Count During Elections Using graph convolution neural networks 5

Forecasting Retweet Count During Elections Using Graph Convo...

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5th IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA)

作者： Vijayan, Raghavendran Mohler, George Indiana Univ Purdue Univ Indianapolis Dept Comp & Informat Sci Indianapolis IN 46202 USA

ISBN: (纸本)9781538650905

A retweet refers to sharing a tweet posted by another user on Twitter and is a primary way information spreads on the Twitter network. Political parties use Twitter extensively as a part of their campaign to promote their presence, announce their propaganda, and at times debating with opponents. In this work we consider the problem of early prediction of the final retweet count using information from the network during the first several minutes after a post is made. Such predictions are useful for ranking and promoting posts and also can be used in combination with fake news detection. From a machine learning perspective, the task can be viewed as a regression problem. We introduce a novel graph convolution neural network for forecasting retweet count that combines network level features through graph convolution layers as well as tweet level features at a higher dense layer in the network. We first will provide an overview of the graph convolution network architecture and then perform several experiments on Twitter data collected during presidential elections in South Africa (2014) and Kenya (2013). We show that the model outperforms baseline models including a feed-forward neural network and the popular point process based model SEISMIC.

关键词： graph convolution neural network Hawkes Process Twitter Election

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MSGCNN: MULTI-SCALE graph convolutionAL neural network FOR POINT CLOUD SEGMENTATION 5

MSGCNN: MULTI-SCALE GRAPH CONVOLUTIONAL NEURAL NETWORK FOR P...

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5th IEEE International Conference on Multimedia Big Data (BigMM)

作者： Xu, Mingxing Dai, Wenrui Shen, Yangmei Xiong, Hongkai Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China

ISBN: (纸本)9781728155272

Point cloud has emerged as a scalable and flexible geometric representation for 3D data. graph convolutional neural networks (GCNNs) have shown superior performance and robustness in point cloud processing with structure-awareness and permutation invariance. However, naive graph convolution networks are limited in point cloud segmentation tasks especially in the border areas of multiple segmentation instances due to the lack of multi-scale feature extraction ability. In this paper, we propose a novel multi-scale graph convolutional neural network (MSGCNN) to allow multi-scale feature learning for fine-grained point cloud segmentation. The proposed geometrical interpretable multi-scale point cloud processing framework is able to considerately enlarge the graph filters receptive fields and exploit discriminative multi scale structure-aware point features for the superior segmentation performance against naive graph convolution networks especially in border area. Experimental results for part segmentation task on ShapeNet datasets show that MSGCNN achieves competitive performance with state-of-the-arts. In comparison to naive graph convolution networks, MSGCNN is shown to obtain better visual quality in the border area. We further validate that our model is robust to data point missing and noise perturbation with the learned multi-scale structure aware point features.

关键词： Point cloud segmentation Multi-scale processing graph convolution neural network Robustness

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