Multi-label arrhythmias classification is of great significance to the diagnosis of cardiovascular disease,and it is a challenging task as it requires identifying the label subset most related to each instance.In this...Multi-label arrhythmias classification is of great significance to the diagnosis of cardiovascular disease,and it is a challenging task as it requires identifying the label subset most related to each instance.In this paper,by integrating a deep residual neural network and auto-encoder,we propose an advanced deep neural network(DNN)framework with unified feature-aware and label embedding to perform multi-label arrhythmias classification involving 30 types of arrhythmias.Firstly,a deep residual neural network is built to extract the complex pathological features from varying-dimensional electrocardiograms(ECGs).Secondly,the mean square error loss is adopted to learn a latent space associating the deep pathological features and the corresponding label data,and then to achieve unified feature-label embedding.Thirdly,the label-correlation aware loss is introduced to optimize the auto-encoder architecture,which enables our model to exploit labelcorrelation for improved multi-label prediction.Our proposed DNN model can allow end-to-end training and prediction,which can perform feature-aware,label embedding,and label-correlation aware prediction in a unified framework.Finally,our proposed model is evaluated on the currently largest public dataset worldwide,and achieves the challenge metric scores of 0.492,0.495,and 0.490 on the 12-lead,3-lead,and all-lead version ECGs,respectively.The performance of our approach outperforms other current state-of-the-art methods in the leave-one-dataset-out cross-validation setting,which demonstrates that our approach has great competitiveness in identifying a wider range of multi-label arrhythmias.展开更多
Let G=(V,E)be a graph.For a vertex labeling f:V→Z2,it induces an edge labeling f+:E→Z2,where for each edge v1 v2∈E we have f+(v1 v2)=f(v1)+f(v2).For each i∈Z2,we use vf(i)(respectively,ef(i))to denote the number o...Let G=(V,E)be a graph.For a vertex labeling f:V→Z2,it induces an edge labeling f+:E→Z2,where for each edge v1 v2∈E we have f+(v1 v2)=f(v1)+f(v2).For each i∈Z2,we use vf(i)(respectively,ef(i))to denote the number of vertices(respectively,edges)with label i.A vertex labeling f of G is said to be friendly if vertices with different labels differ in size by at most one.The full friendly index set of a graph G,denoted by F F I(G),consists of all possible values of ef(1)-ef(0),where f ranges over all friendly labelings of G.In this paper,motivated by a problem raised by[6],we study the full friendly index sets of a family of cubic graphs.展开更多
Event detection(ED)seeks to recognize event triggers and classify them into the predefined event types.Chinese ED is formulated as a character-level task owing to the uncertain word boundaries.Prior methods try to inc...Event detection(ED)seeks to recognize event triggers and classify them into the predefined event types.Chinese ED is formulated as a character-level task owing to the uncertain word boundaries.Prior methods try to incorpo-rate word-level information into characters to enhance their semantics.However,they experience two problems.First,they fail to incorporate word-level information into each character the word encompasses,causing the insufficient word-charac-ter interaction problem.Second,they struggle to distinguish events of similar types with limited annotated instances,which is called the event confusing problem.This paper proposes a novel model named Label-Aware Heterogeneous Graph Attention Network(L-HGAT)to address these two problems.Specifically,we first build a heterogeneous graph of two node types and three edge types to maximally preserve word-character interactions,and then deploy a heterogeneous graph attention network to enhance the semantic propagation between characters and words.Furthermore,we design a pushing-away game to enlarge the predicting gap between the ground-truth event type and its confusing counterpart for each character.Experimental results show that our L-HGAT model consistently achieves superior performance over prior competitive methods.展开更多
基金supported by the National Natural Science Foundation of China(Nos.U21A20447 and 62331025)National Key Research and Development Program of China(No.2021YFC3002204)+4 种基金Pilots Precise Selection and Performance Improvement Special Project(No.2019ZTB02)Special Equipment Scientific Research Key Project(No.LB2020LA060003)CAMS Innovation Fund for Medical Sciences(No.2019-I2M-5-019)Scientific Research Foundation Project Funded by Education Department of Yunnan Province(No.2024J0134)Yunnan Fundamental Research Projects(No.202401CF070028).
文摘Multi-label arrhythmias classification is of great significance to the diagnosis of cardiovascular disease,and it is a challenging task as it requires identifying the label subset most related to each instance.In this paper,by integrating a deep residual neural network and auto-encoder,we propose an advanced deep neural network(DNN)framework with unified feature-aware and label embedding to perform multi-label arrhythmias classification involving 30 types of arrhythmias.Firstly,a deep residual neural network is built to extract the complex pathological features from varying-dimensional electrocardiograms(ECGs).Secondly,the mean square error loss is adopted to learn a latent space associating the deep pathological features and the corresponding label data,and then to achieve unified feature-label embedding.Thirdly,the label-correlation aware loss is introduced to optimize the auto-encoder architecture,which enables our model to exploit labelcorrelation for improved multi-label prediction.Our proposed DNN model can allow end-to-end training and prediction,which can perform feature-aware,label embedding,and label-correlation aware prediction in a unified framework.Finally,our proposed model is evaluated on the currently largest public dataset worldwide,and achieves the challenge metric scores of 0.492,0.495,and 0.490 on the 12-lead,3-lead,and all-lead version ECGs,respectively.The performance of our approach outperforms other current state-of-the-art methods in the leave-one-dataset-out cross-validation setting,which demonstrates that our approach has great competitiveness in identifying a wider range of multi-label arrhythmias.
基金Supported by the National Natural Science Foundation of China(Grant No.11801149)Doctoral Fund of Henan Polytechnic University(Grant No.B2018-55)。
文摘Let G=(V,E)be a graph.For a vertex labeling f:V→Z2,it induces an edge labeling f+:E→Z2,where for each edge v1 v2∈E we have f+(v1 v2)=f(v1)+f(v2).For each i∈Z2,we use vf(i)(respectively,ef(i))to denote the number of vertices(respectively,edges)with label i.A vertex labeling f of G is said to be friendly if vertices with different labels differ in size by at most one.The full friendly index set of a graph G,denoted by F F I(G),consists of all possible values of ef(1)-ef(0),where f ranges over all friendly labelings of G.In this paper,motivated by a problem raised by[6],we study the full friendly index sets of a family of cubic graphs.
基金This work was supported by the National Key Research and Development Program of China under Grant No.2021YFB3100600the Youth Innovation Promotion Association of Chinese Academy of Sciences under Grant No.2021153the State Key Program of National Natural Science Foundation of China under Grant No.U2336202.
文摘Event detection(ED)seeks to recognize event triggers and classify them into the predefined event types.Chinese ED is formulated as a character-level task owing to the uncertain word boundaries.Prior methods try to incorpo-rate word-level information into characters to enhance their semantics.However,they experience two problems.First,they fail to incorporate word-level information into each character the word encompasses,causing the insufficient word-charac-ter interaction problem.Second,they struggle to distinguish events of similar types with limited annotated instances,which is called the event confusing problem.This paper proposes a novel model named Label-Aware Heterogeneous Graph Attention Network(L-HGAT)to address these two problems.Specifically,we first build a heterogeneous graph of two node types and three edge types to maximally preserve word-character interactions,and then deploy a heterogeneous graph attention network to enhance the semantic propagation between characters and words.Furthermore,we design a pushing-away game to enlarge the predicting gap between the ground-truth event type and its confusing counterpart for each character.Experimental results show that our L-HGAT model consistently achieves superior performance over prior competitive methods.
