Heart Anomaly Detection Algorithm Based on Multimodal Feature Engineering and TSNet
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NETL
NSTL
万方数据
心电图(electrocardiogram,ECG)和心音图(phonocardiogram,PCG)是心脏疾病诊断中常用的图像,单一的方法进行心脏疾病诊断效果不佳.基于多模态特征工程,数据集经过切分和归一化预处理后,使用格拉姆角场(Gramian angle fields,GAF)进行时间序列数据重建,形成图像模型.提出一种适用于该图像模型的双流自融合网络(two-stream self-fusion network,TSNet),使用双流自融合(two-stream self-fusion,TS)模块替代底层卷积操作,更好地融合ECG和PCG的异构信息.经Physio Net Challenge 2016 a数据集测试,该算法的准确率、F1值、精确率和召回率最佳值分别达到95.3%,95.4%,96.2%,99.4%,相较其他心电和心音多模态卷积神经网络算法,精度更高.
Electrocardiogram(ECG)and Phonocardiogram(PCG)are commonly used diagrams in heart diseases diagnosis.While,using them alone for heart disease diagnosis is not effective.Based on multimodal feature engineering,after segmentation and normalization preprocess of the dataset,Gramiam angle fields(GAF)are used for time-series data reconstruction to form an image model.Additionally,a two-stream self-fusion network(TSNet)suitable for this image model is proposed,which replaces the bottom-layer convolution operations with a two-stream self-fusion(TS)module to better integrate the heterogeneous information of ECG and PCG.Tested on the PhysioNet Challenge 2016 a dataset,the proposed algorithm achieves best values of accuracy,F1 score,precision,and recall at 95.3%,95.4%,96.2%,and 99.4%,respectively.Compared to other multimodal convolutional neural network algorithms for ECG and PCG,it shows higher accuracy.
NETL
NSTL
万方数据
