An Ensemble Neural Network for Multi-label Classification of Electrocardiogram

An electrocardiogram (ECG) record potentially contains multiple abnormalities concurrently, therefore multi-label classification of ECG is significant in clinical scenarios. In this paper, we propose an ensemble neural network to address the multi-label classification of 12-lead ECG. The proposed network contains two modules, which treat the multi-label task from two different perspectives. The first module deals with the task in a sequence-generation manner by a novel encoder-decoder structure. The second module treats the multi-label problem as multiple binary classification tasks, by employing two convolutional neural networks of different structure. Finally, the predictions of two modules are integrated as the final result. Our method is trained and evaluated on the dataset provided by the First China ECG Intelligent Competition, and yields a Macro-\(F_1\) of 0.872 on the test set.

[1]  Masoumeh Haghpanahi,et al.  Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network , 2019, Nature Medicine.

[2]  Pablo Laguna,et al.  A wavelet-based ECG delineator: evaluation on standard databases , 2004, IEEE Transactions on Biomedical Engineering.

[3]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[4]  Annamalai Nagar,et al.  Automatic Classification of ECG Signal for Heart Disease Diagnosis using morphological features , 2014 .

[5]  Mehmet Korürek,et al.  ECG beat classification using particle swarm optimization and radial basis function neural network , 2010, Expert Syst. Appl..

[6]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[7]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[8]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[9]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[10]  Wei Wu,et al.  SGM: Sequence Generation Model for Multi-label Classification , 2018, COLING.

[11]  Huazhong Yang,et al.  Real-Time ECG Delineation with Randomly Selected Wavelet Transform Feature and Random Walk Estimation , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[12]  Alexander M. Rush,et al.  Sequence-to-Sequence Learning as Beam-Search Optimization , 2016, EMNLP.

[13]  I.Y. Kim,et al.  Hierarchical support vector machine based heartbeat classification using higher order statistics and hermite basis function , 2008, 2008 Computers in Cardiology.

[14]  U. Rajendra Acharya,et al.  Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals , 2017, Inf. Sci..

[15]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Guijin Wang,et al.  Real time ECG characteristic point detection with randomly selected signal pair difference (RSSPD) feature and random forest classifier , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).