A Novel 1-D CCANet for ECG Classification

This paper puts forward a 1-D convolutional neural network (CNN) that exploits a novel analysis of the correlation between the two leads of the noisy electrocardiogram (ECG) to classify heartbeats. The proposed method is one-dimensional, enabling complex structures while maintaining a reasonable computational complexity. It is based on the combination of elementary handcrafted time domain features, frequency domain features through spectrograms and the use of autoregressive modeling. On the MIT-BIH database, a 95.52% overall accuracy is obtained by classifying 15 types, whereas a 95.70% overall accuracy is reached when classifying 7 types from the INCART database.

[1]  Jimeng Sun,et al.  Opportunities and Challenges in Deep Learning Methods on Electrocardiogram Data: A Systematic Review , 2020, ArXiv.

[2]  Huifang Huang,et al.  A new hierarchical method for inter-patient heartbeat classification using random projections and RR intervals , 2014, BioMedical Engineering OnLine.

[3]  Annisa Darmawahyuni,et al.  An Automated ECG Beat Classification System Using Deep Neural Networks with an Unsupervised Feature Extraction Technique , 2019, Applied Sciences.

[4]  Paolo Napoletano,et al.  Benchmark Analysis of Representative Deep Neural Network Architectures , 2018, IEEE Access.

[5]  Shuo Li,et al.  An Automatic Cardiac Arrhythmia Classification System With Wearable Electrocardiogram , 2018, IEEE Access.

[6]  Naomie Salim,et al.  Arrhythmia recognition and classification using combined linear and nonlinear features of ECG signals , 2016, Comput. Methods Programs Biomed..

[7]  Weiyi Yang,et al.  A Novel Approach for Multi-Lead ECG Classification Using DL-CCANet and TL-CCANet , 2019, Sensors.

[8]  Mohamed Hammad,et al.  Detection of abnormal heart conditions based on characteristics of ECG signals , 2018, Measurement.

[9]  Di Wang,et al.  Automatic recognition of arrhythmia based on principal component analysis network and linear support vector machine , 2018, Comput. Biol. Medicine.

[10]  José Luis Rojo-Álvarez,et al.  Detection of Life-Threatening Arrhythmias Using Feature Selection and Support Vector Machines , 2014, IEEE Transactions on Biomedical Engineering.

[11]  Min Hong,et al.  Deep Learning in Physiological Signal Data: A Survey , 2020, Sensors.

[12]  Wei Liu,et al.  Tensor Canonical Correlation Analysis Networks for Multi-View Remote Sensing Scene Recognition , 2020, IEEE Transactions on Knowledge and Data Engineering.

[13]  Pawe Pawiak,et al.  Novel methodology of cardiac health recognition based on ECG signals and evolutionary-neural system , 2018 .

[14]  D. Ge,et al.  Cardiac arrhythmia classification using autoregressive modeling , 2002, Biomedical engineering online.

[15]  Michel Verleysen,et al.  Weighted Conditional Random Fields for Supervised Interpatient Heartbeat Classification , 2012, IEEE Transactions on Biomedical Engineering.

[16]  U. Rajendra Acharya,et al.  Arrhythmia detection using deep convolutional neural network with long duration ECG signals , 2018, Comput. Biol. Medicine.

[17]  Kup-Sze Choi,et al.  Heartbeat classification using disease-specific feature selection , 2014, Comput. Biol. Medicine.

[18]  Rekha Rajagopal,et al.  Evaluation of effect of unsupervised dimensionality reduction techniques on automated arrhythmia classification , 2017, Biomed. Signal Process. Control..

[19]  Tao Xu,et al.  A Spatial Pyramid Pooling-Based Deep Convolutional Neural Network for the Classification of Electrocardiogram Beats , 2018, Applied Sciences.

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

[21]  U. Rajendra Acharya,et al.  ECG beat classification using PCA, LDA, ICA and Discrete Wavelet Transform , 2013, Biomed. Signal Process. Control..

[22]  Sung Bum Pan,et al.  An EigenECG Network Approach Based on PCANet for Personal Identification from ECG Signal , 2018, Sensors.

[23]  Qiao Li,et al.  Ventricular Fibrillation and Tachycardia Classification Using a Machine Learning Approach , 2014, IEEE Transactions on Biomedical Engineering.

[24]  P. Karthigaikumar,et al.  ECG Signal Preprocessing and SVM Classifier-Based Abnormality Detection in Remote Healthcare Applications , 2018, IEEE Access.

[25]  Kenneth E. Barner,et al.  A novel application of deep learning for single-lead ECG classification , 2018, Comput. Biol. Medicine.

[26]  Juan Pablo Martínez,et al.  Heartbeat Classification Using Feature Selection Driven by Database Generalization Criteria , 2011, IEEE Transactions on Biomedical Engineering.

[27]  Danyang Yuan,et al.  Genetic algorithm for the optimization of features and neural networks in ECG signals classification , 2017, Scientific Reports.

[28]  Weifeng Liu,et al.  Canonical correlation analysis networks for two-view image recognition , 2017, Inf. Sci..

[29]  Vinod Kumar,et al.  Detection of myocardial infarction in 12 lead ECG using support vector machine , 2018, Appl. Soft Comput..

[30]  Xiaoqing Luo,et al.  Heartbeat classification using decision level fusion , 2014 .

[31]  U. Rajendra Acharya,et al.  Novel deep genetic ensemble of classifiers for arrhythmia detection using ECG signals , 2019, Neural Computing and Applications.

[32]  U. Rajendra Acharya,et al.  A deep convolutional neural network model to classify heartbeats , 2017, Comput. Biol. Medicine.