A hybrid method for heartbeat classification via convolutional neural networks, multilayer perceptrons and focal loss

[1]  Giuseppe De Pietro,et al.  A deep learning approach for ECG-based heartbeat classification for arrhythmia detection , 2018, Future Gener. Comput. Syst..

[2]  David Menotti,et al.  Inter-Patient ECG Heartbeat Classification with Temporal VCG Optimized by PSO , 2017, Scientific Reports.

[3]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[4]  Heasoo Hwang,et al.  A robust deep convolutional neural network with batch-weighted loss for heartbeat classification , 2019, Expert Syst. Appl..

[5]  Ridha Ouni,et al.  Electrocardiogram heartbeat classification based on a deep convolutional neural network and focal loss , 2020, Comput. Biol. Medicine.

[6]  Manuel G. Penedo,et al.  Heartbeat classification fusing temporal and morphological information of ECGs via ensemble of classifiers , 2019, Biomed. Signal Process. Control..

[7]  Zhining Xia,et al.  The cardiovascular toxicity induced by high doses of gatifloxacin and ciprofloxacin in zebrafish. , 2019, Environmental pollution.

[8]  C. Cohan,et al.  Focal loss of actin bundles causes microtubule redistribution and growth cone turning , 2002, The Journal of cell biology.

[9]  Xuan Zeng,et al.  HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications , 2017, IEEE Access.

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

[11]  Haibin Shen,et al.  ECG Authentication Method Based on Parallel Multi-Scale One-Dimensional Residual Network With Center and Margin Loss , 2019, IEEE Access.

[12]  Khashayar Khorasani,et al.  Deep Convolutional Neural Networks and Learning ECG Features for Screening Paroxysmal Atrial Fibrillation Patients , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[13]  Jinghui Chu,et al.  A NOVEL TWO-LEAD ARRHYTHMIA CLASSIFICATION SYSTEM BASED ON CNN AND LSTM , 2019, Journal of Mechanics in Medicine and Biology.

[14]  Sebastian Zaunseder,et al.  Optimization of ECG Classification by Means of Feature Selection , 2011, IEEE Transactions on Biomedical Engineering.

[15]  Jianqing Huang,et al.  Reconstruction for limited-data nonlinear tomographic absorption spectroscopy via deep learning , 2018, Journal of Quantitative Spectroscopy and Radiative Transfer.

[16]  Jian Li,et al.  Heartbeat classification using projected and dynamic features of ECG signal , 2017, Biomed. Signal Process. Control..

[17]  Sherman Robinson,et al.  Updating and Estimating a Social Accounting Matrix Using Cross Entropy Methods , 2001 .

[18]  B. V. K. Vijaya Kumar,et al.  Heartbeat Classification Using Morphological and Dynamic Features of ECG Signals , 2012, IEEE Transactions on Biomedical Engineering.

[19]  J. Rothstein,et al.  Focal loss of the glutamate transporter EAAT2 in a transgenic rat model of SOD1 mutant-mediated amyotrophic lateral sclerosis (ALS) , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[20]  Philip de Chazal,et al.  Automatic classification of heartbeats using ECG morphology and heartbeat interval features , 2004, IEEE Transactions on Biomedical Engineering.

[21]  David Menotti,et al.  Robust automated cardiac arrhythmia detection in ECG beat signals , 2018, Neural Computing and Applications.

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

[23]  G.B. Moody,et al.  The impact of the MIT-BIH Arrhythmia Database , 2001, IEEE Engineering in Medicine and Biology Magazine.

[24]  Witold Pedrycz,et al.  ECG signal processing, classification, and interpretation : , 2012 .

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