A comparative study and analysis of LSTM deep neural networks for heartbeats classification

[1]  Matin Hashemi,et al.  LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices , 2018, IEEE Journal of Biomedical and Health Informatics.

[2]  Ruxin Wang,et al.  Multi-class Arrhythmia detection from 12-lead varied-length ECG using Attention-based Time-Incremental Convolutional Neural Network , 2020, Inf. Fusion.

[3]  Sengul Dogan,et al.  Automated arrhythmia detection using novel hexadecimal local pattern and multilevel wavelet transform with ECG signals , 2019, Knowl. Based Syst..

[4]  U. Rajendra Acharya,et al.  Deep learning for healthcare applications based on physiological signals: A review , 2018, Comput. Methods Programs Biomed..

[5]  U. Rajendra Acharya,et al.  Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats , 2018, Comput. Biol. Medicine.

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

[7]  Kazim Hanbay,et al.  Deep neural network based approach for ECG classification using hybrid differential features and active learning , 2019, IET Signal Process..

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

[9]  Sungyoung Lee,et al.  The Impact of Big Data In Healthcare Analytics , 2020, 2020 International Conference on Information Networking (ICOIN).

[10]  Jürgen Schmidhuber,et al.  Learning to forget: continual prediction with LSTM , 1999 .

[11]  A. Mathai,et al.  Understanding and using sensitivity, specificity and predictive values , 2008, Indian journal of ophthalmology.

[12]  Samiul Based Shuvo,et al.  A Low-cost, Low-energy Wearable ECG System with Cloud-Based Arrhythmia Detection , 2020, 2020 IEEE Region 10 Symposium (TENSYMP).

[13]  Anthony T. Chronopoulos,et al.  A review on deep learning approaches in healthcare systems: Taxonomies, challenges, and open issues , 2020, J. Biomed. Informatics.

[14]  Howida A. Shedeed,et al.  Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks , 2020, IEEE Access.

[15]  Shraddha Singh,et al.  Classification of ECG Arrhythmia using Recurrent Neural Networks , 2018 .

[16]  U. Rajendra Acharya,et al.  Application of deep learning techniques for heartbeats detection using ECG signals-analysis and review , 2020, Comput. Biol. Medicine.

[17]  Sandeep Raj,et al.  Application of variational mode decomposition and ABC optimized DAG-SVM in arrhythmia analysis , 2017, 2017 7th International Symposium on Embedded Computing and System Design (ISED).

[18]  Pu Wang,et al.  LSTM-Based Auto-Encoder Model for ECG Arrhythmias Classification , 2020, IEEE Transactions on Instrumentation and Measurement.

[19]  Sathish Kumar Jayapal,et al.  Global Burden of Cardiovascular Diseases and Risk Factors, 1990–2019 , 2020, Journal of the American College of Cardiology.

[20]  M. Bobnar,et al.  Tuning a sign of magnetoelectric coupling in paramagnetic NH2(CH3)2Al1−xCrx(SO4)2 × 6H2O crystals by metal ion substitution , 2017, Scientific Reports.

[21]  Younghoon Kim,et al.  Classification of Heart Diseases Based On ECG Signals Using Long Short-Term Memory , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[22]  Yang Wei-we,et al.  A Review on , 2008 .

[23]  U. Rajendra Acharya,et al.  A new approach for arrhythmia classification using deep coded features and LSTM networks , 2019, Comput. Methods Programs Biomed..

[24]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .

[25]  Moncef Gabbouj,et al.  Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias , 2017, Scientific Reports.

[26]  Masoud Daneshtalab,et al.  A review on deep learning methods for ECG arrhythmia classification , 2020, Expert Syst. Appl. X.