Fully Convolutional Deep Neural Networks with Optimized Hyperparameters for Detection of Shockable and Non-Shockable Rhythms
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Irena Jekova | Sarah Ménétré | Jean-Philippe Didon | Vessela Krasteva | I. Jekova | V. Krasteva | J. Didon | Sarah Ménétré
[1] Yiming Yang,et al. DARTS: Differentiable Architecture Search , 2018, ICLR.
[2] Jeffrey M. Hausdorff,et al. Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .
[3] Md. Kamrul Hasan,et al. A simple time domain algorithm for the detection of ventricular fibrillation in electrocardiogram , 2011, Signal Image Video Process..
[4] Hongxing Liu,et al. ECG Heartbeat Classification Using Convolutional Neural Networks , 2020, IEEE Access.
[5] Jonathan Rubin,et al. Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings. , 2018, Journal of electrocardiology.
[6] Jonathan Rubin,et al. Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation , 2018, Physiological measurement.
[7] Karl Unterkofler,et al. Detecting Ventricular Fibrillation by Time-Delay Methods , 2007, IEEE Transactions on Biomedical Engineering.
[8] Pavel Jurák,et al. Fast Detection of Ventricular Fibrillation and Ventricular Tachycardia in 1-Lead ECG from Three-Second Blocks , 2018, 2018 Computing in Cardiology Conference (CinC).
[9] Manal M. Tantawi,et al. Heartbeat Classification Using 1D Convolutional Neural Networks , 2019, AISI.
[10] Elizabeth F. Wanner,et al. QRS Detection in ECG Signal with Convolutional Network , 2018, CIARP.
[11] Sergey Alekseev,et al. ECG Segmentation by Neural Networks: Errors and Correction , 2018, 2019 International Joint Conference on Neural Networks (IJCNN).
[12] C. Deakin,et al. Chest compression pauses during defibrillation attempts , 2016, Current opinion in critical care.
[13] Daniel Davis,et al. Survival after application of automatic external defibrillators before arrival of the emergency medical system: evaluation in the resuscitation outcomes consortium population of 21 million. , 2010, Journal of the American College of Cardiology.
[14] Moncef Gabbouj,et al. Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks , 2016, IEEE Transactions on Biomedical Engineering.
[15] Minho Choi,et al. QRS detection method based on fully convolutional networks for capacitive electrocardiogram , 2019, Expert Syst. Appl..
[16] Qiao Li,et al. Ventricular Fibrillation and Tachycardia Classification Using a Machine Learning Approach , 2014, IEEE Transactions on Biomedical Engineering.
[17] Jinsul Kim,et al. An Automated ECG Beat Classification System Using Convolutional Neural Networks , 2016, 2016 6th International Conference on IT Convergence and Security (ICITCS).
[18] Irena Jekova,et al. Shock advisory system with minimal delay triggering after end of chest compressions: accuracy and gained hands-off time. , 2011, Resuscitation.
[19] Sayan Mukhopadhyay,et al. Deep Learning and Neural Networks , 2018 .
[20] I Jekova,et al. Comparison of five algorithms for the detection of ventricular fibrillation from the surface ECG. , 2000, Physiological measurement.
[21] José Luis Rojo-Álvarez,et al. Detection of Life-Threatening Arrhythmias Using Feature Selection and Support Vector Machines , 2014, IEEE Transactions on Biomedical Engineering.
[22] U. Rajendra Acharya,et al. A deep convolutional neural network model to classify heartbeats , 2017, Comput. Biol. Medicine.
[23] J P Ornato,et al. Automatic external defibrillators for public access defibrillation: recommendations for specifying and reporting arrhythmia analysis algorithm performance, incorporating new waveforms, and enhancing safety. A statement for health professionals from the American Heart Association Task Force on Automa , 1997, Circulation.
[24] Azeddine Mjahad,et al. Ventricular Fibrillation and Tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning , 2017, Comput. Methods Programs Biomed..
[25] Irena Jekova,et al. Real time detection of ventricular fibrillation and tachycardia. , 2004, Physiological measurement.
[26] U. Rajendra Acharya,et al. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network , 2017, Inf. Sci..
[27] David D. Salcido,et al. The impact of peri-shock pause on survival from out-of-hospital shockable cardiac arrest during the Resuscitation Outcomes Consortium PRIMED trial. , 2014, Resuscitation.
[28] Richard E. Kerber,et al. Automatic External Defibrillators for Public Access Defibrillation: Recommendations for Specifying and Reporting Arrhythmia Analysis Algorithm Performance, Incorporating New Waveforms, and Enhancing Safety , 1997, Biomedical instrumentation & technology.
[29] Unai Irusta,et al. Deep Neural Networks for ECG-Based Pulse Detection during Out-of-Hospital Cardiac Arrest , 2019, Entropy.
[30] Irena Jekova,et al. Shock advisory tool: Detection of life-threatening cardiac arrhythmias and shock success prediction by means of a common parameter set , 2007, Biomed. Signal Process. Control..
[31] J. Bache. European Resuscitation Council Guidelines for Resuscitation , 1998 .
[32] Minh Tuan Nguyen,et al. Feature Learning Using Convolutional Neural Network for Cardiac Arrest Detection , 2018, 2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS).
[33] K. Årestedt,et al. Sensitivity and specificity of two different automated external defibrillators. , 2017, Resuscitation.
[34] Samarendra Dandapat,et al. Detection of Shockable Ventricular Arrhythmia using Variational Mode Decomposition , 2016, Journal of Medical Systems.
[35] Felipe Alonso-Atienza,et al. Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia , 2019, PloS one.
[36] Xu-Sheng Zhang,et al. Detecting ventricular tachycardia and fibrillation by complexity measure , 1999, IEEE Transactions on Biomedical Engineering.
[37] Irena Jekova,et al. Assessment of ECG frequency and morphology parameters for automatic classification of life-threatening cardiac arrhythmias , 2005, Physiological measurement.
[38] Irena Jekova,et al. Method for ventricular fibrillation detection in the external electrocardiogram using nonlinear prediction. , 2002, Physiological measurement.
[39] Javier Del Ser,et al. ECG-based Random Forest Classifier for Cardiac Arrest Rhythms , 2019, 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[40] U. Irusta,et al. Deep learning approach for a shock advise algorithm using short electrocardiogram analysis intervals , 2019, Resuscitation.
[41] D. Atkins,et al. Comparison of Electrocardiographic Characteristics of Adults and Children for Automated External Defibrillator Algorithms , 2014, Pediatric emergency care.
[42] S Barro,et al. Algorithmic sequential decision-making in the frequency domain for life threatening ventricular arrhythmias and imitative artefacts: a diagnostic system. , 1989, Journal of biomedical engineering.
[43] Scott David Greenwald,et al. The development and analysis of a ventricular fibrillation detector , 1986 .
[44] Hamido Fujita,et al. Decision support system for arrhythmia prediction using convolutional neural network structure without preprocessing , 2019, Applied Intelligence.
[45] A. Murray,et al. Comparison of four techniques for recognition of ventricular fibrillation from the surface ECG , 1993, Medical and Biological Engineering and Computing.
[46] Irena Jekova,et al. Bench study of the accuracy of a commercial AED arrhythmia analysis algorithm in the presence of electromagnetic interferences , 2009, Physiological measurement.
[47] A. Amann,et al. Reliability of old and new ventricular fibrillation detection algorithms for automated external defibrillators , 2005, Biomedical engineering online.
[48] Quoc V. Le,et al. Efficient Neural Architecture Search via Parameter Sharing , 2018, ICML.
[49] Felipe Alonso-Atienza,et al. Machine Learning Techniques for the Detection of Shockable Rhythms in Automated External Defibrillators , 2016, PloS one.
[50] Yu Tsao,et al. Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders , 2019, IEEE Access.
[51] Guigang Zhang,et al. Deep Learning , 2016, Int. J. Semantic Comput..
[52] U. Raghavendra,et al. Automated identification of shockable and non-shockable life-threatening ventricular arrhythmias using convolutional neural network , 2018, Future Gener. Comput. Syst..
[53] Masoumeh Haghpanahi,et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network , 2019, Nature Medicine.
[54] Valérie Gay,et al. Ventricular Tachycardia/Fibrillation Detection Algorithm for 24/7 Personal Wireless Heart Monitoring , 2007, ICOST.
[55] Ali Bahrami Rad,et al. SPECTRO-TEMPORAL ECG ANALYSIS FOR ATRIAL FIBRILLATION DETECTION , 2018, 2018 IEEE 28th International Workshop on Machine Learning for Signal Processing (MLSP).
[56] T. Eftestøl,et al. Fully automatic rhythm analysis during chest compression pauses. , 2015, Resuscitation.
[57] Ye Li,et al. Multiscaled Fusion of Deep Convolutional Neural Networks for Screening Atrial Fibrillation From Single Lead Short ECG Recordings , 2018, IEEE Journal of Biomedical and Health Informatics.
[58] Sofía Ruiz de Gauna,et al. A high-temporal resolution algorithm to discriminate shockable from nonshockable rhythms in adults and children. , 2012, Resuscitation.
[59] Xuemei Guo,et al. Non-invasive Fetal Electrocardiography Denoising Using Deep Convolutional Encoder-Decoder Networks , 2019, Lecture Notes in Electrical Engineering.
[60] N. Thakor,et al. Ventricular tachycardia and fibrillation detection by a sequential hypothesis testing algorithm , 1990, IEEE Transactions on Biomedical Engineering.
[61] Jesús Requena-Carrión,et al. Analysis of the robustness of spectral indices during ventricular fibrillation , 2013, Biomed. Signal Process. Control..
[62] Daniel Jost,et al. Comparison of Pediatric and Adult ECG Rhythm Analysis by Automated External Defibrillators During Out-of-Hospital Cardiac Arrest , 2018, 2018 Computing in Cardiology Conference (CinC).