Shenda Hong, Yuxi Zhou, Junyuan Shang, Cao Xiao, and Jimeng Sun. "Opportunities and Challenges of Deep Learning Methods for Electrocardiogram Data: A Systematic Review." Computers in Biology and Medicine. https://arxiv.org/pdf/2001.01550.pdf
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| ID | Year | Title | Link | Venue | Task | Method | Dataset |
|---|---|---|---|---|---|---|---|
| 1 | 2015 | Real-Time Patient-Specific ECG Classification by 1-D Convolutional Neural Networks | link | TBME | beat classify | CNN | MIT-BIH arrhythmia |
| 2 | 2015 | Convolutional Neural Networks for patient-specific ECG classification | link | EMBC | beat classify | CNN | MIT-BIH arrhythmia |
| 3 | 2016 | ECG Monitoring System Integrated With IR-UWB Radar Based on CNN | link | IEEE ACCESS | arrhythmia classification | CNN | The radar data and ECG data are acquired by radar module NVA-R661 and BMD101 sensor chip |
| 4 | 2016 | A stacked contractive denoising auto-encoder for ECG signal denoising | link | PMEA | denoising | DAE | MIT-BIH arrhythmia, MIT-BIH Noise Stress Test |
| 5 | 2016 | Semi-supervised stacked label consistent autoencoder for reconstruction and analysis of biomedical signals | link | TBME | reconstruction | AE | MIT-BIH arrhythmia, edb |
| 6 | 2017 | Real-time multilead convolutional neural network for myocardial infarction detection | link | BHI | MI | CNN | PTBDB |
| 7 | 2017 | A deep convolutional neural network model to classify heartbeats | link | CBM | classify MIT-BIH arrhythmia | CNN | MIT-BIH arrhythmia |
| 8 | 2017 | Automatic coordinate prediction of the exit of ventricular tachycardia from 12-lead electrocardiogram | link | CinC | localize VT | DAE | not found |
| 9 | 2017 | ENCASE: An ENsemble ClASsifiEr for ECG classification using expert features and deep neural networks | link | CinC | AF | resnet+expert | Physionet Challenge 2017 |
| 10 | 2017 | Convolutional recurrent neural networks for electrocardiogram classification | link | CinC | AF | CNN+RNN | Physionet Challenge 2017 |
| 11 | 2017 | AF Classification from a short single lead ECG recording: the PhysioNet/Computing in Cardiology Challenge 2017 | link | CinC | AF | Physionet Challenge 2017 | |
| 12 | 2017 | Robust ECG signal classification for detection of atrial fibrillation using a novel neural network | link | CinC | AF | resnet | Physionet Challenge 2017 |
| 13 | 2017 | Comparing feature-based classifiers and convolutional neural networks to detect arrhythmia from short segments of ECG | link | CinC | AF | CNN+feature | Physionet Challenge 2017 |
| 14 | 2017 | Cardiac arrhythmia detection from ECG combining convolutional and long short-term memory networks | link | CinC | AF | CNN+RNN | Physionet Challenge 2017 |
| 15 | 2017 | Atrial fibrillation detection using stationary wavelet transform and deep learning | link | CinC | AF | CNN+feature | Physionet Challenge 2017 |
| 16 | 2017 | Densely connected convolutional networks and signal quality analysis to detect atrial fibrillation using short single-lead ECG recordings | link | CinC | AF | DenseNet | Physionet Challenge 2017 |
| 17 | 2017 | Atrial fibrillation detection and ECG classification based on convolutional recurrent neural network | link | CinC | AF | CNN | Physionet Challenge 2017 |
| 18 | 2017 | Atrial fibrillation classification using qrs complex features and lstm | link | CinC | AF | LSTM+expert | Physionet Challenge 2017 |
| 19 | 2017 | Atrial fibrillation detection using feature based algorithm and deep convolutional neural network | link | CinC | AF | CNN+expert | Physionet Challenge 2017 |
| 20 | 2017 | Beat by beat: Classifying cardiac arrhythmias with recurrent neural networks | link | CinC | AF | RNN+attention | Physionet Challenge 2017 |
| 21 | 2017 | Annotating ECG Signals With Deep Neural Networks | link | Circulation | annotate ECG waves | BiLSTM | QTDB |
| 22 | 2017 | Cardiovascular risk stratification using off-the-shelf wearables and a multi-task deep learning algorithm | link | Circulation | prevalent hypertension, sleep apnea, and diabetes | CNN+RNN | see DeepHeart |
| 23 | 2017 | HeartID: A Multiresolution Convolutional Neural Network for ECG-Based Biometric Human Identification in Smart Health Applications | link | IEEE ACCESS | Biometric Human Identification | CNN | CEBSDB, WECG, FANTASIA, NSRDB, STDB, MITDB, AFDB, VFDB |
| 24 | 2017 | ECG data compression using a neural network model based on multi-objective optimization | link | PlosOne | data compression | FCNN | MIT-BIH arrhythmia |
| 25 | 2017 | Personalized Monitoring and Advance Warning System for Cardiac Arrhythmias | link | SR | classify MIT-BIH arrhythmia | CNN | MIT-BIH arrhythmia |
| 26 | 2017 | Deep convolutional neural networks and learning ECG features for screening paroxysmal atrial fibrillation patients | link | T Cybernetics | AF | CNN | |
| 27 | 2017 | Localization of origins of premature ventricular contraction by means of convolutional neural network from 12-lead ECG | link | TBME | Localization | CNN | not found |
| 28 | 2017 | Recognition of emotions using multimodal physiological signals and an ensemble deep learning model | link | CMPB | emotion detection | SAE | DEAP |
| 29 | 2017 | Elimination of power line interference from ECG signals using recurrent neural networks | link | EMBC | denoisig | RNN | synthetic |
| 30 | 2018 | DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction | link | AAAI | diagnosis | CNN+RNN semi | close, https://ahajournals.org/doi/abs/10.1161/circ.136.suppl_1.21029 |
| 31 | 2018 | A Deep Learning Approach to Examine Ischemic ST Changes in Ambulatory ECG Recordings | link | AMIA | ST | CNN model from Inception V3 | ltstdb |
| 32 | 2018 | Towards end-to-end ECG classification with raw signal extraction and deep neural networks | link | BHI | classify MIT-BIH arrhythmia | RBM | MIT-BIH arrhythmia |
| 33 | 2018 | Automated detection of atrial fibrillation using long short-term memory network with RR interval signals | link | CBM | AF | CNN+RNN | afdb |
| 34 | 2018 | Detecting atrial fibrillation by deep convolutional neural networks | link | CBM | AF | CNN | afdb |
| 35 | 2018 | ECG authentication system design incorporating a convolutional neural network and generalized S-Transformation | link | CBM | Biometric Human Identification | CNN | ecgiddb, Physionet Challenge 2017 |
| 36 | 2018 | Application of stacked convolutional and long short-term memory network for accurate identification of CAD ECG signals | link | CBM | Coronary artery disease | CNN+RNN | incartdb |
| 37 | 2018 | Arrhythmia detection using deep convolutional neural network with long duration ECG signals | link | CBM | arrhythmia | CNN | MIT-BIH arrhythmia |
| 38 | 2018 | A novel application of deep learning for single-lead ECG classification | link | CBM | arrhythmia | DBN | MIT-BIH arrhythmia |
| 39 | 2018 | Automated diagnosis of arrhythmia using combination of CNN and LSTM techniques with variable length heart beats | link | CBM | arrhythmia | CNN+RNN | MIT-BIH arrhythmia |
| 40 | 2018 | A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification | link | CBM | classify MIT-BIH arrhythmia | BiLSTM | MIT-BIH arrhythmia |
| 41 | 2018 | Deep Learning for Pulse Detection in Out-of-Hospital Cardiac Arrest Using the ECG | link | CinC | Pulse Detection | CNN+RNN | not found |
| 42 | 2018 | Electrocardiogram Monitoring and Interpretation: From Traditional Machine Learning to Deep Learning, and Their Combination | link | CinC | NA | ||
| 43 | 2018 | AF Detection by Exploiting the Spectral and Temporal Characteristics of ECG Signals With the LSTM Model | link | CinC | AF | LSTM for multi-head ECG | Physionet Challenge 2017 |
| 44 | 2018 | Classification of Atrial Fibrillation Using Stacked Auto Encoders Neural Networks | link | CinC | AF | AE | Physionet Challenge 2017 |
| 45 | 2018 | Generalization Studies of Neural Network Models for Cardiac Disease Detection Using Limited Channel ECG | link | CinC | Disease | CNN+RNN | PTBDB |
| 46 | 2018 | Deep learning based QRS multilead delineator in electrocardiogram signals | link | CinC | QRS | 1-D CNN, ECG delineation to find QRS width by two CNN+MLP | QTDB |
| 47 | 2018 | Artificial Intelligence Detects Pediatric Heart Murmurs With Cardiologist-Level Accuracy | link | Circulation | Detects Pediatric Heart Murmurs | 只说了deep neural network | 54 patients collected at duPont Hospital for Children using an Eko Core digital stethoscope |
| 48 | 2018 | Electrocardiographic Screening for Atrial Fibrillation While in Sinus Rhythm Using Deep Learning | link | Circulation | AF | CNN | Mayo Clinic database |
| 49 | 2018 | A 10-RR-Interval-Based Rhythm Classifier Using a Deep Neural Network | link | Circulation | 5 rhythms: normal sinus rhythm, atrial fibrillation, premature beats, bigeminy, and trigeminy | 只说了deep neural network | only PhysioNet |
| 50 | 2018 | Deep Learning to Detect Atrial Fibrillation From Short Noisy ECG Segments Measured With Wireless Sensors | link | Circulation | AF | CNN | Physionet Challenge 2017 |
| 51 | 2018 | ECG classification using three-level fusion of different feature descriptors | link | ESA | classify MIT-BIH arrhythmia | CNN+expert | MIT-BIH arrhythmia |
| 52 | 2018 | Using deep convolutional neural network for emotion detection on a physiological signals dataset (AMIGOS) | link | IEEE ACCESS | emotion detection | CNN | AMIGOS |
| 53 | 2018 | Combining convolutional neural network and distance distribution matrix for identification of congestive heart failure | link | IEEE ACCESS | diagnoise CHF | CNN+emotion detection | CHF2DB, NSRDB |
| 54 | 2018 | Automated ECG Classification Using Dual Heartbeat Coupling Based on Convolutional Neural Network | link | IEEE ACCESS | classify MIT-BIH arrhythmia | CNN | MIT-BIH arrhythmia |
| 55 | 2018 | An Automatic Cardiac Arrhythmia Classification System With Wearable Electrocardiogram | link | IEEE ACCESS | classify MIT-BIH arrhythmia | SDAE | MIT-BIH arrhythmia |
| 56 | 2018 | Multimodal Biometric Authentication Systems Using Convolution Neural Network Based on Different Level Fusion of ECG and Fingerprint | link | IEEE ACCESS | Biometric Human Identification | CNN+SVM | PTBDB, CYBHi |
| 57 | 2018 | NON-INVASIVE DETECTION OF HYPERKALEMIA WITH A SMARTPHONE ELECTROCARDIOGRAM AND ARTIFICIAL INTELLIGENCE | link | JACC | DNN | ||
| 58 | 2018 | RAIM: Recurrent Attentive and Intensive Model of Multimodal Patient Monitoring Data | link | KDD | mortality | CRNN | MIMIC-III |
| 59 | 2018 | Detection of Paroxysmal Atrial Fibrillation using Attention-based Bidirectional Recurrent Neural Networks | link | KDD | AF | BiRNN+attention+CNN | own collected |
| 60 | 2018 | A method to detect sleep apnea based on deep neural network and hidden markov model using single-lead ECG signal | link | Neurocomputing | Sleep | SAE | Apnea-ECG |
| 61 | 2018 | Patient-specific ECG classification by deeper CNN from generic to dedicated | link | Neurocomputing | classify MIT-BIH arrhythmia | CNN | MIT-BIH arrhythmia |
| 62 | 2018 | Noninvasive assessment of dofetilide plasma concentration using a deep learning (neural network) analysis of the surface electrocardiogram: A proof of concept study | link | PlosOne | drug assessment | CNN | ecgrdvq, ecgdmmld |
| 63 | 2018 | Sleep-wake classification via quantifying heart rate variability by convolutional neural network | link | PMEA | sleep | CNN | own collected |
| 64 | 2018 | Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram | link | PMEA | sleep stage | CNN | own collected |
| 65 | 2018 | A deep learning approach for fetal QRS complex detection | link | PMEA | QRS | CNN | Physionet Challenge 2013 |
| 66 | 2018 | Abductive reasoning as a basis to reproduce expert criteria in ECG atrial fibrillation identification | link | PMEA | AF | LSTM+expert | Physionet Challenge 2017 |
| 67 | 2018 | Analyzing single-lead short ECG recordings using dense convolutional neural networks and feature-based post-processing to detect atrial fibrillation | link | PMEA | arrhythmia | densenet | Physionet Challenge 2017 |
| 68 | 2018 | ECG signal classification for the detection of cardiac arrhythmias using a convolutional recurrent neural network | link | PMEA | arrhythmia | resnet | Physionet Challenge 2017 |
| 69 | 2018 | Parallel use of a convolutional neural network and bagged tree ensemble for the classification of Holter ECG | link | PMEA | arrhythmia | CNN+tree | Physionet Challenge 2017 |
| 70 | 2018 | Ensembling convolutional and long short-term memory networks for electrocardiogram arrhythmia detection | link | PMEA | arrhythmia | CNN+LSTM | Physionet Challenge 2017 |
| 71 | 2018 | A convolutional neural network for ECG annotation as the basis for classification of cardiac rhythms | link | PMEA | arrhythmia | CNN | Physionet Challenge 2017 |
| 72 | 2018 | A robust deep convolutional neural network for the classification of abnormal cardiac rhythm using single lead electrocardiograms of variable length | link | PMEA | arrhythmia | CNN+feature | Physionet Challenge 2017 |
| 73 | 2018 | Detecting and interpreting myocardial infarction using fully convolutional neural networks | link | PMEA | MI | CNN | PTBDB |
| 74 | 2018 | Deep Feature Learning for Sudden Cardiac Arrest Detection in Automated External Defibrillators | link | SR | Sudden Cardiac Arrest Detection | CNN | CUDB, VFDB |
| 75 | 2018 | Monitoring Significant ST Changes through Deep Learning | link | JE | ST | CNN | LTSTDB |
| 76 | 2018 | Densely connected convolutional networks for detection of atrial fibrillation from short single-lead ECG recordings | link | JE | arrhythmia | CNN | Physionet Challenge 2017 |
| 77 | 2018 | Region Aggregation Network: Improving Convolutional Neural Network for ECG Characteristic Detection | link | EMBC | annotate ECG waves | CNN | QTDB |
| 78 | 2018 | A Generative Modeling Approach to Limited Channel ECG Classification | link | EMBC | classify | RNN (encoder decoder) | PTBDB |
| 79 | 2018 | Classification of Heart Diseases Based On ECG Signals Using Long Short-Term Memory | link | EMBC | beat classify | RNN | UCR |
| 80 | 2018 | Finger ECG based Two-phase Authentication Using 1D Convolutional Neural Networks | link | EMBC | identification | CNN | own collected |
| 81 | 2018 | QRS Detection and Measurement Method of ECG Paper Based on Convolutional Neural Networks | link | EMBC | QRS detection | CNN | incartdb |
| 82 | 2018 | Bidirectional Recurrent Neural Network And Convolutional Neural Network (BiRCNN) For ECG Beat Classification | link | EMBC | beat classify | CRNN | MIT-BIH arrhythmia |
| 83 | 2018 | Premature Ventricular Contraction Detection from Ambulatory ECG Using Recurrent Neural Networks | link | EMBC | PVC | RNN | MIT-BIH arrhythmia |
| 84 | 2019 | A Comparison of Patient History- and EKG-based Cardiac Risk Scores | link | AMIA | Patient-specific risk scores | resnet | close, electronic health records in the Partners health care system, associated with Brigham and Women’s hospital (BWH) |
| 85 | 2019 | Synthesis of Electrocardiogram V Lead Signals from Limb Lead Measurement using R peak Aligned Generative Adversarial Network | link | BHI | Synthesis V Lead | GAN | PTBDB |
| 86 | 2019 | I-Vector Based Patient Adaptation of Deep Neural Networks for Automatic Heartbeat Classification | link | BHI | classify MIT-BIH arrhythmia | FCNN | MIT-BIH arrhythmia |
| 87 | 2019 | Accurate detection of atrial fibrillation from 12-lead ECG using deep neural network | link | CBM | AF | CNN | Chinese PLA General Hospital, CardioCloud Medical Technology (Beijing) Co. Ltd, CPSC |
| 88 | 2019 | ECG anomaly class identification using LSTM and error profile modeling | link | CBM | classify MIT-BIH arrhythmia | LSTM | MIT-BIH arrhythmia |
| 89 | 2019 | Automated beat-wise arrhythmia diagnosis using modified U-net on extended electrocardiographic recordings with heterogeneous arrhythmia types | link | CBM | classify MIT-BIH arrhythmia | Unet | MIT-BIH arrhythmia |
| 90 | 2019 | Automatic driver stress level classification using multimodal deep learning | link | ESA | driver stress level classification | CNN+LSTM | closed |
| 91 | 2019 | A deep learning approach for real-time detection of atrial fibrillation | link | ESA | AF | CNN+RNN | afdb, mitdb, nsrdb |
| 92 | 2019 | A novel multi-module neural network system for imbalanced heartbeats classification | link | ESA | classify beats MIT-BIH & others | CNN | MIT-BIH arrhythmia |
| 93 | 2019 | A robust deep convolutional neural network with batch-weighted loss for heartbeat classification | link | ESA | classify MIT-BIH arrhythmia | CNN | MIT-BIH arrhythmia |
| 94 | 2019 | QRS detection method based on fully convolutional networks for capacitive electrocardiogram | link | ESA | QRS | CNN | MIT-BIH arrhythmia |
| 95 | 2019 | A LSTM and CNN Based Assemble Neural Network Framework for Arrhythmias Classification | link | ICASSP | arrhythmia | CNN+LSTM | incartdb |
| 96 | 2019 | Inter- and Intra- Patient ECG Heartbeat Classification for Arrhythmia Detection: A Sequence to Sequence Deep Learning Approach | link | ICASSP | classify MIT-BIH arrhythmia | CRNN | MIT-BIH arrhythmia |
| 97 | 2019 | An Ensemble of Deep Recurrent Neural Networks for P-wave Detection in Electrocardiogram | link | ICASSP | P wave detection | RNN | QTDB |
| 98 | 2019 | A Multi-Class Automatic Sleep Staging Method Based on Long Short-Term Memory Network Using Single-Lead Electrocardiogram Signals | link | IEEE ACCESS | Sleep Staging | LSTM | own collected |
| 99 | 2019 | Noise Rejection for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Networks | link | IEEE ACCESS | noise detection | CNN+features | 12-lead Lenovo Smart ECG device |
| 100 | 2019 | A Novel Deep Arrhythmia-Diagnosis Network for Atrial Fibrillation Classification Using Electrocardiogram Signals | link | IEEE ACCESS | AF heartbeats, MIT-BIH AF | CNN+BiLSTM | afdb |
| 101 | 2019 | Deep Ensemble Detection of Congestive Heart Failure Using Short-Term RR Intervals | link | IEEE ACCESS | CHF | CNN+RNN | BIDMC, CHF2DB |
| 102 | 2019 | A Probabilistic Process Neural Network and Its Application in ECG Classification | link | IEEE ACCESS | disease classification, Chinese Cardiovascular Disease Database (CCDD) | probabilistic process neural network (PPNN) | CCDD |
| 103 | 2019 | Automatic Cardiac Arrhythmia Classification Using Combination of Deep Residual Network and Bidirectional LSTM | link | IEEE ACCESS | CPSC | resnet+BiLSTM | CPSC |
| 104 | 2019 | Personal Identification Using a Robust Eigen ECG Network Based on Time-Frequency Representations of ECG Signals | link | IEEE ACCESS | Biometric Human Identification | principal component analysis network | CUDB, PTBDB |
| 105 | 2019 | Early and remote detection of possible heartbeat problems with convolutional neural networks and multipart interactive training | link | IEEE ACCESS | beat classification | resnet | Kaggle Classifying Heart Sounds Challenge |
| 106 | 2019 | ECG Arrhythmia Classification Using STFT-Based Spectrogram and Convolutional Neural Network | link | IEEE ACCESS | arrhythmia classification | CNN+features | MIT-BIH arrhythmia |
| 107 | 2019 | Automatic classification of CAD ECG signals with SDAE and bidirectional long short-term term network | link | IEEE ACCESS | classify MIT-BIH arrhythmia | SDAE+BiLSTM | MIT-BIH arrhythmia |
| 108 | 2019 | Simultaneous Human Health Monitoring and Time-Frequency Sparse Representation Using EEG and ECG Signals | link | IEEE ACCESS | classify MIT-BIH arrhythmia | CNN+generalized minimax-concave (GMC) | MIT-BIH arrhythmia |
| 109 | 2019 | A Novel Wearable Electrocardiogram Classification System Using Convolutional Neural Networks and Active Learning | link | IEEE ACCESS | classify MIT-BIH arrhythmia | CNN + active learning | MIT-BIH arrhythmia |
| 110 | 2019 | Interpretability Analysis of Heartbeat Classification Based on Heartbeat Activity’s Global Sequence Features and BiLSTM-Attention Neural Network | link | IEEE ACCESS | classify MIT-BIH arrhythmia | BiLSTM-Attention | MIT-BIH arrhythmia |
| 111 | 2019 | Morphological Arrhythmia Automated Diagnosis Method Using Gray-Level Co-Occurrence Matrix Enhanced Convolutional Neural Network | link | IEEE ACCESS | classify MIT-BIH arrhythmia | CNN+??? | MIT-BIH arrhythmia |
| 112 | 2019 | Automated Heartbeat Classification Using 3-D Inputs Based on Convolutional Neural Network With Multi-Fields of View | link | IEEE ACCESS | classify MIT-BIH arrhythmia | CNN | MIT-BIH arrhythmia |
| 113 | 2019 | Automated Heartbeat Classification Exploiting Convolutional Neural Network With Channel-Wise Attention | link | IEEE ACCESS | classify MIT-BIH arrhythmia | CNN+Attention | MIT-BIH arrhythmia |
| 114 | 2019 | Impact of ECG Dataset Diversity on Generalization of CNN Model for Detecting QRS Complex | link | IEEE ACCESS | QRS detection | CNN | MIT-BIH arrhythmia, INCART, QT |
| 115 | 2019 | Noise Reduction in ECG Signals Using Fully Convolutional Denoising Autoencoders | link | IEEE ACCESS | Noise Reduction | CNN+DAE | MIT-BIH arrhythmia, NSTDB |
| 116 | 2019 | ECG Arrhythmias Detection Using Auxiliary Classifier Generative Adversarial Network and Residual Network | link | IEEE ACCESS | MIT-BIH + 2018 China physiological signal challenge (CPSC) | GAN(data augmentation)+CRNN | MIT-BIH, CPSC |
| 117 | 2019 | Real-Time Detection of Acute Cognitive Stress Using a Convolutional Neural Network From Electrocardiographic Signal | link | IEEE ACCESS | Detection of Acute Cognitive Stress | CNN | own collected |
| 118 | 2019 | Atrial Fibrillation Detection Using an Improved Multi-Scale Decomposition Enhanced Residual Convolutional Neural Network | link | IEEE ACCESS | Atrial Fibrillation Detection | 1-D CNN, multis-cale residual neural network | Physionet Challenge 2017 |
| 119 | 2019 | ECG Authentication Method Based on Parallel Multi-Scale One-Dimensional Residual Network With Center and Margin Loss | link | IEEE ACCESS | Biometric Human Identification | resnet | Physionet Challenge 2017, PTBDB, ECG-ID, MIT-BIH arrhythmia |
| 120 | 2019 | Automated Detection and Localization of Myocardial Infarction With Staked Sparse Autoencoder and TreeBagger | link | IEEE ACCESS | PTBDB, MI | SAE | PTBDB |
| 121 | 2019 | Preprocessing Method for Performance Enhancement in CNN-Based STEMI Detection From 12-Lead ECG | link | IEEE ACCESS | ST | CNN | SNUBH |
| 122 | 2019 | Classification of Atrial Fibrillation Recurrence Based on a Convolution Neural Network With SVM Architecture | link | IEEE ACCESS | Atrial Fibrillation Detection | CNN+SVM | West China Hospital |
| 123 | 2019 | MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals | link | IJCAI | AF | CNN+RNN+attention | Physionet Challenge 2017 |
| 124 | 2019 | K-margin-based Residual-convolution-recurrent Neural Network for Atrial Fibrillation Detection | link | IJCAI | AF | CNN + BiLSTM | Physionet Challenge 2017 |
| 125 | 2019 | PREDICTION OF THE HEART RATE CORRECTED QT INTERVAL (QTC) FROM A NOVEL, MULTILEAD SMARTPHONE-ENABLED ECG USING A DEEP NEURAL NETWORK | link | JACC | QT | DNN | |
| 126 | 2019 | APPLICATION OF ARTIFICIAL INTELLIGENCE TO DETECT ST ELEVATION MI WITH A SINGLE LEAD EKG | link | JACC | ST MI | CNN | |
| 127 | 2019 | Ambulatory Atrial Fibrillation Monitoring Using Wearable Photoplethysmography with Deep Learning | link | KDD | AF | resnet | own collected |
| 128 | 2019 | Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram | link | Nature medicine | diagnose ALVD on 12 lead | CNN, 6 Conv layers | Mayo |
| 129 | 2019 | Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network | link | Nature medicine | classify 12 rhythm classes | resnet | own collected |
| 130 | 2019 | Adversarial de-noising of electrocardiogram | link | Neurocomputing | Denoising | GAN | MIT-BIH arrhythmia, MIT-BIH Noise Stress Test |
| 131 | 2019 | Artificial intelligence to predict needs for urgent revascularization from 12-leads electrocardiography in emergency patients | link | PlosOne | predict needs for urgent revascularization | CNN+RNN | own collected |
| 132 | 2019 | Mixed convolutional and long short-term memory network for the detection of lethal ventricular arrhythmia | link | PlosOne | detection of VF | CNN+RNN | VFDB, CUDB, AHADB |
| 133 | 2019 | Ventricular ectopic beat detection using a wavelet transform and a convolutional neural network | link | PMEA | Ventricular ectopic beat | CNN | MIT-BIH arrhythmia, AHADB |
| 134 | 2019 | Combining deep neural networks and engineered features for cardiac arrhythmia detection from ECG recordings | link | PMEA | arrhythmia | resnet+expert | Physionet Challenge 2017 |
| 135 | 2019 | Electrocardiogram generation with a bidirectional LSTM-CNN generative adversarial network | link | SR | data generation | BiLSTM+CNN+GAN | MIT-BIH arrhythmia |
| 136 | 2019 | Sequential Factorized Autoencoder for Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms | link | TBME | localizing the origin of VT | RNN + AE | own collected |
| 137 | 2019 | PGANs: Personalized Generative Adversarial Networks for ECG Synthesis to Improve Patient-Specific Deep ECG Classification | link | AAAI | semi-supervised classification | GAN+LSTM | MIT-BIH arrhythmia |
| 138 | 2019 | Inter-Patient ECG Classification with Symbolic Representations and Multi-Perspective Convolutional Neural Networks | link | JBHI | classify MIT-BIH arrhythmia | symbolic features + CNN | MIT-BIH arrhythmia |
| 139 | 2019 | Detection of First-Degree Atrioventricular Block on Variable-Length Electrocardiogram via a Multimodal Deep Learning Method | link | CinC | IAVB | CRNN | CPSC |
| 140 | 2019 | Fetal Electrocardiography and Deep Learning for Prenatal Detection of Congenital Heart Disease | link | CinC | CHD | CNN | own collected |
| 141 | 2019 | U-Net Architecture for the Automatic Detection and Delineation of the Electrocardiogram | link | CinC | segmentation | CNN (unet) | QTDB |
| 142 | 2019 | Pay Attention and Watch Temporal Correlation: A Novel 1-D Convolutional Neural Network for ECG Record Classification | link | CinC | classify | CRNN | CPSC, Physionet Challenge 2017 |
| 143 | 2019 | Deep Convolutional Encoder-Decoder Framework for Fetal ECG Signal Denoising | link | CinC | denoisig | CNN (encoder decoder) | Fetal ECG Synthetic database of Physionet |
| 144 | 2019 | PVC Recognition for Wearable ECGs Using Modified Frequency Slice Wavelet Transform and Convolutional Neural Network | link | CinC | PVC | CNN | CPSC |
| 145 | 2019 | Validation of a Deep Learning-Enabled Electrocardiogram Algorithm to Detect and Predict Cardiac Contractile Dysfunction in the Community | link | Circulation | disease | unclear | own collected |
| 146 | 2019 | Deep Neural Networks Can Predict 1-Year Mortality Directly From ECG Signal, Even When Clinically Interpreted as Normal | link | Circulation | mortality | unclear | own collected |
| 147 | 2019 | A Deep Neural Network for Predicting Incident Atrial Fibrillation Directly From 12-Lead Electrocardiogram Traces | link | Circulation | AF | unclear | own collected |
| 148 | 2019 | A Deep Learning Model to Predict Outcome After Thoracoscopic Surgery for Atrial Fibrillation Using Single Beat Electrocardiographic Samples | link | Circulation | Benefits of Thoracoscopic surgery for atrial fibrillation | CNN | own collected |
| 149 | 2019 | Developing Convolutional Neural Networks for Deep Learning of Ventricular Action Potentials to Predict Risk for Ventricular Arrhythmias | link | Circulation | Ventricular Arrhythmias | CNN | own collected |
| 150 | 2019 | Age and Sex Estimation Using Artificial Intelligence From Standard 12-Lead ECGs | link | Circulation | demographics | CNN | own collected |
| 151 | 2019 | Automated and Interpretable Patient ECG Profiles for Disease Detection, Tracking, and Discovery | link | Circulation | segmentation | CNN + HMM | own collected |
| 152 | 2019 | BeatGAN: Anomalous Rhythm Detection using Adversarially Generated Time Series | link | IJCAI | beat level anomaly detection | CNN + GAN | MIT-BIH arrhythmia |
| 153 | 2019 | Artificial intelligence algorithm for predicting mortality of patients with acute heart failure | link | PlosOne | mortality | DNN | own collected |
| 154 | 2019 | Detection of atrial fibrillation and other abnormal rhythms from ECG using a multi-layer classifier architecture | link | PMEA | arrhythmia | MLP | Physionet Challenge 2017 |
| 155 | 2019 | Deep-ECG: Convolutional Neural Networks for ECG biometric recognition | link | PRL | identification | CNN | IDEAL, PTBDB |
| 156 | 2019 | Classification of myocardial infarction with multi-lead ECG signals and deep CNN | link | PRL | MI | CNN | PTBDB |
| 157 | 2019 | ECG-based personal recognition using a convolutional neural network | link | PRL | identification | CNN | PTBDB, CEBSDB, NSRDB, MITDB |
| 158 | 2019 | Deep learning approaches for automatic detection of sleep apnea events from an electrocardiogram | link | CMPB | sleep | DNN, CNN, RNN | own collected |
| 159 | 2019 | A new approach for arrhythmia classification using deep coded features and LSTM networks | link | CMPB | classify MIT-BIH arrhythmia | CNN + RNN, AE | MIT-BIH arrhythmia |
| 160 | 2019 | A deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation | link | JE | ECG interpretation | CNN | own collected |
| 161 | 2019 | Deep learning to automatically interpret images of the electrocardiogram: Do we need the raw samples? | link | JE | arrhythmia | CNN | Physionet Challenge 2017 |
| 162 | 2019 | Dual-Input Neural Network Integrating Feature Extraction and Deep Learning for Coronary Artery Disease Detection Using Electrocardiogram and Phonocardiogram | link | IEEE ACCESS | CAD | CRNN + expert | own collected |
| 163 | 2019 | A Deep Bidirectional GRU Network Model for Biometric Electrocardiogram Classification Based on Recurrent Neural Networks | link | IEEE ACCESS | identification | CRNN | ECGID, MITDB |
| 164 | 2019 | Localization of Myocardial Infarction With Multi-Lead Bidirectional Gated Recurrent Unit Neural Network | link | IEEE ACCESS | MI | RNN | PTBDB |
| 165 | 2019 | Feature Enrichment Based Convolutional Neural Network for Heartbeat Classification From Electrocardiogram | link | IEEE ACCESS | classify MIT-BIH arrhythmia | CNN | MIT-BIH arrhythmia |
| 166 | 2019 | ECG Generation With Sequence Generative Adversarial Nets Optimized by Policy Gradient | link | IEEE ACCESS | ECG generation | CNN + GAN | MIT-BIH arrhythmia |
| 167 | 2019 | Dense Convolutional Networks With Focal Loss and Image Generation for Electrocardiogram Classification | link | IEEE ACCESS | classify MIT-BIH arrhythmia | CNN | MIT-BIH arrhythmia |
| 168 | 2019 | An Automatic System for Real-Time Identifying Atrial Fibrillation by Using a Lightweight Convolutional Neural Network | link | IEEE ACCESS | AF | CNN + experts | AFDB |
| 169 | 2019 | Biosignal Generation and Latent Variable Analysis With Recurrent Generative Adversarial Networks | link | IEEE ACCESS | ECG generation | RNN + GAN | UCR |
| 170 | 2019 | A Parallel GRU Recurrent Network Model and its Application to Multi-Channel Time-Varying Signal Classification | link | IEEE ACCESS | classify | RNN | CCDD |
| 171 | 2019 | Inter-Patient CNN-LSTM for QRS Complex Detection in Noisy ECG Signals | link | IEEE ACCESS | QRS detection | CRNN | MIT-BIH arrhythmia, European ST-T |
| 172 | 2019 | ECG Biometric Recognition: Template-Free Approaches Based on Deep Learning | link | EMBC | identification | CNN | PTBDB |
| 173 | 2019 | Deep Learning Based Patient-Specific Classification of Arrhythmia on ECG signal | link | EMBC | classify MIT-BIH arrhythmia | CNN | MIT-BIH arrhythmia |
| 174 | 2019 | A Deep Learning Method to Detect Atrial Fibrillation Based on Continuous Wavelet Transform | link | EMBC | AF | CNN | MIT-BIH Arrhythmia Database (DB1) [11], the MIT-BIH Malignant Ventricular Arrhythmia Database (DB2) [12], the MIT-BIH Atrial Fibrillation Database (DB3) [13], the Long- Term AF Database (DB4) [14], the MIT-BIH Normal Sinus Rhythm Database (DB5) [15] and the MIT-BIH Noise Stress Test Database (DB6) [16] |
| 175 | 2019 | An Electrocardiogram Delineator via Deep Segmentation Network | link | EMBC | annotate ECG waves | CNN | QTDB |
| 176 | 2019 | Spectro-Temporal Feature Based Multi-Channel Convolutional Neural Network for ECG Beat Classification | link | EMBC | beat classify | CNN + experts | MIT-BIH arrhythmia |
| 177 | 2019 | Cardiovascular disease diagnosis using cross-domain transfer learning | link | EMBC | classify | CNN | CPSC |
| 178 | 2020 | LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices | link | JBHI | classify MIT-BIH arrhythmia | LSTM | MIT-BIH arrhythmia |
| 179 | 2020 | Atrial Fibrillation Prediction With Residual Network Using Sensitivity and Orthogonality Constraints | link | JBHI | AF | spectrogram + ResNet CNN | AFDB, Physionet Challenge 2017 |
| 180 | 2020 | MFB-CBRNN: A Hybrid Network for MI Detection Using 12-Lead ECGs | link | JBHI | MI | CRNN | PTBDB |
| 181 | 2020 | Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis | link | Circulation | LVEF | CNN | own collected |
| 182 | 2020 | Toward improving ECG biometric identification using cascaded convolutional neural networks | link | Neurocomputing | identification | CNN | FANTASIA, CEBSDB, NSRDB, STDB, AFDB |
| 183 | 2020 | Single-modal and multi-modal false arrhythmia alarm reduction using attention- based convolutional and recurrent neural networks | link | PlosOne | arrhythmia | CRNN | PhysioNet challenge 2015 |
| 184 | 2020 | Detection of strict left bundle branch block by neural network and a method to test detection consistency | link | PMEA | LBBB | DNN | International Society for Computerized Electrocardiology (ISCE) and the Telemetric and Holter Initiative (THEW-project.org) held a competition |
| 185 | 2020 | End-to-end trained encoder–decoder convolutional neural network for fetal electrocardiogram signal denoising | link | PMEA | denoisig | CNN (encoder decoder) | Fetal ECG Synthetic database of Physionet |
| 186 | 2020 | Precision Medicine and Artificial Intelligence: A Pilot Study on Deep Learning for Hypoglycemic Events Detection based on ECG | link | SR | Hypoglycemic Events Detection | CRNN | own collected |
| 187 | 2020 | Transfer Learning in ECG Classification from Human to Horse Using a Novel Parallel Neural Network Architecture | link | SR | transfer diagnosis from human to horse | CNN | MIT-BIH arrhythmia, equine electrocardiogram |
| 188 | 2020 | Multi-branch fusion network for Myocardial infarction screening from 12-lead ECG images | link | CMPB | MI | CNN | own collected |
| 189 | 2020 | ML–ResNet: A novel network to detect and locate myocardial infarction using 12 leads ECG | link | CMPB | MI | CNN | PTBDB |
| 190 | 2020 | Heartbeat classification using deep residual convolutional neural network from 2-lead electrocardiogram | link | JE | classify MIT-BIH arrhythmia | CNN | MIT-BIH arrhythmia |
| 191 | 2020 | Generalization of Convolutional Neural Networks for ECG Classification Using Generative Adversarial Networks | link | IEEE ACCESS | ECG generation | CNN + GAN | MIT-BIH arrhythmia |
| 192 | 2020 | Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network | link | Nature Medicine | Mortality prediction | CNN | US health system (Geisinger) |
| 193 | 2020 | Deep learning models for electrocardiograms are susceptible to adversarial attack | link | Nature Medicine | Adversarial attack in disease detection | GAN | PhysioNet Challenge 2017 |
| 194 | 2019 | An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction | link | The Lancet | AF detection during sinus rhythm | CNN | Mayo Clinic |
| 195 | 2020 | Automatic Multi-Label ECG Diagnosis of Impulse or Conduction Abnormalities in Patients with Deep Learning Algorithm: A Cohort Study | link | The Lancet Digital Health | Arrhythmias | CNN | Three campuses of Tongji Hospital (Huazhong University of Science and Technology, Wuhan, China) and CPSC |
| 196 | 2020 | Improve robustness of DNN for ECG signal classification: a noise-to-signal ratio perspective | link | ICLR2020, AI4AH workshop | Improving robustness against adversarial noises | MLP+CNN | MIT-BIH Arrhythmia |
| 196 | 2019 | Deep Learning Applied to Attractor Images Derived from ECG Signals for Detection of Genetic Mutation | link | CinC 2019 | Generating attractor images from ECG signals | Symmetric Projection Attractor Reconstruction (SPAR) + pre-trained network from Matlab 2019a Deep Learning Toolbox | 42 anaesthetised mice |