Publications citing AF Classification from a Short Single Lead ECG Recording: The PhysioNet/Computing in Cardiology Challenge 2017 v1.0.0

Deep Learning-based Handheld Device-Enabled Symptom-driven Recording: A Pragmatic Approach for the Detection of Post-ablation Atrial Fibrillation Recurrence
Laite Chen, Chenyang Jiang. Cardiovascular Innovations and Applications, 2023. Cites: paper

Designing Application-Specific Approximate Operators
Salim Ullah, Akash S. Kumar. Springer eBooks, 2023. Cites: paper

Diagnostic validation of smart wearable device embedded with single-lead electrocardiogram for arrhythmia detection
Yonghong Niu, Hao Wang, Hong Wang et al. Digital Health, 2023. Cites: paper

Discrimination of Cardiac Abnormalities Based on Multifractal Analysis in Reservoir Computing Framework
Basab Bijoy Purkayastha, Shovan Barma. IEEE Open Journal of Instrumentation and Measurement, 2023. Cites: paper

Enhanced Atrial Fibrillation Detection-based Wavelet Scattering Transform with Time Window Selection and Neural Network Integration
Mohamed Elmehdi Ait Bourkha, Anas Hatim, Dounia Nasir et al. International Journal of Advanced Computer Science and Applications, 2023. Cites: paper

Joint Optimization of a $$\beta $$-VAE for ECG Task-Specific Feature Extraction
Viktor van der Valk, Douwe E. Atsma, Roderick W.C. Scherptong et al. Lecture notes in computer science, 2023. Cites: paper

Simple, efficient, and generalized ECG signal quality assessment method for telemedicine applications
Fotsing Kuetche, Noura Alexendre, Ntsama Eloundou Pascal et al. Informatics in Medicine Unlocked, 2023. Cites: paper

Interpretable Machine Learning Techniques in ECG-Based Heart Disease Classification: A Systematic Review
Yehualashet Megersa Ayano, Friedhelm Schwenker, Bisrat Derebssa Dufera et al. Diagnostics, 2022. Cites: paper

Atrial fibrillation classification and detection from ECG recordings
Ali Fatih Gündüz, Muhammed Fatih Talu. Biomedical Signal Processing and Control, 2022. Cites: paper

An ECG Classification Method Based on Feature Fusion Model
Guan Wencong, Juhar Mohammed Assefa, Fitsum Hailu Assefa et al. International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2022. Cites: paper

Auto-detection of Atrial Fibrillation with Improved Classification and Noise Removal Algorithm along with Dimensionality Reduction Methods
B. Suresh Chander Kapali, K. Kala, S. Pavithra et al. International Conference on Advanced Technologies in Intelligent Control, Environment, Computing & Communication Engineering (ICATIECE), 2022. Cites: paper

Defensive Adversarial Training for Enhancing Robustness of ECG based User Identification
Hongbi Jeong, Junggab Son, Hyunbum Kim et al. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022. Cites: paper

An Effective Atrial Fibrillation Detection from Short Single-Lead Electrocardiogram Recordings Using MCNN-BLSTM Network
Hongpo Zhang, Hongzhuang Gu, Junli Gao et al. Algorithms, 2022. Cites: paper

Deep Learning in the prediction of Angiography-proven Severe Coronary Stenosis in Patients with Apparently Normal Electrocardiograms
Zheng-Kai Xue, Shijia Geng, Shaohua Guo et al. medRxiv, 2022. Cites: paper

Benchmarking Uncertainty Quantification on Biosignal Classification Tasks Under Dataset Shift
Tong Xia, Jing Han, Cecilia Mascolo C. Studies in computational intelligence, 2022. Cites: paper

When less is more powerful: Shapley value attributed ablation with augmented learning for practical time series sensor data classification
Arijit Ukil, Leandro Marín, Antonio J. Jara. PLoS ONE, 2022. Cites: paper

DG-ECG: Multi-stream deep graph learning for the recognition of disease-altered patterns in electrocardiogram
Chen Kan, Zehao Ye, Houliang Zhou et al. Biomedical Signal Processing and Control, 2022. Cites: paper

Detection of Atrial Fibrillation From Variable-Duration ECG Signal Based on Time-Adaptive Densely Network and Feature Enhancement Strategy
Xianbin Zhang, Mingzhe Jiang, Kemal Polat et al. IEEE Journal of Biomedical and Health Informatics, 2022. Cites: paper

An end-to-end model for ECG signals classification based on residual attention network
Xiang Lu, Xingrui Wang, Wanying Zhang et al. Biomedical Signal Processing and Control, 2022. Cites: paper

Consumer-Led Screening for Atrial Fibrillation: Frontier Review of the AF-SCREEN International Collaboration
Axel Brandes, Stavros Stavrakis, Ben Freedman et al. Circulation, 2022. Cites: paper

Automated Generation of Tiny Model for Real-Time ECG Classification on Tiny Edge Devices
Shalini Mukhopadhyay, S. Dey, Avik Ghose et al. ACM Conference on Embedded Networked Sensor Systems (SenSys), 2022. Cites: paper

Explainable AI for clinical and remote health applications: a survey on tabular and time series data
Flavio Di Martino, Franca Delmastro. Artificial Intelligence Review, 2022. Cites: paper

Dataflow Optimizations in a Sub-uW Data-Driven TCN Accelerator for Continuous ECG Monitoring
Johnson Loh, Tobias Gemmeke. IEEE Nordic Circuits and Systems Conference (NorCAS), 2022. Cites: paper

Automatic ECG Quality Assessment Techniques: A Systematic Review
Kirina van der Bijl, Mohamed Elgendi, Carlo Menon. Diagnostics, 2022. Cites: paper

Detection of Cardiac Arrhythmias in Electroc Ardiograms Using Deep Learning
Amenah Alwan Salman, Abdullahi Abdu İbrahim. International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2022. Cites: paper

A comparative study on neural networks for paroxysmal atrial fibrillation events detection from electrocardiography
Hao Wen, Jingsu Kang. Journal of Electrocardiology, 2022. Cites: paper

Dominant noise-aided EMD (DEMD): Extending empirical mode decomposition for noise reduction by incorporating dominant noise and deep classification
Zeinab Shamaee, Mohsen Mivehchy. Biomedical Signal Processing and Control, 2022. Cites: paper

An Overview of Health Monitoring Systems for Arrhythmia Patients
Saoueb Kerdoudi, Larbi Guezouli, Tahar Dilekh. Lecture notes in networks and systems, 2022. Cites: paper

Experimental Convolutional-Recurrent Network in ECG Rhythm for Atrial Fibrillation Classification
Annisa Darmawahyuni, Bambang Tutuko, Muhammad Naufal Rachmatullah et al. International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2022. Cites: paper

Automated Atrial Fibrillation Detection with ECG
Ting-Ruen Wei, Senbao Lu, Yuling Yan. Bioengineering, 2022. Cites: paper

A Systematic Review on Artificial Intelligence-Based Techniques for Diagnosis of Cardiovascular Arrhythmia Diseases: Challenges and Opportunities
Shikha Singhal, Manjeet Kumar. Archives of Computational Methods in Engineering, 2022. Cites: paper

Statistical and Diagnostic Properties of pRRx Parameters in Atrial Fibrillation Detection
Szymon Buś, Konrad Jędrzejewski, Przemysław Guzik. Journal of Clinical Medicine, 2022. Cites: paper

Classification of Arrhythmia Signals Using Hybrid Convolutional Neural Network (CNN) Model
Evelyn Siao Yung Ern, Dzati Athiar Ramli. Intelligent systems reference library, 2022. Cites: paper

A novel deep learning package for electrocardiography research
Hao Wen, Jingsu Kang. Physiological Measurement, 2022. Cites: paper

Automatic Detection of Atrial Fibrillation from ECG Signal Using Hybrid Deep Learning Techniques
Saroj Kumar Pandey, Gaurav Kumar, Shubham Shukla et al. Journal of Sensors, 2022. Cites: paper

Multiscale Encoding of Electrocardiogram Signals with a Residual Network for the Detection of Atrial Fibrillation
Mona N. Alsaleem, Md Saiful Islam, Saad A. Al-Ahmadi et al. Bioengineering, 2022. Cites: paper

Detection of Atrial Fibrillation by Custom Designed Convolutional Neural Network: A Medical Viewpoint
Yasmin Cetintemel, Eda Nur Oner, Çağla Sarvan et al. Innovations in Intelligent Systems and Applications Conference (ASYU), 2022. Cites: paper

Multi-Scale Deep Residual Shrinkage Network for Atrial Fibrillation Recognition
Dayin Shi, Zhiyong Wu, Longbo Zhang et al. International Journal of Computational Intelligence and Applications, 2022. Cites: paper

Single-lead ECG recordings modeling for end-to-end recognition of atrial fibrillation with dual-path RNN
Mou Wang, Sylwan Rahardja, Pasi Fränti et al. Biomedical Signal Processing and Control, 2022. Cites: paper

Contrastive learning for atrial fibrillation detection in challenging scenarios
Karen Fonseca, Sergio Osorio, Jeyson Arley Castillo Bohórquez et al. European Signal Processing Conference (EUSIPCO), 2022. Cites: paper

Self-Attention Neural Bag-of-Features
Kateryna Chumachenko, Alexandros Iosifidis, Moncef Gabbouj. Machine learning for signal processing ..., 2022. Cites: paper

Heart murmur detection from phonocardiogram recordings: The George B. Moody PhysioNet Challenge 2022
Matthew A. Reyna, Yashar Kiarashi, Andoni Elola et al. medRxiv, 2022. Cites: paper

Explanation of HRV Features for Detecting Atrial Fibrillation
Yongho Lee, Vinh Pham, Tai‐Myoung Chung. SN Computer Science, 2022. Cites: paper

Detection and Classification of Arrhythmias by Deploying Deep Learning Models
T Padma, A Uday Kiran, C Jahnavi et al. Journal of Physics Conference Series, 2022. Cites: paper

Human-Assisted vs. Deep Learning Feature Extraction: An Evaluation of ECG Features Extraction Methods for Arrhythmia Classification Using Machine Learning
Larissa Montenegro, Mariana Abreu, Ana L. N. Fred et al. Applied Sciences, 2022. Cites: paper

A systematic review of deep learning methods for modeling electrocardiograms during sleep
Chenxi Sun, Shenda Hong, Jingyu Wang et al. Physiological Measurement, 2022. Cites: paper

Atrial Fibrillation Classification and Prediction Explanation using Transformer Neural Network
Deepankar Nankani, Rashmi Dutta Baruah. International Joint Conference on Neural Networks (IJCNN), 2022. Cites: paper

Training Convolutional Neural Networks on Simulated Photoplethysmography Data: Application to Bradycardia and Tachycardia Detection
Andrius Sološenko, Birutė Paliakaitė, Vaidotas Marozas et al. Frontiers in Physiology, 2022. Cites: paper

Development and performance evaluation of a coronary artery disease prediction system with transfer learned model based on single lead and multi-lead ECG & TMT-ECG signals
T. V. Shrivathsa, Cherukupally Kalyan, Shrikantha Sasihithlu Rao et al. International Journal of Health Sciences, 2022. Cites: paper

A Deep Learning Scheme for Detecting Atrial Fibrillation Based on Fusion of Raw and Discrete Wavelet Transformed ECG Features
Md Awsafur Rahman, Shahed Ahmed, Shaikh Anowarul Fattah. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022. Cites: paper

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