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

Topological Data Analysis Approach to Extract the Persistent Homology Features of Ballistocardiogram Signal in Unobstructive Atrial Fibrillation Detection
Fangfang Jiang, Bowen Xu, Ziyu Zhu et al. IEEE Sensors Journal, 2022. Cites: paper

An accurate and efficient method to train classifiers for atrial fibrillation detection in ECGs: Learning by asking better questions
Fons J. Wesselius, Mathijs S. van Schie, Natasja M. S. de Groot et al. Computers in Biology and Medicine, 2022. Cites: paper

AppAxO : Designing App lication-specific A ppro x imate O perators for FPGA-based Embedded Systems
Salim Ullah, Siva Satyendra Sahoo, Nemath Ahmed et al. ACM Transactions on Embedded Computing Systems, 2022. Cites: paper

Artificial intelligence for the detection, prediction, and management of atrial fibrillation
Jonas Lynggaard Isaksen, Mathias Baumert, Astrid N L Hermans et al. Herzschrittmachertherapie + Elektrophysiologie, 2022. Cites: paper

A Review of Recurrent Neural Network-Based Methods in Computational Physiology
Shitong Mao, Ervin Sejdić. IEEE Transactions on Neural Networks and Learning Systems, 2022. Cites: paper

Atrial Fibrillation Detection Using a Feedforward Neural Network
Yunfan Chen, Chong Zhang, Chengyu Liu et al. Journal of Medical and Biological Engineering, 2022. Cites: paper

Edge2Analysis: A Novel AIoT Platform for Atrial Fibrillation Recognition and Detection
Wanqing Wu, Jia‐Rong Chen, Yingfang Zheng et al. 2022. Cites: paper

Inter Patient Atrial Fibrillation Classification Using One Dimensional Convolution Neural Network
Ahmad Rifa’i, Muhammad Naufal Rachmamtullah, Sutarno Sutarno et al. Computer Engineering and Applications Journal, 2022. Cites: paper

Deep-learning online EEG decoding brain-computer interface using error-related potentials recorded with a consumer-grade headset
Dorina Ancău, Mircea Ancău, Mihai Ancău. Biomedical Physics & Engineering Express, 2022. Cites: paper

Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020
Shenda Hong, Wenrui Zhang, Chenxi Sun et al. Frontiers in Physiology, 2022. Cites: paper

System on Chip (SoC) for Invisible Electrocardiography (ECG) Biometrics
Francisco de Melo, Horácio C. Neto, Hugo Silva. Sensors, 2022. Cites: paper

Abnormal heart rhythms
Anirban Dutta Choudhury, Rohan Banerjee, Sanjay Kimbahune et al. Elsevier eBooks, 2022. Cites: paper

A Multistep Paroxysmal Atrial Fibrillation Scanning Strategy in Long-Term ECGs
Caiyun Ma, Chengyu Liu, Xingyao Wang et al. IEEE Transactions on Instrumentation and Measurement, 2022. Cites: paper

Arithmetic Optimization Algorithm with Explainable Artificial Intelligence Technique for Biomedical Signal Analysis
Mahmoud Ragab, Nofe Ateq Alganmi. Intelligent systems reference library, 2022. Cites: paper

Attention-Based Neural Bag-of-Features Learning for Sequence Data
Dat Thanh Tran, Nikolaos Passalis, Anastasios Tefas et al. IEEE Access, 2022. Cites: paper

Automatic Cardiac Arrhythmia Classification Using Residual Network Combined With Long Short-Term Memory
Yun Kwan Kim, Minji Lee, Hee Seok Song et al. IEEE Transactions on Instrumentation and Measurement, 2022. Cites: paper

Automatic Detection of Atrial Fibrillation from Single-Lead ECG Using Deep Learning of the Cardiac Cycle
Alina Dubatovka, Joachim M. Buhmann. BME Frontiers, 2022. Cites: paper

Classification Techniques for Arrhythmia Patterns Using Convolutional Neural Networks and Internet of Things (IoT) Devices
Michael Opoku Agyeman, Andres Felipe Guerrero, Quoc‐Tuan Vien. IEEE Access, 2022. Cites: paper

Data and signals for the assessment of the cardiovascular system
Francisco Castells, Raquel Cervigón, José Millet. Elsevier eBooks, 2022. Cites: paper

Deep learning approaches for the cardiovascular disease diagnosis using smartphone
Abdülhamit Subaşı, Elina Kontio, Mojtaba Jafaritadi. Elsevier eBooks, 2022. Cites: paper

Demonstrating Analog Inference on the BrainScaleS-2 Mobile System
Yannik Stradmann, Sebastian Billaudelle, Oliver Breitwieser et al. IEEE Open Journal of Circuits and Systems, 2022. Cites: paper

Detection of Paroxysmal Atrial Fibrillation from Dynamic ECG Recordings Based on a Deep Learning Model
Yating Hu, Tengfei Feng, Miao Wang et al. SSRN Electronic Journal, 2022. Cites: paper

ECG signal classification using machine learning techniques
M. Kovalchuk, Vyacheslav Serhiiovych Kharchenko, A. Yavorskyi et al. Bulletin of Taras Shevchenko National University of Kyiv Series Physics and Mathematics, 2022. Cites: paper

Electrocardiogram Effective Analysis Based on the Random Forest Model with Preselected Parameters
Taras Panchenko, Andrii Yavorskyi, Zhengbing Hu. Lecture notes on data engineering and communications technologies, 2022. Cites: paper

Embedded Machine Learning Using Microcontrollers in Wearable and Ambulatory Systems for Health and Care Applications: A Review
Maha S. Diab, Esther Rodriguez–Villegas. IEEE Access, 2022. Cites: paper

Evaluation of Transfer Learning to Improve Arrhythmia Classification for a Small ECG Database
Larissa Montenegro, Hugo Peixoto, Jose Manuel Machado. Lecture notes in computer science, 2022. Cites: paper

Heartbeat Classification by Random Forest With a Novel Context Feature: A Segment Label
Congyu Zou, Alexander Muller, Wolfgang Utschick et al. IEEE Journal of Translational Engineering in Health and Medicine, 2022. Cites: paper

Meticulous Presaging Arrhythmia Fibrillation for Heart Disease Classification Using Oversampling Method for Multiple Classifiers Based on Machine Learning
Ritu Aggarwal, Prateek Thakral. Lecture notes on data engineering and communications technologies, 2022. Cites: paper

Personalized decision support for cardiology based on deep learning: an overview
Ling Chen, Vincent S. M. Tseng, HSUAN‐MING TSAO et al. Elsevier eBooks, 2022. Cites: paper

RandECG: Data Augmentation for Deep Neural Network Based ECG Classification
Naoki Nonaka, Jun Seita. Advances in intelligent systems and computing, 2022. Cites: paper

Multi-Scale Convolutional Neural Network Ensemble for Multi-Class Arrhythmia Classification
Eedara Prabhakararao, Samarendra Dandapat. IEEE Journal of Biomedical and Health Informatics, 2021. Cites: paper

Binary classification for atrial fibrillation detection from ECG-based signals using 1D-convolutional neural network
Yuxi Chen, Huayiting Qiao, Alexander Wen. International Conference on Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), 2021. Cites: paper

Novel feature extraction method for signal analysis based on independent component analysis and wavelet transform
Mariusz Topolski, Jędrzej Kozal. PLoS ONE, 2021. Cites: paper

Remote Arrhythmia Detection for Eldercare in Malaysia
Kevin Thomas Chew, Valliappan Raman, Patrick Then. Sensors, 2021. Cites: paper

Atrial Fibrillation Detection by Multi-Lead ECG Processing at the Edge
Andrea Petroni, Francesca Cuomo, Gaetano Scarano et al. IEEE Globecom Workshops (GC Wkshps), 2021. Cites: paper

ECG signal classification based on deep CNN and BiLSTM
Jinyong Cheng, Qingxu Zou, Yunxiang Zhao. BMC Medical Informatics and Decision Making, 2021. Cites: paper

End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge Devices
J B Sideshwar, Sachin Krishan T, Vishal Nagarajan et al. International Conference on Machine Learning and Applications, 2021. Cites: paper

Efficient on-site confirmatory testing for atrial fibrillation with derived 12-lead ECG in a wireless body area network
Aneesh M. Koya, P. P. Deepthi. Journal of Ambient Intelligence and Humanized Computing, 2021. Cites: paper

A Framework for Segmentation and Classification of Arrhythmia Using Novel Bidirectional LSTM Network
Archana K. Ratnaparkhi, Pallavi D. Deshpande, Gauri Ghule. International Journal of Computing and Digital Systems, 2021. Cites: paper

End-to-End Optimized Arrhythmia Detection Pipeline using Machine Learning for Ultra-Edge Devices
Sideshwar J. B, Sachin Krishan T, Vishal Nagarajan et al. arXiv (Cornell University), 2021. Cites: paper

Atrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach
Ali Bahrami Rad, Conner D. Galloway, Daniel L. Treiman et al. PLoS ONE, 2021. Cites: paper

Hybrid feature fusion for classification optimization of short ECG segment in IoT based intelligent healthcare system
Xianbin Zhang, Mingzhe Jiang, Wanqing Wu et al. Neural Computing and Applications, 2021. Cites: paper

Ensemble Learning for Atrial Fibrillation Screening from a Single Lead ECG Wave of Wearable Devices
Yu Liu, Bo Fang, Yue Zhao et al. IEEE International Conference on Frontiers Technology of Information and Computer (ICFTIC), 2021. Cites: paper

Training neural networks with synthetic electrocardiograms
Matti Kaisti, Juho Laitala, Antti Airola. arXiv (Cornell University), 2021. Cites: paper

Atrial Fibrillation Detection on Low-Power Wearables using Knowledge Distillation
Antonino Faraone, Halla Sigurthorsdottir, Ricard Delgado-Gonzalo. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021. Cites: paper

Deep Learning-Based Data-Point Precise R-Peak Detection in Single-Lead Electrocardiograms
Marinka Oudkerk Pool, Bob D. de Vos, Michiel M. Winter et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021. Cites: paper

ECG Signal Detection via Bidirectional Projection Clustering-enhanced Hashing
Xiaoyun Yi, Wenrui Lv, Li Qi et al. International Conference on Artificial Intelligence and Computer Engineering (ICAICE), 2021. Cites: paper

Review of Deep Learning-Based Atrial Fibrillation Detection Studies
Fatma Murat, Ferhat Sadak, Özal Yıldırım et al. International Journal of Environmental Research and Public Health, 2021. Cites: paper

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