Publications citing PTB-XL, a large publicly available electrocardiography dataset v1.0.3

Masked Autoencoder for ECG Representation Learning
Shunxiang Yang, Cheng Lian, Zhigang Zeng. International Conference on Information Science and Technology, 2022. Cites: paper

Loss Optimization Based Algorithm for Multi-classification of ECG Signal
Junhui Liu, Ming Zeng, Ke Shan et al. International Symposium on Artificial Intelligence for Medicine Sciences (ISAIMS), 2022. Cites: paper

MCA-net: A multi-task channel attention network for Myocardial infarction detection and location using 12-lead ECGs
Weibai Pan, Ying An, Yuxia Guan et al. Computers in Biology and Medicine, 2022. Cites: paper

A CVD Critical Level-aware Scheduling Model Based on Reinforcement Learning for ECG Service Request
Jian Gao, Jie Yu, Ning Wang et al. International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2022. Cites: paper

QT-STNet: A Spatial and Temporal Network Combined with QT Segment for MI Detection and Location
Jie Yu, Jian Gao, Ning Wang et al. International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2022. Cites: paper

Myocardial Ischemia Detection Using a Reduced Number of ECG Leads
Anton Volodymyrovych Mnevets, Н. Г. Иванушкина, Kateryna Olehivna Ivanko. Вісник Національного технічного університету України Київський політехнічний інститут. Серія: Радіотехніка. Радіоапаратобудування, 2022. Cites: paper

A personalized real-time virtual model of whole heart electrophysiology
Karli K. Gillette, Matthias A. F. Gsell, Marina Strocchi et al. Frontiers in Physiology, 2022. Cites: paper

Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data
Dmytro Chumachenko, Mykola Butkevych, Daniel Lode et al. Sensors, 2022. Cites: paper

Ensemble classification combining ResNet and handcrafted features with three-steps training
Guadalupe García Isla, Federico Muscato, Andrea Sansonetti et al. Physiological Measurement, 2022. Cites: paper

Automated Detection of Myocardial Infarction and Heart Conduction Disorders Based on Feature Selection and a Deep Learning Model
Mohamed Adel Hammad, Samia Allaoua Chelloug, Reem Ibrahim Alkanhel et al. Sensors, 2022. Cites: paper

The health digital twin to tackle cardiovascular disease—a review of an emerging interdisciplinary field
Genevieve Coorey, Gemma A. Figtree, David Frederick Fletcher et al. npj Digital Medicine, 2022. Cites: paper

Transfer learning enables prediction of myocardial injury from continuous single-lead electrocardiography
Boyang Tom Jin, Raj Palleti, Siyu Shi et al. Journal of the American Medical Informatics Association, 2022. Cites: paper

Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models
Juan Miguel Lopez Alcaraz, Nils Strodthoff. arXiv (Cornell University), 2022. Cites: paper

A Novel Constraint-Based Knee- Guided Neuroevolutionary Algorithm for Context-Specific ECG Early Classification
Yu Huang, Gary G. Yen, Vincent S. M. Tseng. IEEE Journal of Biomedical and Health Informatics, 2022. Cites: paper

An ECG generative model of myocardial infarction
Wenge Que, Chuang Han, Xiliang Zhao et al. Computer Methods and Programs in Biomedicine, 2022. Cites: paper

Enhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation
Hossein Honarvar, Chirag Agarwal, Sulaiman S. Somani et al. Cardiovascular Digital Health Journal, 2022. Cites: paper

Lightweight Multireceptive Field CNN for 12-Lead ECG Signal Classification
Degaga Wolde Feyisa, Taye Girma Debelee, Yehualashet Megersa Ayano et al. Computational Intelligence and Neuroscience, 2022. Cites: paper

A Domain Generalization Approach for Out-Of-Distribution 12-lead ECG Classification with Convolutional Neural Networks
Aristotelis Ballas, Christos Diou. IEEE International Conference on Big Data Computing Service and Applications (BigDataService), 2022. Cites: paper

Denoising of ECG with single and multiple hidden layer autoencoders
Bagas Marwan, FARS ESMAT SAMANN, Thomas Schanze. Current Directions in Biomedical Engineering, 2022. Cites: paper

On the use of singular value decomposition for QRS detection and ECG denoising
Thomas Schanze. Current Directions in Biomedical Engineering, 2022. Cites: dataset

Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset
Kushagra Sharma, Rasit Eskicioglu. International Journal of Artificial Intelligence & Applications, 2022. Cites: paper

Cardiac Arrhythmia classification based on 3D recurrence plot analysis and deep learning
Hua Zhang, Chengyu Liu, Fangfang Tang et al. Frontiers in Physiology, 2022. Cites: paper

Deep-Learning-Based Estimation of the Spatial QRS-T Angle from Reduced-Lead ECGs
Ana Santos Rodrigues, Rytis Augustauskas, Mantas Lukoševičius et al. Sensors, 2022. Cites: paper

Analysis of Augmentations for Contrastive ECG Representation Learning
Sahar Soltanieh, Ali Etemad, Javad Hashemi. International Joint Conference on Neural Networks (IJCNN), 2022. Cites: paper

Improving Deep Learning-based Cardiac Abnormality Detection in 12-Lead ECG with Data Augmentation
Jingna Qiu, Maximilian P. Oppelt, Michael Nissen et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022. Cites: paper

Issues in the automated classification of multilead ecgs using heterogeneous labels and populations
Matthew A. Reyna, Nadi Sadr, Erick Andres Perez Alday et al. Physiological Measurement, 2022. Cites: paper

Muscle Artifact Removal in Single-Channel Electrocardiograms using Temporal Convolutional Networks
Lukas Boudnik, Jan Graßhoff, Felix Vollmer et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2022. Cites: paper

Snippet Policy Network V2: Knee-Guided Neuroevolution for Multi-Lead ECG Early Classification
Yu Huang, Gary G. Yen, Vincent S. M. Tseng. IEEE Transactions on Neural Networks and Learning Systems, 2022. Cites: paper

Application of artificial intelligence techniques for automated detection of myocardial infarction: a review
Javad Hassannataj Joloudari, Sanaz Mojrian, Issa Nodehi et al. Physiological Measurement, 2022. Cites: paper

Lead Separation and Combination: A Novel Unsupervised 12-Lead ECG Feature Learning Framework for Internet of Medical Things
Wenhan Liu, Qianxi Guo, Xinwei Gao et al. IEEE Internet of Things Journal, 2022. Cites: paper

State-of-the-Art Deep Learning Methods on Electrocardiogram Data: Systematic Review
Γεώργιος Πετμεζάς, Leandros Stefanopoulos, Vassilis Kilintzis et al. JMIR Medical Informatics, 2022. Cites: paper

CACHET-CADB: A Contextualized Ambulatory Electrocardiography Arrhythmia Dataset
Devender Kumar, Sadasivan K Puthusserypady, Helena Domínguez et al. Frontiers in Cardiovascular Medicine, 2022. Cites: paper

ECG Classification Using Orthogonal Matching Pursuit and Machine Learning
Sandra Śmigiel. Sensors, 2022. Cites: paper

Rapid Detection of Cardiac Pathologies by Neural Networks Using ECG Signals (1D) and sECG Images (3D)
Evelyn Aguiar-Salazar, Fernando Villalba-Meneses, Andrés Tirado-Espín et al. Computation, 2022. Cites: paper

Use of Wearable Technology and Deep Learning to Improve the Diagnosis of Brugada Syndrome
Shun Liao, Mahmoud Bokhari, Praloy Chakraborty et al. JACC. Clinical electrophysiology, 2022. Cites: paper

Intra-Inter Subject Self-Supervised Learning for Multivariate Cardiac Signals
Lan Xiang, Dianwen Ng, Shenda Hong et al. Proceedings of the AAAI Conference on Artificial Intelligence, 2022. Cites: paper

Joint ECG–EMG–EEG signal compression and reconstruction with incremental multimodal autoencoder approach
Evangelin Dasan, Rajakumar Gnanaraj. Circuits Systems and Signal Processing, 2022. Cites: dataset

Electrocardiogram Classification Problem Solving using Deep Learning Algorithms : Fully connected Neural Networks
Anwaar Gharaibeh, Muhannad Quwaider. International Conference on Information and Communication Systems (ICICS), 2022. Cites: paper

A large-scale multi-label 12-lead electrocardiogram database with standardized diagnostic statements
Hui Liu, Dan Chen, Da Chen et al. Scientific Data, 2022. Cites: dataset, paper

Aprendizado de Máquina para Predição de Diagnósticos de Doenças Cardiovasculares
Francisco Romes da Silva Filho, Emanuel Ferreira Coutinho. 2022. Cites: paper

Brief Overview of Neural Networks for Medical Applications
Máté Hireš, Peter Bugata, Matej Gazda et al. Acta Electrotechnica et Informatica, 2022. Cites: paper

Efficient and Private ECG Classification on the Edge Using a Modified Split Learning Mechanism
Ahmad Ayad, Marian Frei, Anke Schmeink. IEEE International Conference on Healthcare Informatics (ICHI), 2022. Cites: paper

Multi-label classification of reduced-lead ECGs using an interpretable deep convolutional neural network
Nima L. Wickramasinghe, Mohamed Athif. Physiological Measurement, 2022. Cites: paper

From 12 to 1 ECG lead: multiple cardiac condition detection mixing a hybrid machine learning approach with a one-versus-rest classification strategy
Santiago Jiménez-Serrano, Miguel Rodrigo, Conrado Javier Calvo et al. Physiological Measurement, 2022. Cites: paper

Benchmarking Deep Learning Methods for Arrhythmia Detection
Elena Merdjanovska, Aleksandra Rashkovska. International Convention on Information and Communication Technology, Electronics and Microelectronics, 2022. Cites: paper

Classification and Feature Extraction of Biological Signals Using Machine Learning Techniques
Marina Ciocîrlan, Andreea Udrea. International Conference on Control, Decision and Information Technologies (CoDIT), 2022. Cites: paper

EKG-Diagnostik mithilfe künstlicher Intelligenz: aktueller Stand und zukünftige Perspektiven – Teil 1
Wilhelm Haverkamp, Nils Strodthoff, Carsten Walter Israel. Herzschrittmachertherapie + Elektrophysiologie, 2022. Cites: paper

Comparison of neural basis expansion analysis for interpretable time series (N-BEATS) and recurrent neural networks for heart dysfunction classification
Bartosz Puszkarski, Krzysztof Hryniów, Grzegorz Sarwas. Physiological Measurement, 2022. Cites: paper

Multiple electrocardiogram generator with single-lead electrocardiogram
Hyo-Chang Seo, Gi-Won Yoon, Segyeong Joo et al. Computer Methods and Programs in Biomedicine, 2022. Cites: paper

An efficient deep learning model to predict cardiovascular disease based on ECG signal
Nizar Sakli, Haifa Ghabri, I Ahmed Zouinkh et al. International Multi-Conference on Systems, Signals & Devices (SSD), 2022. Cites: paper

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