Publications citing PTB-XL, a large publicly available electrocardiography dataset v1.0.1

Electrocardiogram lead conversion from single-lead blindly-segmented signals
Sofia C. Beco, João Ribeiro Pinto, Jaime S. Cardoso. BMC Medical Informatics and Decision Making, 2022. Cites: dataset, paper

Ensemble model for ECG Classification
Abdul Haque, Nadeem Akhtar. International Conference on Multimedia, Signal Processing and Communication Technologies (IMPACT), 2022. Cites: paper

A unique cardiac electrocardiographic 3D model. Toward interpretable AI diagnosis
Cristina Rueda, Alejandro Rodríguez-Collado, Itziar Fernández et al. iScience, 2022. Cites: paper

Generalized Generative Deep Learning Models for Biosignal Synthesis and Modality Transfer
Theekshana Dissanayake, Tharindu Fernando, Simon Denman et al. IEEE Journal of Biomedical and Health Informatics, 2022. Cites: paper

Application of Convolutional Dendrite Net for Detection of Myocardial Infarction Using ECG Signals
Xin Ma, Xingwen Fu, Yiqi Sun et al. IEEE Sensors Journal, 2022. Cites: paper

Automatic Diagnosis of Cardiovascular Diseases from ECG Signals using Convolutional Neural Network and Soft Computing Methods
Shimpy Goyal. Research Square, 2022. Cites: paper

Estimating critical values from electrocardiogram using a deep ordinal convolutional neural network
Guodong Wei, Xinxin Di, Wenrui Zhang et al. BMC Medical Informatics and Decision Making, 2022. Cites: paper

MInSC: A VLSI Architecture for Myocardial Infarction Stages Classifier for Wearable Healthcare Applications
Meenali Janveja, Rushik Parmar, Gaurav Trivedi. IEEE Transactions on Circuits & Systems II Express Briefs, 2022. Cites: paper

Development and validation of deep learning ECG-based prediction of myocardial infarction in emergency department patients
Stefan Gustafsson, Daniel Gedon, Erik Lampa et al. Scientific Reports, 2022. Cites: paper

A novel deep neural network heartbeats classifier for heart health monitoring
Velagapudi Swapna Sindhu, Kavuri Jaya Lakshmi, Ameya Sanjanita Tangellamudi et al. International Journal of Intelligent Networks, 2022. Cites: paper

Enhancing Electrocardiogram Classification with Multiple Datasets and Distant Transfer Learning
Kwok Tai Chui, Brij Bhooshan Gupta, Mingbo Zhao et al. Bioengineering, 2022. Cites: paper

MUSE: MUlti-lead Sub-beat ECG for remote AI based atrial fibrillation detection
Andrea Petroni, Francesca Cuomo, Gaetano Scarano et al. Journal of Network and Computer Applications, 2022. Cites: paper

The ΔWaveECG: The differences to the normal 12‑lead ECG amplitudes
Klaudia K. Proniewska, Roger Abächerli, Peter Michael van Dam. Journal of Electrocardiology, 2022. Cites: paper

A novel multi-scale 2D CNN with weighted focal loss for arrhythmias detection on varying-dimensional ECGs
Pan Xia, Zhengling He, Zhongrui Bai et al. Physiological Measurement, 2022. Cites: paper

ML for MI - Integrating Multimodal Information in Machine Learning for Predicting Acute Myocardial Infarction
Ran Xiao, Cheng Ding, Xiao Hu et al. medRxiv, 2022. Cites: paper

Possibility of Using Various Architectures of Convolutional Neural Networks in the Problem of Determining the Type of Rhythm
Denis Rodionov, Denis Karchkov, V. A. Moskalenko et al. Studies in computational intelligence, 2022. Cites: paper

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

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 Alexandra 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

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 Dayana 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

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