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

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

Estimating Critical Value from Electrocardiogram using a Deep Ordinal Convolutional Neural Network
Guodong Wei, Xinxin Di, Wenrui Zhang et al. Research Square, 2022. Cites: paper

Multi-domain Feature Fusion Neural Network for Electrocardiogram Classification
Nan Jiao, Zhanquan Sun. Research Square, 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

ResNet-50 for 12-Lead Electrocardiogram Automated Diagnosis
Nizar Sakli, Haifa Ghabri, Ben Othman Soufiene et al. Computational Intelligence and Neuroscience, 2022. Cites: paper

A study on several critical problems on arrhythmia detection using varying-dimensional electrocardiography
Jingsu Kang, Hao Wen. Physiological Measurement, 2022. Cites: paper

Comprehensive survey of computational ECG analysis: Databases, methods and applications
Elena Merdjanovska, Aleksandra Rashkovska. Expert Systems with Applications, 2022. Cites: paper

Automatic ECG classification and label quality in training data
Ľubomír Antoni, Erik Bruoth, Peter Bugata et al. Physiological Measurement, 2022. Cites: paper

Compelling new electrocardiographic markers for automatic diagnosis
Cristina Rueda, Itziar Fernández, Yolanda Larriba et al. Computer Methods and Programs in Biomedicine, 2022. Cites: paper

Future Techniques and Perspectives on Implanted and Wearable Heart Failure Detection Devices
Muhammad E. H. Chowdhury, Amith Khandaker, Yazan Qiblawey et al. 2022. Cites: paper

Preserving Privacy of High-Dimensional Data by l-Diverse Constrained Slicing
Zenab Amin, Adeel Anjum, Abid Khan et al. Electronics, 2022. Cites: paper

The Optimization of ECG-Leads Based Cardiovascular Abnormal Detection Scheme
Warda R. Mohammed, Ahmed Faeq Hussein. IEEE International Conference on Computing and Machine Intelligence (ICMI), 2022. Cites: paper

Predict alone, decide together: cardiac abnormality detection based on single lead classifier voting
Pierre Gabriel Aublin, Mouïn Ben Ammar, Jérémy Fix et al. Physiological Measurement, 2022. Cites: paper

XBeats: A Real-Time Electrocardiogram Monitoring and Analysis System
Ahmed Badr, Abeer A. Badawi, Abdulmonem M. Rashwan et al. Signals, 2022. Cites: paper

Federated Learning Stability Under Byzantine Attacks
Ala Gouissem, Khalid Abualsaud, Elias E. Yaacoub et al. IEEE Wireless Communications and Networking Conference, 2022. Cites: paper

State-of-the-art Deep Learning Methods on Electrocardiogram Data: A Systematic Review (Preprint)
Γεώργιος Πετμεζάς, Leandros Stefanopoulos, Vassiis Kilintzis et al. 2022. Cites: paper

CNN-FWS: A Model for the Diagnosis of Normal and Abnormal ECG with Feature Adaptive
Junjiang Zhu, Jintao Lv, Dongdong Kong. Entropy, 2022. Cites: paper

Deep Learning for Detecting and Locating Myocardial Infarction by Electrocardiogram: A Literature Review
Ping Xiong, Simon Ming‐Yuen Lee, Ging Chan. Frontiers in Cardiovascular Medicine, 2022. Cites: paper

Automated multilabel diagnosis on electrocardiographic images and signals
Veer Sangha, Bobak J. Mortazavi, Adrian D. Haimovich et al. Nature Communications, 2022. Cites: paper

A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification
Rui Fang, Chih‐Cheng Lu, Cheng-Ta Chuang et al. Computer Methods and Programs in Biomedicine, 2022. Cites: dataset, paper

Generalizable Beat-by-Beat Arrhythmia Detection by Using Weakly Supervised Deep Learning
Yang Liu, Qince Li, Runnan He et al. Frontiers in Physiology, 2022. Cites: paper

WaSP-ECG: A Wave Segmentation Pretraining Toolkit for Electrocardiogram Analysis
Rob Brisk, Raymond R. Bond, Dewar Finlay et al. Frontiers in Physiology, 2022. Cites: paper

Unsupervised ECG Analysis: A Review
Kasra Nezamabadi, Neda Sardaripour, Benyamin Haghi et al. IEEE Reviews in Biomedical Engineering, 2022. Cites: paper

A transformer-based deep neural network for arrhythmia detection using continuous ECG signals
Rui Min Hu, Jie Chen, Li Zhou. Computers in Biology and Medicine, 2022. Cites: paper

Explainable AI decision model for ECG data of cardiac disorders
Atul Anand, Tushar Kadian, Manu Kumar Shetty et al. Biomedical Signal Processing and Control, 2022. Cites: paper

Real-Time Deep Compressed Sensing Reconstruction for Electrocardiogram Signals
Weibin Cao, Jun Zhang. International Conference on Machine Learning and Computing (ICMLC), 2022. Cites: paper

Transfer learning for non-image data in clinical research: A scoping review
Andreas Ladefoged Ebbehøj, Mette Østergaard Thunbo, Ole Emil Andersen et al. PLOS Digital Health, 2022. Cites: paper

CineECG provides a novel anatomical view on the normal atrial P-wave
Emanuela H. Locati, Carlo Pappone, Francesca Heilbron et al. European Heart Journal - Digital Health, 2022. Cites: paper

Study of the Few-Shot Learning for ECG Classification Based on the PTB-XL Dataset
Krzysztof Pałczyński, Sandra Śmigiel, Damian Ledziński et al. Sensors, 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

A Bi-atrial Statistical Shape Model as a Basis to Classify Left Atrial Enlargement from Simulated and Clinical 12-Lead ECGs
Claudia Nagel, Matthias Schaufelberger, Olaf Dössel et al. Lecture notes in computer science, 2022. Cites: paper

An EMD-Based Approach for Atrial Fibrillation Classification Using Wavelets and Convolutional Neural Network
Hassan Serhal, Nassib Abdallah, Jean-Marie Marion et al. Biomedical Signal Processing and Control, 2022. Cites: paper

Artificial Intelligence for Detection of Cardiovascular-Related Diseases from Wearable Devices: A Systematic Review and Meta-Analysis
Solam Lee, Yuseong Chu, Jiseung Ryu et al. Yonsei Medical Journal, 2022. Cites: paper

Assessing the Reidentification Risks Posed by Deep Learning Algorithms Applied to ECG Data
Arin Ghazarian, Jianwei Zheng, Daniele Carlo Struppa et al. IEEE Access, 2022. Cites: paper

BiDLNet: An Integrated Deep Learning Model for ECG-based Heart Disease Diagnosis
S. Kusuma, K R Jothi. International Journal of Advanced Computer Science and Applications, 2022. Cites: paper

Classificação do infarto de miocárdio a partir da extração de parâmetros de espaços e fase com o uso dos Descritores de Haralick
Vinícius Jefferson Dias Vieira, Emanuel thiago, Dimas Merêncio et al. Anais de XXXV Simpósio Brasileiro de Telecomunicações e Processamento de Sinais, 2022. Cites: paper

Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research
Zhibo Zhang, Hussam Al Hamadi, Ernesto Damiani et al. IEEE Access, 2022. Cites: paper

Explainable Electrocardiogram Analysis with Wave Decomposition: Application to Myocardial Infarction Detection
Yingyu Yang, Marie Rocher, Pamela Moceri et al. Lecture notes in computer science, 2022. Cites: paper

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