Cited by

Waveform Database Software Package (WFDB) for Python (v4.1.0)

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25 publications cite any version of this project, newest first.

• Physiology-augmented deep representations improve cross-database electrocardiogram beat classification under unseen-domain shift
  Mujahid Shahid¹, Oliver Konyo. Research Square, 2026.
• Multiscale Statistical Process Control of RR Interval Dynamics for Explainable Pre-Onset Characterization of Paroxysmal Atrial Fibrillation
  Michel Alexander Castañeda Alarcón, Javier Díaz-Carmona, Horacio Rostro‐González et al. World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany, 2026.
• Vitabel: A Python Framework for Visualizing and Labelling High-Resolution Physiological Data for Critical Care Machine Learning
  Simon Orlob, Wolfgang J. Kern, Benjamin Hackl et al. Journal of Medical Systems, 2026.
• The Harvard-Emory ECG Database
  Zuzana Koscova, Qiao Li, Chad J. Robichaux et al. Scientific Data, 2026.
• A Deep Lightweight Convolutional Neural Network for Detecting Artifacts in Continuous EEG Signals
  Evans Nyanney, Parthasarathy D. Thirumala, Shyam Visweswaran et al. medRxiv, 2025.
• Cardiac pathology classification with one-dimensional convolutional neural network
  Thomas Sponchiado Pastore, Gabriel de Oliveira Ramos, Jean Schmith. 2025.
• LaborEase: An Artificial Intelligence-Based Wearable Electrohysterography Device for Preterm Pregnancy Detection
  Muhammad Omar Cheema, Zia Mohy Ud Din, Abdullah Al Aishan et al. IEEE Access, 2025.
• MIMIC-BP: A curated dataset for blood pressure estimation
  Ivandro Sanches, Victor V. Gomes, Carlos Caetano et al. Scientific Data, 2024.
• The Harvard-Emory ECG Database
  Zuzana Koscova, Qiao Li, Chad J. Robichaux et al. medRxiv, 2024.
• Explainable AI-driven machine learning for heart disease detection using ECG signal
  Babita Majhi, Aarti Kashyap. Applied Soft Computing, 2024.
• A novel ECG compression algorithm using Pulse-Width Modulation integrated quantization for low-power real-time monitoring
  Isuri Devindi, Sashini Liyanage, Titus Jayarathna et al. Scientific Reports, 2024.
• Versatile Machine Learning-Based Authentications by Using Enhanced Time-Sliced Electrocardiograms
  Yi Zhao, Song-Kyoo Kim. Information, 2024.
• Improved QT ınterval estimation using conditional generative adversarial networks
  Awad Al‐Zaben, Mohammad A. Al-Abed. Neural Computing and Applications, 2024.
• Pyheartlib: A Python package for processing electrocardiogram signals
  Sadegh Mohammadi. The Journal of Open Source Software, 2024.
• Analysis of electrocardiographic signals to assess heart rate variability under surgical context: a methodological proposal
  Muñoz-Ruiz Sofia, Saavedra-Ordoñez Juandiego, Valencia-Fajardo Natalia et al. 2024.
• EFC: An open-source Python library for electrocardiography format conversion
  Sebastian Schmale, Theresa Bender, Nicolai Spicher. 2024.
• A Low-Complexity R-peak Detection Based on Exponential Weight Mean-Variance for Wearable ECG Devices
  Duong Hoang Tien, Ma Pham Nhut Tan, Tran Hoang Nam et al. International Conference on Advanced Technologies for Communications, 2023.
• Signal Quality Indices based on Skewness of Peak-Peak Interval for Wearable ECG Devices
  Ta Viet Tai, Ma Pham Nhut Tan, Duong Hoang Tien et al. Signal Processing: Algorithms, Architectures, Arrangements, and Applications, 2023.
• Completing the Cabrera Circle: deriving adaptable leads from ECG limb leads by combining constraints with a correction factor
  Henning Dathe, Dagmar Krefting, Nicolai Spicher. Physiological Measurement, 2023.
• Cardiac Disease Diagnosis on Imbalanced Electrocardiography Data Through Optimal Transport Augmentation
  Jielin Qiu, Jiacheng Zhu, Mengdi Xu et al. IEEE International Conference on Acoustics Speech and Signal Processing, 2023.
• Invertible Kernel PCA With Random Fourier Features
  Daniel Gedon, Antônio H. Ribeiro, Niklas Wahlström et al. IEEE Signal Processing Letters, 2023.
• Classifying sepsis from photoplethysmography
  Sara Lombardi, Petri Partanen, Piergiorgio Francia et al. Health Information Science and Systems, 2022.
• A novel deep learning package for electrocardiography research
  Hao Wen, Jingsu Kang. Physiological Measurement, 2022.
• A study on several critical problems on arrhythmia detection using varying-dimensional electrocardiography
  Jingsu Kang, Hao Wen. Physiological Measurement, 2022.
• Hybrid Arrhythmia Detection on Varying-Dimensional Electrocardiography: Combining Deep Neural Networks and Clinical Rules
  Hao Wen, Jingsu Kang. Computing in cardiology, 2021.

Citation data from OpenAlex, updated weekly. Papers that use the project without citing it may be missing.