Empirical investigation of multi-source cross-validation in clinical ECG classification
Open Access
- 19 October 2024
- journal article
- Published by Elsevier in Computers in Biology and Medicine
- Vol. 183, 109271
Abstract
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Funding Information
- China Scholarship Council
- Research Council of Finland (358868, 352893, 345805, 340140)
This publication has 28 references indexed in Scilit:
- Leakage and the reproducibility crisis in machine-learning-based sciencePatterns, 2023
- Data Augmentation for 12-Lead ECG Beat ClassificationSN Computer Science, 2021
- Addressing bias in big data and AI for health care: A call for open sciencePatterns, 2021
- A comparative study and analysis of LSTM deep neural networks for heartbeats classificationHealth and Technology, 2021
- Data Science2020
- Voxel-Wise Logistic Regression and Leave-One-Source-Out Cross Validation for white matter hyperintensity segmentationMagnetic Resonance Imaging, 2018
- ECG Signal Classification Using Various Machine Learning TechniquesJournal of Medical Systems, 2018
- Performance evaluation of classification algorithms by k-fold and leave-one-out cross validationPattern Recognition, 2015
- Avoiding non-independence in fMRI data analysis: Leave one subject outNeuroImage, 2010
- External validation is necessary in prediction research:Journal of Clinical Epidemiology, 2003