A refined SMOTE-ENN optimization method based on machine learning for heart rate variability data classification
The refined SMOTE-ENN optimization method enhances the detection performance of the machine learning algorithms for classifying imbalanced HRV data, providing valuable technical support for the early detection of depression.
Authors
- Biao Zhang (ORCID: https://orcid.org/0000-0002-9738-1123)
- Xufeng Lang (ORCID: https://orcid.org/0000-0002-4767-9482)
- YiHua SONG
- Run Gao (ORCID: https://orcid.org/0000-0002-0063-1975)
- Meng Han (ORCID: https://orcid.org/0000-0003-1005-3264)
- Binbin Ji
- Yuanlun Zhou
- Hongyan Li
- Xuebin Qiao
- Muzi Liang
- Zuojian Zhou
Institutions
- Nanjing University of Chinese Medicine (CN)
- Nanjing Brain Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2026-02-12
- DOI
- https://doi.org/10.3389/fdgth.2026.1749570
- Primary Topic
- Heart Rate Variability and Autonomic Control
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Government of Jiangsu Province