A heart failure classification model from radial artery pulse wave using LSTM neural networks
Our findings strongly suggest that an LSTM-based model analyzing radial artery pulse waves can effectively differentiate between healthy, CAD, and HF states. This approach represents a simple, non-invasive, and cost-effective methodology with significant potential as a valuable strategy to aid in the early screening and detection of HF. Further investigation and broader clinical validation are warranted to confirm the robustness and real-world applicability of this promising tool.
Authors
- Jing Hong (ORCID: https://orcid.org/0000-0003-3364-1173)
- Haixia Yan (ORCID: https://orcid.org/0000-0002-1963-3292)
- Yiqin Wang (ORCID: https://orcid.org/0000-0002-0280-7495)
- Jin Xu (ORCID: https://orcid.org/0000-0002-6021-5551)
- Haimei Wu
- Yi Lyu
- Wen-Yue Huang
Institutions
- Shanghai Medical Information Center (CN)
- Shanghai University of Traditional Chinese Medicine (CN)
- Community Health Center (US)
- Shuguang Hospital (CN)
Publication Details
- Journal
- BMC Medical Informatics and Decision Making
- Published
- 2025-08-27
- DOI
- https://doi.org/10.1186/s12911-025-03167-5
- Citations
- 3
- Primary Topic
- ECG Monitoring and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 4.16
Funders
- National Natural Science Foundation of China
- Shanghai University of Traditional Chinese Medicine