An AI recognition method for children's clinical operative pain by skin potential (SP) signal
We developed a comprehensive SP recognition model, innovatively employing WT for SP signal analysis. This time-frequency analysis method, by preserving low-frequency features, is particularly suitable for SP signals. By combining pain monitoring with SP signals and ML, subjective pain experiences are transformed into quantifiable data, achieving high accuracy and real-time measurement capabilities. These advantages provide valuable technical support for clinical pediatric pain management.
Authors
- Jikui Luo (ORCID: https://orcid.org/0000-0003-0310-2443)
- Bin Wu (ORCID: https://orcid.org/0000-0003-2755-6706)
- Cangcang Fu
- Shaohua Hu (ORCID: https://orcid.org/0000-0003-0570-670X)
- Mingxuan Huang (ORCID: https://orcid.org/0009-0008-2383-9446)
- Jihua Zhu (ORCID: https://orcid.org/0000-0001-8964-896X)
- Yubo Li (ORCID: https://orcid.org/0000-0002-9135-8360)
- Yonggang Chen (ORCID: https://orcid.org/0000-0002-0970-6363)
- Xiaozhi Wang (ORCID: https://orcid.org/0000-0002-5727-143X)
- Jiadong He (ORCID: https://orcid.org/0000-0001-8845-0556)
- Linbo Chui
Institutions
- Children's Hospital of Zhejiang University (CN)
- Applied Pulsed Power (United States) (US)
- First Affiliated Hospital Zhejiang University (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Heliyon
- Published
- 2024-12-27
- DOI
- https://doi.org/10.1016/j.heliyon.2024.e41558
- Citations
- 2
- Primary Topic
- Pediatric Pain Management Techniques
- Type
- article
- Field-Weighted Citation Impact
- 2.22