Name
Title | ||
|---|---|---|
Automatic pain assessment on cancer patients using physiological signals recorded in real-world contexts. |
Abstract | ||
|---|---|---|
Pain assessment represents the first fundamental stage for proper pain management, but currently, methods applied in clinical practice often lack in providing a satisfying characterization of the pain experience. Automatic methods based on the analysis of physiological signals (e.g., photoplethysmography, electrodermal activity) promise to overcome these limitations, also providing the possibility to record these signals through wearable devices, thus capturing the physiological response in everyday life. After applying preprocessing, feature extraction and feature selection methods, we tested several machine learning algorithms to develop an automatic classifier fed with physiological signals recorded in real-world contexts and pain ratings from 21 cancer patients. The best algorithm achieved up to 72% accuracy. Although performance can be improved by enlarging the dataset, preliminary results proved the feasibility of assessing pain by using physiological signals recorded in real-world contexts. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/EMBC48229.2022.9871990 | Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
DocType | Volume | ISSN |
Conference | 2022 | 2694-0604 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Serena Moscato | 1 | 0 | 0.34 |
| Silvia Orlandi | 2 | 12 | 4.73 |
| Andrea Giannelli | 3 | 0 | 0.34 |
| Rita Ostan | 4 | 0 | 0.34 |
| Lorenzo Chiari | 5 | 97 | 18.65 |