Name
Abstract | ||
|---|---|---|
Patient self-reporting of pain is not always possible, in those cases automated objective pain assessment could lead to reliable pain assessment. In this context, physiological measurements have been studied and one of the promising signals is skin conductance (SC). In this study, 1Hz SC signal acquisition is performed while gradually increasing heat and electrical pain stimuli are induced. Three labeled study periods are defined based on pain stimuli presence, self-reported pain threshold and pain tolerance. Different classification and regression models are compared, together with selected SC features. The model performances are evaluated using c-index. Results show good predictability, especially for the slow tonic component decomposed from the SC signal. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/EMBC.2019.8857776 | 2019 41ST ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC) |
Field | DocType | Volume |
Computer vision,Threshold of pain,Tonic (music),Signal acquisition,Computer science,Pain assessment,Pain tolerance,Artificial intelligence,Audiology,Stimulus (physiology),Skin conductance | Conference | 2019 |
ISSN | Citations | PageRank |
1557-170X | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elise Syrjälä | 1 | 0 | 0.68 |
| Mingzhe Jiang | 2 | 51 | 3.05 |
| Tapio Pahikkala | 3 | 1005 | 70.68 |
| Sanna Salanterä | 4 | 151 | 21.92 |
| Pasi Liljeberg | 5 | 1147 | 92.79 |