Name
Abstract | ||
|---|---|---|
In this work, we analyse different temporal feature extraction window approaches, in combination with short-time heat and electric pain stimuli. Thereby, we focus on the physiological signals of the Experimentally Induced Thermal and Electrical (X-ITE) Pain Database. Each of our proposed approaches is evaluated based on the leave-one-subject-out cross-validation using the random forest method. Moreover, the effectiveness of each physiological signal is inspected separately, as well as by applying the feature fusion approach. Thereby, we analyse different binary classification tasks, as well as four-class classification tasks. Our outcomes indicate that a shifted temporal feature extraction window increases the classification performance significantly, when pain is induced by thermal stimuli. Moreover, our evaluations point out that the outcomes differ significantly, when participants are exposed to electrical pain stimuli. For short-term electric pain stimuli, the best results are obtained without temporal shifts of the feature extraction windows. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-58309-5_11 | ANNPR |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tobias Ricken | 1 | 0 | 0.34 |
| Adrian Steinert | 2 | 0 | 0.34 |
| Peter Bellmann | 3 | 0 | 2.03 |
| Steffen Walter | 4 | 127 | 13.34 |
| Friedhelm Schwenker | 5 | 1160 | 96.59 |