Abstract
Pain is a highly unpleasant sensory experience, for which currently no objective diagnostic test exists to measure it. Identification and localisation of pain, where the subject is unable to communicate, is a key step in enhancing therapeutic outcomes. Numerous studies have been conducted to categorise pain, but no reliable conclusion has been achieved. This is the first study that aims to show a strict relation between Electrodermal Activity (EDA) signal features and the presence of pain and to clarify the relation of classified signals to the location of the pain. For that purpose, EDA signals were recorded from 28 healthy subjects by inducing electrical pain at two anatomical locations (hand and forearm) of each subject. The EDA data were preprocessed with a Discrete Wavelet Transform to remove any irrelevant information. Chi-square feature selection was used to select features extracted from three domains: time, frequency, and cepstrum. The final feature vector was fed to a pool of classification schemes where an Artificial Neural Network classifier performed best. The proposed method, evaluated through leave-one-subject-out cross-validation, provided 90% accuracy in pain detection (no pain vs. pain), whereas the pain localisation experiment (hand pain vs. forearm pain) achieved 66.67% accuracy.Clinical relevance- This is the first study to provide an analysis of EDA signals in finding the source of the pain. This research explores the viability of using EDA for pain localisation, which may be helpful in the treatment of noncommunicable patients.
Original language | English |
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Title of host publication | Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
Editors | Georgios Mitsis, Lei Ding, Marius George Linguraru, Jim Xiuquan Ji, Esmaiel Jabbari, Socrates Dokos, Jie Liang , Thomas Heldt, Rebecca Mieloszyk, David Guiraud, Shanbao Tong, Ivan Lee, Benny Lo, Arturo Forner-Cordero, Dorin Panescu, Dieter Haemmerich, Mark van Gils, Omer Inan, Hans van Oostrom, Ian Goon |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 1-5 |
Number of pages | 5 |
Volume | 2023 |
ISBN (Electronic) | 9798350324471 |
ISBN (Print) | 9798350324488 |
DOIs | |
Publication status | Published - 11 Dec 2023 |
Event | 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) - AustraliA, Sydney, Australia Duration: 24 Jul 2023 → 27 Jul 2023 http://10.1109/EMBC40787.2023 |
Publication series
Name | Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference |
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Conference
Conference | 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) |
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Abbreviated title | EMBC 2023 |
Country/Territory | Australia |
City | Sydney |
Period | 24/07/23 → 27/07/23 |
Internet address |