Continuous Short-Term Pain Assessment in Temporomandibular Joint Therapy Using LSTM Models Supported by Heat-Induced Pain Data Patterns
This study aims to design a time-continuous pain level assessment system for temporomandibular joint therapy. Our objectives cover verifying literature suggestions on pain stimulus, protocols for collecting reference data, and continuous pain recognition models. We use two types of pain data acquire...
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| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
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| Online Access: | https://ieeexplore.ieee.org/document/10680582/ |
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| author | Aleksandra Badura Maria Bienkowska Andrzej Mysliwiec Ewa Pietka |
| author_facet | Aleksandra Badura Maria Bienkowska Andrzej Mysliwiec Ewa Pietka |
| author_sort | Aleksandra Badura |
| collection | DOAJ |
| description | This study aims to design a time-continuous pain level assessment system for temporomandibular joint therapy. Our objectives cover verifying literature suggestions on pain stimulus, protocols for collecting reference data, and continuous pain recognition models. We use two types of pain data acquired during 1) heat stimulation and 2) temporomandibular joint therapy. Thirty-six electrodermal activity (EDA) features are determined to build a binary classification model. The experimental dataset is used to train the initial model that produces pseudo-labels for weakly-labeled clinical data. In training the final long short-term memory (LSTM) model, we propose a novel multivariate loss involving, i.a., dynamometer data. Significant differences are found between EDA features extracted from experimental and clinical datasets in pain and no pain events. The classification model is validated at different stages of the model development. The final model classifies each four-second frame with a mean accuracy of 0.89 and an F1 score of 0.85. Our study introduces the dynamometer as a novel source of pain-feeling indications that meets the challenges given in the literature: data can be acquired in various procedures and from patients with limited abilities. The main contribution of the study is to design the first time-continuous and short-term pain assessment system for a clinical setting. |
| format | Article |
| id | doaj-art-0f75606c65ec44ecafa690c1cbfc64cc |
| institution | DOAJ |
| issn | 1534-4320 1558-0210 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj-art-0f75606c65ec44ecafa690c1cbfc64cc2025-08-20T03:07:37ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1534-43201558-02102024-01-01323565357610.1109/TNSRE.2024.346158910680582Continuous Short-Term Pain Assessment in Temporomandibular Joint Therapy Using LSTM Models Supported by Heat-Induced Pain Data PatternsAleksandra Badura0https://orcid.org/0000-0001-7109-572XMaria Bienkowska1https://orcid.org/0000-0002-0226-4902Andrzej Mysliwiec2https://orcid.org/0000-0003-2183-1156Ewa Pietka3Faculty of Biomedical Engineering, Silesian University of Technology, Zabrze, PolandFaculty of Biomedical Engineering, Silesian University of Technology, Zabrze, PolandInstitute of Physiotherapy and Health Science, Academy of Physical Education in Katowice, Katowice, PolandFaculty of Biomedical Engineering, Silesian University of Technology, Zabrze, PolandThis study aims to design a time-continuous pain level assessment system for temporomandibular joint therapy. Our objectives cover verifying literature suggestions on pain stimulus, protocols for collecting reference data, and continuous pain recognition models. We use two types of pain data acquired during 1) heat stimulation and 2) temporomandibular joint therapy. Thirty-six electrodermal activity (EDA) features are determined to build a binary classification model. The experimental dataset is used to train the initial model that produces pseudo-labels for weakly-labeled clinical data. In training the final long short-term memory (LSTM) model, we propose a novel multivariate loss involving, i.a., dynamometer data. Significant differences are found between EDA features extracted from experimental and clinical datasets in pain and no pain events. The classification model is validated at different stages of the model development. The final model classifies each four-second frame with a mean accuracy of 0.89 and an F1 score of 0.85. Our study introduces the dynamometer as a novel source of pain-feeling indications that meets the challenges given in the literature: data can be acquired in various procedures and from patients with limited abilities. The main contribution of the study is to design the first time-continuous and short-term pain assessment system for a clinical setting.https://ieeexplore.ieee.org/document/10680582/Automated pain assessmentelectrodermal activity (EDA)pattern recognitionknowledge transferphysiotherapy |
| spellingShingle | Aleksandra Badura Maria Bienkowska Andrzej Mysliwiec Ewa Pietka Continuous Short-Term Pain Assessment in Temporomandibular Joint Therapy Using LSTM Models Supported by Heat-Induced Pain Data Patterns IEEE Transactions on Neural Systems and Rehabilitation Engineering Automated pain assessment electrodermal activity (EDA) pattern recognition knowledge transfer physiotherapy |
| title | Continuous Short-Term Pain Assessment in Temporomandibular Joint Therapy Using LSTM Models Supported by Heat-Induced Pain Data Patterns |
| title_full | Continuous Short-Term Pain Assessment in Temporomandibular Joint Therapy Using LSTM Models Supported by Heat-Induced Pain Data Patterns |
| title_fullStr | Continuous Short-Term Pain Assessment in Temporomandibular Joint Therapy Using LSTM Models Supported by Heat-Induced Pain Data Patterns |
| title_full_unstemmed | Continuous Short-Term Pain Assessment in Temporomandibular Joint Therapy Using LSTM Models Supported by Heat-Induced Pain Data Patterns |
| title_short | Continuous Short-Term Pain Assessment in Temporomandibular Joint Therapy Using LSTM Models Supported by Heat-Induced Pain Data Patterns |
| title_sort | continuous short term pain assessment in temporomandibular joint therapy using lstm models supported by heat induced pain data patterns |
| topic | Automated pain assessment electrodermal activity (EDA) pattern recognition knowledge transfer physiotherapy |
| url | https://ieeexplore.ieee.org/document/10680582/ |
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