Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning
Abstract Pain is an undesirable sensory experience that can induce depression and limit individuals’ activities of daily living, in turn negatively impacting the labor force. Affected people frequently feel pain during activity; however, pain is subjective and difficult to judge objectively, particu...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2021-02-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-82696-1 |
| _version_ | 1818571144948613120 |
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| author | Atsushi Kimura Yasue Mitsukura Akihito Oya Morio Matsumoto Masaya Nakamura Arihiko Kanaji Takeshi Miyamoto |
| author_facet | Atsushi Kimura Yasue Mitsukura Akihito Oya Morio Matsumoto Masaya Nakamura Arihiko Kanaji Takeshi Miyamoto |
| author_sort | Atsushi Kimura |
| collection | DOAJ |
| description | Abstract Pain is an undesirable sensory experience that can induce depression and limit individuals’ activities of daily living, in turn negatively impacting the labor force. Affected people frequently feel pain during activity; however, pain is subjective and difficult to judge objectively, particularly during activity. Here, we developed a system to objectively judge pain levels in walking subjects by recording their quantitative electroencephalography (qEEG) and analyzing data by machine learning. To do so, we enrolled 23 patients who had undergone total hip replacement for pain, and recorded their qEEG during a five-minute walk via a wearable device with a single electrode placed over the Fp1 region, based on the 10–20 Electrode Placement System, before and three months after surgery. We also assessed subject hip pain using a numerical rating scale. Brain wave amplitude differed significantly among subjects with different levels of hip pain at frequencies ranging from 1 to 35 Hz. qEEG data were also analyzed by a support vector machine using the Radial Basis Functional Kernel, a function used in machine learning. That approach showed that an individual’s hip pain during walking can be recognized and subdivided into pain quartiles with 79.6% recognition Accuracy. Overall, we have devised an objective and non-invasive tool to monitor an individual’s pain during walking. |
| first_indexed | 2024-12-14T13:51:50Z |
| format | Article |
| id | doaj.art-0480e81d5b184829b473cabdaf409660 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T13:51:50Z |
| publishDate | 2021-02-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-0480e81d5b184829b473cabdaf4096602022-12-21T22:59:03ZengNature PortfolioScientific Reports2045-23222021-02-0111111010.1038/s41598-021-82696-1Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learningAtsushi Kimura0Yasue Mitsukura1Akihito Oya2Morio Matsumoto3Masaya Nakamura4Arihiko Kanaji5Takeshi Miyamoto6Department of Orthopedic Surgery, Keio University School of MedicineDepartment of Technology and Engineering, Keio UniversityDepartment of Orthopedic Surgery, Keio University School of MedicineDepartment of Orthopedic Surgery, Keio University School of MedicineDepartment of Orthopedic Surgery, Keio University School of MedicineDepartment of Orthopedic Surgery, Keio University School of MedicineDepartment of Orthopedic Surgery, Keio University School of MedicineAbstract Pain is an undesirable sensory experience that can induce depression and limit individuals’ activities of daily living, in turn negatively impacting the labor force. Affected people frequently feel pain during activity; however, pain is subjective and difficult to judge objectively, particularly during activity. Here, we developed a system to objectively judge pain levels in walking subjects by recording their quantitative electroencephalography (qEEG) and analyzing data by machine learning. To do so, we enrolled 23 patients who had undergone total hip replacement for pain, and recorded their qEEG during a five-minute walk via a wearable device with a single electrode placed over the Fp1 region, based on the 10–20 Electrode Placement System, before and three months after surgery. We also assessed subject hip pain using a numerical rating scale. Brain wave amplitude differed significantly among subjects with different levels of hip pain at frequencies ranging from 1 to 35 Hz. qEEG data were also analyzed by a support vector machine using the Radial Basis Functional Kernel, a function used in machine learning. That approach showed that an individual’s hip pain during walking can be recognized and subdivided into pain quartiles with 79.6% recognition Accuracy. Overall, we have devised an objective and non-invasive tool to monitor an individual’s pain during walking.https://doi.org/10.1038/s41598-021-82696-1 |
| spellingShingle | Atsushi Kimura Yasue Mitsukura Akihito Oya Morio Matsumoto Masaya Nakamura Arihiko Kanaji Takeshi Miyamoto Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning Scientific Reports |
| title | Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning |
| title_full | Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning |
| title_fullStr | Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning |
| title_full_unstemmed | Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning |
| title_short | Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning |
| title_sort | objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning |
| url | https://doi.org/10.1038/s41598-021-82696-1 |
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