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...

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Autores principales:	Atsushi Kimura, Yasue Mitsukura, Akihito Oya, Morio Matsumoto, Masaya Nakamura, Arihiko Kanaji, Takeshi Miyamoto
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/0480e81d5b184829b473cabdaf409660
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spelling	oai:doaj.org-article:0480e81d5b184829b473cabdaf4096602021-12-02T14:06:25ZObjective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning10.1038/s41598-021-82696-12045-2322https://doaj.org/article/0480e81d5b184829b473cabdaf4096602021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82696-1https://doaj.org/toc/2045-2322Abstract 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.Atsushi KimuraYasue MitsukuraAkihito OyaMorio MatsumotoMasaya NakamuraArihiko KanajiTakeshi MiyamotoNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q 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
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.
format	article
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
title	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_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_sort	objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/0480e81d5b184829b473cabdaf409660
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Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning

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