Objective characterization of hip pain levels during walking by combining quantitative electroencephalography with machine learning
Autor: | Arihiko Kanaji, Yasue Mitsukura, Morio Matsumoto, Masaya Nakamura, Atsushi Kimura, Takeshi Miyamoto, Akihito Oya |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
Adult
Male Activities of daily living Science Arthroplasty Replacement Hip Sensory system Diseases Walking Machine learning computer.software_genre Article Machine Learning 03 medical and health sciences Wearable Electronic Devices 0302 clinical medicine Rating scale Medicine Humans Hip pain Electrode placement Depression (differential diagnoses) Aged Pain Measurement 030203 arthritis & rheumatology Aged 80 and over Multidisciplinary business.industry Biological techniques Brain Electroencephalography Middle Aged Quantitative electroencephalography Arthralgia Brain Waves Quartile Female Artificial intelligence business computer 030217 neurology & neurosurgery |
Zdroj: | Scientific Reports Scientific Reports, Vol 11, Iss 1, Pp 1-10 (2021) |
ISSN: | 2045-2322 |
Popis: | 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. |
Databáze: | OpenAIRE |
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