A novel neural network method using radial basis function for effective assessment of stiffness index on lumbar disc degenerative subjects
ABSTRACTLumbar disc degenerative disc disease with back pain and its severity is a leading health issue in society and MRI is the best modality to detect the severity and degree of disc degeneration. The most critical component of degenerative disc disease deals with triggering rapid action for real...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2023-10-01
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| Series: | Automatika |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/00051144.2023.2223496 |
| _version_ | 1797245082425884672 |
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| author | C. K. Sreeja V. N. Meena Devi M. K. Aneesh |
| author_facet | C. K. Sreeja V. N. Meena Devi M. K. Aneesh |
| author_sort | C. K. Sreeja |
| collection | DOAJ |
| description | ABSTRACTLumbar disc degenerative disc disease with back pain and its severity is a leading health issue in society and MRI is the best modality to detect the severity and degree of disc degeneration. The most critical component of degenerative disc disease deals with triggering rapid action for real-time-based system identification. The input is obtained from the non-invasive device called finger pulse plethysmography to assess the stiffness and its correlation with body composition in lumbar disc degeneration. The recent methodology contributions aim at predicting the stiffness which uses pulse wave velocity and reflection on signal features. As the signals are very sensitive to differences between high and low ranges, finger pulse plethysmography effectively detects irregularities at early stages. Based on the severity of degeneration, shown by the MRI report, subjects were grouped into the disc bulging group (DBG) and the nerve compression group (NCG). The supervised features help in training the signals to correct the limitations of prediction. Finally, the Radial Basis Function neural network approach helps in diminishing the local minimal values in the signal. It helps in the effective categorization of anomalous and ordinary stiffness index measurements for lumbar disc degeneration. |
| first_indexed | 2024-03-10T15:57:14Z |
| format | Article |
| id | doaj.art-d51f6c70fcac47d586e3f46aa4102461 |
| institution | Directory Open Access Journal |
| issn | 0005-1144 1848-3380 |
| language | English |
| last_indexed | 2024-04-24T19:21:15Z |
| publishDate | 2023-10-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Automatika |
| spelling | doaj.art-d51f6c70fcac47d586e3f46aa41024612024-03-25T18:18:03ZengTaylor & Francis GroupAutomatika0005-11441848-33802023-10-0164496497010.1080/00051144.2023.2223496A novel neural network method using radial basis function for effective assessment of stiffness index on lumbar disc degenerative subjectsC. K. Sreeja0V. N. Meena Devi1M. K. Aneesh2Department of Physics, Noorul Islam Center of Higher Education, Kumaracoil, Tamil Nadu, IndiaDepartment of Physics, Noorul Islam Center of Higher Education, Kumaracoil, Tamil Nadu, IndiaDepartment of Radiology, Jubilee Mission Medical College & Research Institute, Thrissur, Kerala, IndiaABSTRACTLumbar disc degenerative disc disease with back pain and its severity is a leading health issue in society and MRI is the best modality to detect the severity and degree of disc degeneration. The most critical component of degenerative disc disease deals with triggering rapid action for real-time-based system identification. The input is obtained from the non-invasive device called finger pulse plethysmography to assess the stiffness and its correlation with body composition in lumbar disc degeneration. The recent methodology contributions aim at predicting the stiffness which uses pulse wave velocity and reflection on signal features. As the signals are very sensitive to differences between high and low ranges, finger pulse plethysmography effectively detects irregularities at early stages. Based on the severity of degeneration, shown by the MRI report, subjects were grouped into the disc bulging group (DBG) and the nerve compression group (NCG). The supervised features help in training the signals to correct the limitations of prediction. Finally, the Radial Basis Function neural network approach helps in diminishing the local minimal values in the signal. It helps in the effective categorization of anomalous and ordinary stiffness index measurements for lumbar disc degeneration.https://www.tandfonline.com/doi/10.1080/00051144.2023.2223496Back paindisc degenerationstiffness indexfinger pulse plethysmographyradial basis function neural network |
| spellingShingle | C. K. Sreeja V. N. Meena Devi M. K. Aneesh A novel neural network method using radial basis function for effective assessment of stiffness index on lumbar disc degenerative subjects Automatika Back pain disc degeneration stiffness index finger pulse plethysmography radial basis function neural network |
| title | A novel neural network method using radial basis function for effective assessment of stiffness index on lumbar disc degenerative subjects |
| title_full | A novel neural network method using radial basis function for effective assessment of stiffness index on lumbar disc degenerative subjects |
| title_fullStr | A novel neural network method using radial basis function for effective assessment of stiffness index on lumbar disc degenerative subjects |
| title_full_unstemmed | A novel neural network method using radial basis function for effective assessment of stiffness index on lumbar disc degenerative subjects |
| title_short | A novel neural network method using radial basis function for effective assessment of stiffness index on lumbar disc degenerative subjects |
| title_sort | novel neural network method using radial basis function for effective assessment of stiffness index on lumbar disc degenerative subjects |
| topic | Back pain disc degeneration stiffness index finger pulse plethysmography radial basis function neural network |
| url | https://www.tandfonline.com/doi/10.1080/00051144.2023.2223496 |
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