volume | PIER Journals

Abstract

Globally, microwave frequencies are being extensively employed in numerous biomedical implementations due to its high resolution, reasonable penetration through the human tissue, and cost-effectiveness. However, the quantization of human osseous tissue through microwave sensing is still not proficient. Therefore, this article provides an insight on the prediction of onset and progression of osteoporosis developed through the use of a microwave setup for the contactless evaluation of osteoporosis. This microwave setup comprises a human wrist model as a device under test which is illuminated through a pair of planar stubbed monopole antennas to characterize the different degrees of osteoporosis through frequency domain simulation analysis. By diversifying the wrist dimensions, we are collecting the dataset of the transfer characteristics. Furthermore, different machine learning algorithms are employed on this dataset to train, classify and eventually evaluate the different degrees of osteoporosis. Finally, an optimum machine learning algorithm was obtained to work at an optimum bandwidth and optimum frequency.

1. Lin, J. T. and J. M. Lane, "Osteoporosis: A review," Clin. Orthopaedics Relat. Res., Vol. 425, 126-134, 2004.
doi:10.1097/01.blo.0000132404.30139.f2

2. Kerketta, S. R. and D. Ghosh, "Bandwidth enhancement of monopole antenna using stubbed ground plane," Wiley --- Int. J. RF Microw. Comput. Aided Eng., Vol. 29, No. 10, e21868, 2019.

3. Kerketta, S. R. and D. Ghosh, "Microwave sensing for human bone health evaluation," Elsevier AEU --- International Journal of Electronics and Communications, Vol. 127, 153469, 2020.
doi:10.1016/j.aeue.2020.153469

4. Kerketta, S. R. and D. Ghosh, "Microwave analysis on bone mineral density," 2020 International Symposium on Antennas & Propagation (APSYM), 83-86, Cochin, India, 2020.

5. Roohi, M., J. Mazloum, M. A. Pourmina, and B. Ghalamkari, "Machine learning approaches for automated stroke detection, segmentation, and classification in microwave brain imaging systems," Progress In Electromagnetics Research C, Vol. 116, 193-205, 2021.
doi:10.2528/PIERC21080404

6. Bamatraf, S. M., M. A. Aldhaeebi, and O. M. Ramahi, "Noninvasive continuous glucose monitoring on aqueous solutions using microwave sensor with machine learning," Progress In Electromagnetics Research Letters, Vol. 102, 127-134, 2022.
doi:10.2528/PIERL21110905

7. Santorelli, A., E. Porter, E. Kirshin, Y. J. Liu, and M. Popovic, "Investigation of classifiers for tumor detection with an experimental time-domain breast screening system," Progress In Electromagnetics Research, Vol. 144, 45-57, 2014.
doi:10.2528/PIER13110709

8. Kanis, J. A., O. Johnell, A. Oden, H. Johansson, and E. McCloskey, "FRAX^TM and the assessment of fracture probability in men and women from the UK," Osteoporosis International, Vol. 19, No. 4, 385-397, 2008.
doi:10.1007/s00198-007-0543-5

9. Rubin, K. H., T. Friis-Holmberg, A. P. Hermann, B. Abrahamsen, and K. Brixen, "Risk assessment tools to identify women with increased risk of osteoporotic fracture: Complexity or simplicity?," A Systematic Review. Journal of Bone and Mineral Research, Vol. 28, No. 8, 1701-1717, 2013.
doi:10.1002/jbmr.1956

10. Koh, L. K., W. B. Sedrine, T. P. Torralba, A. Kung, S. Fujiwara, S. P. Chan, Q. R. Huang, R. Rajatanavin, K. S. Tsai, H. M. Park, and J. Y. Reginster, "A simple tool to identify Asian women at increased risk of osteoporosis," Osteoporos Int., Vol. 12, 699-705, 2001.
doi:10.1007/s001980170070

11. Richy, F., M. Gourlay, P. D. Ross, S. S. Sen, L. Radican, F. D. Ceulaer, W. B. Sedrine, O. Ethgen, O. Bruyere, and J. Y. Reginst, "Validation and comparative evaluation of the Osteoporosis Self- assessment Tool (OST) in a Caucasian population from Belgium," QJM, Vol. 97, 39-46, 2004.
doi:10.1093/qjmed/hch002

12. Kim, S. K., T. K. Yoo, E. Oh, and D. W. Kim, "Osteoporosis risk prediction using machine learning and conventional methods," 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 188-191, Osaka, July 2013.

13. Ho-Le, T. P., J. R. Center, J. A. Eisman, T. V. Nguyen, and H. T. Nguyen, "Prediction of hip fracture in post-menopausal women using artificial neural network approach," 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 4207-4210, Seogwipo, July 2017.

14. Wang, W., B. Jiang, S. Ye, and L. Qian, "Risk factor analysis of bone mineral density based on feature selection in type 2 diabetes," 2018 IEEE International Conference on Big Knowledge (ICBK), 221-226, Singapore, 2018.

15. Rivas, P., S. Moore, U. Iwaniec, R. Turner, K. Grant, and E. Baker, "Optimizing support vector machine analysis in low density biological data sets," 2018 International Conference on Computational Science and Computational Intelligence (CSCI ), 1357-1361, Las Vegas, NV, USA, 2018.

16. Krishnaraj, A., S. Barrett, O. Bregman-Amitai, M. Cohen-Sfady, A. Bar, D. Chettrit, M. Orlovsky, and E. Elnekave, "Simulating dual-energy X-ray absorptiometry in CT using deep-learning segmentation cascade," Journal of the American College of Radiology, Vol. 16, No. 10, 1473-1479, 2019.
doi:10.1016/j.jacr.2019.02.033

17. Fathima, S. M. N., R. Tamilselvi, M. P. Beham, and A. Nagaraj, "A deep learning approach on segmentation of bone for BMD measurement from DEXA scan images," 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII), 1-5, Chennai, India, October 2020.

18. Recenti, M., C. Ricciardi, K. Edmunds, M. K. Gislason, and P. Gargiulo, "Machine learning predictive system based upon radiodensitometric distributions from mid-thigh CT images," European Journal of Translational Myology, Vol. 30, No. 1, 2020.
doi:10.4081/ejtm.2019.8892

19. Recenti, M., C. Ricciardi, R. Aubonnet, L. Esposito, H. Jonsson, and P. Gargiulo, "A regression approach to assess bone mineral density of patients undergoing total hip arthroplasty through gait analysis," 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 1-6, Bari, Italy, 2020.

20. Minonzio, J. G., B. Cataldo, R. Olivares, D. Ramiandrisoa, R. Soto, B. Crawford, V. H. C. De Albuquerque, and R. Munoz, "Automatic classifying of patients with non-traumatic fractures based on ultrasonic guided wave spectrum image using a dynamic support vector machine," IEEE Access, Vol. 8, 194752-194764, 2020.
doi:10.1109/ACCESS.2020.3033480

21. Amin, B., M. A. Elahi, A. Shahzad, E. Parle, L. McNamara, and M. O'Halloran, "An insight into bone dielectric properties variation: A foundation for electromagnetic medical devices," 2018 EMF-Med 1st World Conference on Biomedical Applications of Electromagnetic Fields (EMF-Med), 1-2, Split, Croatia, 2018.

22. Andreuccetti, D., R. Fossi, and C. Perrucci, "Calculation of the dielectric properties of body tissues in the frequency range 10 Hz-100 GHz," IFAC-CNR, Florence (Italy), 1997-2015.

23. Gabriel, C., T. Y. A. Chan, and E. H. Grant, "Admittance models for open ended coaxial probes and their place in dielectric spectroscopy," Phys. Med. Biol., Vol. 39, 2183-2200, 1994.
doi:10.1088/0031-9155/39/12/004

24. Zaouiat, C. E. and A. Latif, "Internet of things and machine learning convergence: The E-healthcare revolution," Proceedings of the 2nd International Conference on Computing and Wireless Communication Systems, 1-5, 2016.

25. Huh, J., "Big data analysis for personalized health activities: Machine learning processing for automatic keyword extraction approach," Symmetry, Vol. 10, No. 4, 93, 2018.
doi:10.3390/sym10040093

26. Sasubilli, S. M., A. Kumar, and V. Dutt, "Machine learning implementation on medical domain to identify disease insights using TMS," 2020 International Conference on Advances in Computing and Communication Engineering (ICACCE), 1-4, Las Vegas, NV, USA, June 2020.

27. Gavankar, S. S. and S. D. Sawarkar, "Eager decision tree," 2017 2nd International Conference for Convergence in Technology (I2CT), 837-840, Mumbai, India, April 2017.

28. Patil, D. V. and R. S. Bichkar, "A hybrid evolutionary approach to construct optimal decision trees with large data sets," 2006 IEEE International Conference on Industrial Technology, 429-433, Mumbai, India, December 2006.
doi:10.1109/ICIT.2006.372250

29. Lan, H. and Y. Pan, "A crowdsourcing quality prediction model based on random forests," 2019 IEEE/ACIS 18th International Conference on Computer and Information Science (ICIS), 315-319, Beijing, China, June 2019.

30. Breiman, L., "Bagging predictors," Machine Learning, Vol. 24, No. 2, 123-140, 1996.

31. Yi, H., Q. Xiong, Q. Zou, R. Xu, K. Wang, and M. Gao, "A novel random forest and its application on classification of air quality," 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI), 35-38, Toyama, Japan, July 2019.

32. Conceicao, R. C., M. O'Halloran, M. Glavin, and E. Jones, "Support vector machines for the classification of early-stage breast cancer based on radar target signatures," Progress In Electromagnetics Research B, Vol. 23, 311-327, 2010.
doi:10.2528/PIERB10062407

33. Wu, Y., Z.-X. Tang, B. Zhang, and Y. Xu, "Permeability measurement of ferromagnetic materials in microwave frequency range using support vector machine regression," Progress In Electromagnetics Research, Vol. 70, 247-256, 2007.
doi:10.2528/PIER07012801

34. Hall, M. and G. Holmes, "Benchmarking attribute selection techniques for discrete class data mining," IEEE Transactions on Knowledge and Data Engineering, Vol. 15, No. 3, 1437-1447, 2003.
doi:10.1109/TKDE.2003.1245283

35. Kerketta, S. R. and D. Ghosh, "Detection of onset and progression of osteoporosis using machine learning," Machine Learning for Healthcare Applications, 137-149, 2021.
doi:10.1002/9781119792611.ch9

36. Kumar, D., A. Sarkar, S. R. Kerketta, and D. Ghosh, "Human activity classification based on breathing patterns using IR-UWB radar," 2019 IEEE 16th India Council International Conference (INDICON), 1-4, Rajkot, India, December 2019.

Dr. Shilpi Ruchi Kerketta

Dr. Debalina Ghosh