Adaptive Vibrarthographic Signal Denoising via Ant Colony Optimization Using Dynamic Denoising Filter Parameters


  • Rui Gong Graduate School of Systems Design, Tokyo Metropolitan University, Tokyo, Japan; Informatics and Computer Education Center, Mejiro University, Tokyo, Japan
  • Kazunori Hase Faculty of Systems Design, Tokyo Metropolitan University, Tokyo, Japan
  • Hajime Ohtsu Graduate School of Systems Design, Tokyo Metropolitan University, Tokyo, Japan; Japan Society for the Promotion of Science, Tokyo, Japan
  • Susumu Ota Faculty of Rehabilitation and Care, Seijoh University, Nagoya, Japan



ant colony optimization, denoising, ensemble empirical mode decomposition, vibrarthographic signal


This study proposes an ant colony optimization (ACO) denoising method with dynamic filter parameters. The proposed method is developed based on ensemble empirical mode decomposition (EEMD), and aims to improve the quality of vibrarthographic (VAG) signals. It mixes the original VAG signals with different white noise amplitudes, and adopts a hybrid technology that combines EEMD with a Savitzky-Golay (SG) filter containing the dynamic parameters optimized by ACO. The results show that the proposed method provides a higher peak signal-to-noise ratio (PSNR) and a smaller root-mean-square difference than the regular methods. The SNR improvement for the VAG signals of normal knees can reach 13 dB while maintaining the original signal structure, and the SNR improvement for the VAG signals of abnormal knees can reach 20 dB. The method proposed in this study can improve the quality of nonstationary VAG signals.


D. W. Jackson, T. M. Simon, and H. M. Aberman, “Symptomatic Articular Cartilage Degeneration: The Impact in the New Millennium,” Clinical Orthopaedics and Related Research, vol. 391, pp. 14-25, October 2001.

A. Balajee and R. Venkatesan, “A Survey on Classification Methodologies Utilized for Classifying the Knee Joint Disorder Levels Using Vibroarthrographic Signals,” Materials Today: Proceedings, in press.

D. B. Percival and A. T. Walden, Wavelet Methods for Time Series Analysis, Cambridge: Cambridge University Press, 2010.

R. Gong, K. Hase, H. Goto, K. Yoshioka, and S. Ota, “Knee Osteoarthritis Detection Based on the Combination of Empirical Mode Decomposition and Wavelet Analysis,” Journal of Biomechanical Science and Engineering, vol. 15, no. 3, 20-00017, 2020.

R. James, H. Appukuttan, and L. A. Joseph, “Mixed Noise Removal by Processing of Patches,” Proceedings of Engineering and Technology Innovation, vol. 17, pp. 32-41, January 2021.

R. Gong, H. Ohtsu, K. Hase, and S. Ota, “Vibroarthrographic Signals for the Low-Cost and Computationally Efficient Classification of Aging and Healthy Knees,” Biomedical Signal Processing and Control, vol. 70, 103003, September 2021.

A. Sundar, V. Pahwa, and C. Das, “A New Method for Denoising Knee Joint Vibroarthrographic Signals,” Annual IEEE India Conference, pp. 1-5, December 2015.

Y. T. Chiang, C. H. Lu, C. C. Tuan, T. F. Lee, Y. C. Huang, and M. C. Chen, “Non-Invasive Detection of Sound Signals for Diagnosis of Ligament Injuries around Knee Based on Mel-Frequency Cepstrum,” Intelligent Systems and Applications, vol. 274, pp. 1940-1949, 2015.

J. Rahul, M. Sora, and L. D. Sharma, “An Overview on Biomedical Signal Analysis,” International Journal of Recent Technology and Engineering, vol. 7, no. 5, pp. 206-209, January 2019.

M. E. Torres, M. A. Colominas, G. Schlotthauer, and P. Flandrin, “A Complete Ensemble Empirical Mode Decomposition with Adaptive Noise,” IEEE International Conference on Acoustics, Speech, and Signal Processing, pp. 4144-4147, May 2011.

P. A. Vikhar, “Evolutionary Algorithms: A Critical Review and Its Future Prospects,” International Conference on Global Trends in Signal Processing, Information Computing, and Communication, pp. 261-265, December 2016.

R. Gong and K. Hase, “A Plant Root System Algorithm Based on Swarm Intelligence for One-Dimensional Biomedical Signal Feature Engineering,”, July 31, 2021.

L. Li, Y. Ma, B. Wang, H. Dong, and Z. Zhang, “Research on Traffic Signal Timing Method Based on Ant Colony Algorithm and Fuzzy Control Theory,” Proceedings of Engineering and Technology Innovation, vol. 11, pp. 21-29, January 2019.

S. Ota, A. Ando, Y. Tozawa, T. Nakamura, S. Okamoto, T. Sakai, et al., “Preliminary Study of Optimal Measurement Location on Vibroarthrography for Classification of Patients with Knee Osteoarthritis,” Journal of Physical Therapy Science, vol. 28, no. 10, pp. 2904-2908, 2016.

R. Gong, K. Hase, H. Goto, and K. Yoshioka, “Post-Processing Algorithm for Removing Soft-Tissue Movement Artifacts from Vibroarthrographic Knee-Joint Signal,” 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 936-939, July 2020.

N. Befrui, J. Elsner, A. Flesser, J. Huvanandana, O. Jarrousse, T. N. Le, et al., “Vibroarthrography for Early Detection of Knee Osteoarthritis Using Normalized Frequency Features,” Medical and Biological Engineering and Computing, vol. 56, no. 8, pp. 1499-1514, August 2018.

R. Mancini and B. Carter, Op Amps for Everyone, United States: Elsevier, 2009.

B. Boashash, Time-Frequency Signal Analysis and Processing: A Comprehensive Reference, London: Elsevier, 2016.

Z. Wu and N. E. Huang, “Ensemble Empirical Mode Decomposition: A Noise Assisted Data Analysis Method,” Advances in Adaptive Data Analysis, vol. 1, no. 1, pp. 1-41, January 2009.

G. F. Inbar, O. Paiss, J. Allin, and H. Kranz, “Monitoring Surface EMG Spectral Changes by the Zero Crossing Rate,” Medical and Biological Engineering and Computing, vol. 24, no. 1, pp. 10-18, 1986.

M. Tanweer and K. A. Halonen, “Development of Wearable Hardware Platform to Measure the ECG and EMG with IMU to Detect Motion Artifacts,” IEEE 22nd International Symp. on Design and Diagnostics of Electronic Circuits and Systems, pp. 1-4, April 2019.

R. Schafer, “What is a Savitzky-Golay Filter?” IEEE Signal Processing Magazine, vol. 28, no. 4, pp. 111-117, July 2011.

S. Dubnov, “Generalization of Spectral Flatness Measure for Non-Gaussian Linear Processes,” IEEE Signal Processing Letters, vol. 11, no. 8, pp. 698-701, August 2004.

K. Socha and M. Dorigo, “Ant Colony Optimization for Continuous Domains,” European Journal of Operational Research, vol. 185, no. 3, pp. 1155-1173, March 2008.

J. Ning, Q. Zhang, C. Zhang, and B. Zhang, “A Best-Path-Updating Information-Guided Ant Colony Optimization Algorithm,” Information Sciences, vol. 433, pp. 142-162, April 2018.

S. Poornachandra and N. Kumaravel, “Subband-Adaptive Shrinkage for Denoising of ECG Signals,” EURASIP Journal on Advances in Signal Processing, vol. 2006, 081236, December 2006.

N. Tanaka and M. Hoshiyama, “Articular Sound and Clinical Stages in Knee Arthropathy,” Journal of Musculoskeletal Research, vol. 14, no. 1, 1150006, March 2011.

B. H. Tracey and E. L. Miller, “Nonlocal Means Denoising of ECG Signals,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 9, pp. 2383-2386, September 2012.

C. Kaur, A. Bisht, P. Singh, and G. Joshi, “EEG Signal Denoising Using Hybrid Approach of Variational Mode Decomposition and Wavelets for Depression,” Biomedical Signal Processing and Control, vol. 65, 102337, March 2021.

G. Tzanetakis, G. Essl, and P. Cook, “Audio Analysis Using the Discrete Wavelet Transform,” WSEAS International Conference on Acoustics and Music: Theory and Applications, pp. 318-323, September 2001.




How to Cite

R. Gong, K. Hase, H. Ohtsu, and S. Ota, “Adaptive Vibrarthographic Signal Denoising via Ant Colony Optimization Using Dynamic Denoising Filter Parameters”, Int. j. eng. technol. innov., vol. 12, no. 1, pp. 01–15, Dec. 2021.