Multi-Point Multi-Dimensional Accelerometer Data Logging System for Biomedical Applications

Yıl 2022, Cilt: 24 Sayı: 72, 787 - 797, 19.09.2022

Taner Akkan , Yavuz Şenol , Murat Özgören

https://doi.org/10.21205/deufmd.2022247209

Öz

Some biomedical signal processing applications require specific data logging hardware. Knee related non-invasive diagnosing and jaw related electroencephalography (EEG) artifact cleaning applications would be good candidates requiring simultaneous multi-channel vibration data logging. In this study, a novel multi-point multi-dimensional accelerometer data logging system was proposed. This system collects three-dimensional tilting and vibration data from three different points simultaneously by using accelerometers. Multi-channel signal analyzing requires simultaneous data recordings for filtering and separating the sensor data into components. The selected accelerometer provides the requirement of simultaneous three axes data recordings. The accelerometer data logging system can be used to obtain tilting and vibration data from knee for diagnosing support and from jaw for EEG jaw artifact cleaning support. Three accelerometers can be placed on kneecap and lateral positions to detect vibrations of knee movements (vibroarthrographic (VAG) signals). The obtained VAG signals can be evaluated by statistical or time-frequency analysis techniques. Also, three accelerometers are placed on face to record jaw and neck movements. Simultaneously recorded EEG and jaw data can be further analyzed by filtering or statistical methods to extract undesired neck and jaw artifacts.

Anahtar Kelimeler

Accelerometers, Biomedical signal processing, Electroencephalography, Independent component analysis, Vibration measurement

Teşekkür

The authors thank Prof. Dr. Hasan Havıtçıoğlu on his discussion for the use of the designed system in knee application. The authors thank to the members of Dokuz Eylul University Biophysics Labs for their support for the jaw application. 2006.KB.SAG.017, 2008.KB.SAG.019 and TUBITAK 108S113 projects have been partially utilized.

Biyomedikal Uygulamalar için Çok Noktalı Çok Boyutlu İvmeölçer Veri Kayıt Sistemi

Öz

Bazı biyomedikal sinyal işleme uygulamaları, özel veri kaydı donanımı gerektirir. Diz ile ilgili non-invaziv teşhis ve çene ile ilgili elektroensefalografi (EEG) bozunum temizleme uygulamaları, eşzamanlı çok kanallı titreşim veri kaydı gerektiren iyi adaylar olacaktır. Bu çalışmada, yeni bir çok noktalı çok boyutlu ivme veri kayıt sistemi önerilmiştir. Bu sistem ivmeölçerler kullanarak aynı anda üç farklı noktadan üç boyutlu eğilim ve titreşim verilerini toplamaktadır. Çok kanallı sinyal analizi, sensör verilerini filtrelemek ve bileşenlerine ayırmak için eşzamanlı veri kayıtları gerektirir. Seçilen ivmeölçer, aynı anda üç eksen veri kaydı gerekliliğini sağlamaktadır. İvmeölçer veri kayıt sistemi, teşhis desteği için dizden ve EEG çene bozunum temizleme desteği için çeneden eğilim ve titreşim verileri elde etmek için kullanılabilir. Diz hareketlerinin titreşimlerini (vibroartrografik (VAG) sinyaller) tespit etmek için diz kapağı ve yan pozisyonlara üç ivmeölçer yerleştirilebilir. Elde edilen VAG sinyalleri, istatiksel veya zaman-frekans analiz teknikleri ile değerlendirilebilir. Ayrıca çene ve boyun hareketlerini kaydetmek için yüze üç adet ivmeölçer yerleştirilmiştir. Eş zamanlı olarak kaydedilen EEG ve çene verileri, istenmeyen boyun ve çene bozunumlarını çıkarmak için filtreleme veya istatistik yöntemler ile daha ileri bir şekilde analiz edilebilir.

Anahtar Kelimeler

İvmeölçerler, Biyomedikal sinyal işleme, Elektroensefalografi, Bağımsız bileşen analizi, Titreşim ölçümü

Kaynakça

• [1] Nokes L.D. 1999. The use of low-frequency vibration measurement in orthopaedics, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine, 213(3), pp. 271–290. DOI: 10.1243/0954411991534979
• [2] Foster, R., Lanningham-Foster, L., & Levine, J. 2008. Optimization of accelerometers for measuring walking, Proceedings of the Institution of Mechanical Engineers, Part P: Journal of Sports Engineering and Technology, 222(1), pp. 53–60. DOI: 10.1243/17543371JSET3
• [3] Tavathia, S., Rangayyan, R.M., Frank, C.B., Bell, G.D., Ladly, K.O., & Zhang, Y.T. 1992. Analysis of knee vibration signals using linear prediction, IEEE transactions on bio-medical engineering, 39(9), pp.959–970. DOI: 10.1109/10.256430
• [4] Zhang, Y.T., Rolston, W. A., Rangayyan, R.M., Frank, C.B. and Bell, G.D. 1992. Wavelet Transform Analysis of Vibroarthrographic (VAG) signals obtained during dynamic knee movement, Proceedings of the IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis, pp. 235-238.
• [5] Shen, Y., Rangayyan, R. M., Bell, G. D., Frank, C. B., Zhang, Y. T., & Ladly, K. O. 1995. Localization of knee joint cartilage pathology by multichannel vibroarthrography, Medical Engineering & Physics, 17(8), pp. 583–594. DOI: 10.1016/1350-4533(95)00013-d
• [6] Krishnan S. and Rangayyan, R. M. 1999. Denoising knee joint vibration signals using adaptive time-frequency representations, Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411), pp. 1495-1500 vol.3, DOI: 10.1109/CCECE.1999.804930
• [7] Krishnan, S., & Rangayyan, R.M. 2000. Automatic de-noising of knee-joint vibration signals using adaptive time-frequency representations, Medical & biological engineering & computing, 38(1), pp. 2–8. DOI: 10.1007/BF02344681
• [8] Krishnan, S., Rangayyan, R.M., Bell G.D. and Frank, C.B. 2000. Adaptive time-frequency analysis of knee joint vibroarthrographic signals for noninvasive screening of articular cartilage pathology, IEEE Transactions on Biomedical Engineering, vol. 47, no. 6, pp. 773-783, DOI: 10.1109/10.844228
• [9] Krishnan, S., Rangayyan, R.M., Bell G.D. and Frank, C.B. 2001. Auditory display of knee-joint vibration signals, The Journal of the Acoustical Society of America, vol. 110(6), pp. 3292-3304, DOI: 10.1121/1.1413995
• [10] Rangayyan, R.M. 2002. Biomedical Signal Analysis: A Case-Study Approach. Wiley-IEEE Press, pp. 46-48, 556 pages, ISBN 0471208116
• [11] Rangayyan, R.M., Wu. Y.F. 2008. Screening of knee-joint vibroarthrographic signals using statistical parameters and radial basis functions, Medical and Biological Engineering and Computing, 46(3), pp. 223-232, DOI: 10.1007/s11517-007-0278-7
• [12] Rangayyan, R.M., Wu. Y.F. 2009. Analysis of vibroarthrographic signals with features related to signal variability and radial-basis functions, Annals of Biomedical Engineering, 37(1), pp. 156-163, DOI: 10.1007/s10439-008-9601-1
• [13] Cai, S., Yang, S., Zheng, F., Lu, M., Wu, Y., & Krishnan, S. 2013. Knee joint vibration signal analysis with matching pursuit decomposition and dynamic weighted classifier fusion, Computational and mathematical methods in medicine, 904267. DOI: 10.1155/2013/904267
• [14] Łysiak, A., Froń, A., Bączkowicz, D., & Szmajda, M. 2020. Vibroarthrographic Signal Spectral Features in 5-Class Knee Joint Classification, Sensors (Basel, Switzerland), 20(17), 5015, DOI: 10.3390/s20175015
• [15] de Tocqueville S, Marjin M, Ruzek M. 2021. A Review of the Vibration Arthrography Technique Applied to the Knee Diagnostics, Applied Sciences. 11(16):7337. DOI: 10.3390/app11167337
• [16] Shidore, M.M., Athreya, S.S., Deshpande, S., & Jalnekar, R. 2021. Screening of knee-joint vibroarthrographic signals using time and spectral domain features. Biomedical Signal Processing and Control, 68, 102808. DOI: 10.1016/j.bspc.2021.102808
• [17] Gong, R., Ohtsu, H., Hase, K., & Ota, S. 2021. Vibroarthrographic signals for the low-cost and computationally efficient classification of aging and healthy knees. Biomedical Signal Processing and Control, 70, 103003. DOI: 10.1016/j.bspc.2021.103003
• [18] Verma, D. K., Kumari, P., & Kanagaraj, S. 2022. Engineering Aspects of Incidence, Prevalence, and Management of Osteoarthritis: A Review. Annals of Biomedical Engineering, 1-16. DOI: 10.3390/s22103765
• [19] Ye, Y., Wan, Z., Liu, B., Xu, H., Wang, Q., & Ding, T. 2022. Monitoring deterioration of knee osteoarthritis using vibration arthrography in daily activities. Computer Methods and Programs in Biomedicine, 213, 106519.
• [20] Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. 2022. Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part I: Femoral-Tibial Joint. Sensors, 22(6), 2176. DOI: 10.3390/s22062176
• [21] Karpiński, R., Krakowski, P., Jonak, J., Machrowska, A., Maciejewski, M., & Nogalski, A. 2022. Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN—Part II: Patellofemoral Joint. Sensors, 22(10), 3765. DOI: 10.3390/s22103765
• [22] Praveena R., Ravish D.K., Ganesh Babu T.R. and Preetha J., 2021. Design and Development of Vibroarthrogram Screening Device and Assessment of Joint Motion in the pursuit of Signal Processing, ICTACT Journal, Vol.11, Issue 04, pp. 2453-2459.
• [23] MemsNet, What is MEMS Technology, https://www.memsnet.org/about/what-is.html (Accessed March 5, 2022)
• [24] MemsNet, MEMS and Nanotechnology Applications, https://www.memsnet.org/about/applications.htm(Accessed March 5, 2022)
• [25] Akkan T. and Senol Y. 2008. Capturing and analysis of knee-joint signals using acceleremoters, IEEE 16th Signal Processing, Communication and Applications Conference, 2008, pp. 1-4, DOI: 10.1109/SIU.2008.4632614
• [26] Akkan T., Şenol Y. 2009. Applied Accelerometer Data Logging System. pp.195-200. Özgören,M., Öniz A., eds. 2009. The Applied Biophysics-Uygulamali Beyin Biyofizigi ve Multidisipliner Yaklasim, Dokuz Eylul Yayinlari, D.E.U. Rektorluk Matbaasi, Izmir, Turkey. ISBN: 978-975-441-259-8.
• [27] Christopher, J., Christian, W.H., 2005. Independent component analysis for biomedical signals, Physiological measurement. 26(1), pp.15-39, DOI:10.1088/0967-3334/26/1/R02.
• [28] Microchip Inc., Microchip PIC18F4550 Data Sheet, https://ww1.microchip.com/downloads/en/devicedoc/39632c.pdf (Accessed March 5, 2022)
• [29] ST Microelectronics, LIS3LV02DQ MEMS Inertial Sensor,https://www.st.com/resource/en/datasheet/cd00047926.pdf (Accessed March 5, 2022)
• [30] Flandry, F., & Hommel, G. (2011). Normal anatomy and biomechanics of the knee. Sports medicine and arthroscopy review, 19(2), 82–92. https://doi.org/10.1097/JSA.0b013e318210c0aa
• [31] Jung, T.P., Makeig, S., Humphries, C., Lee, T.W., McKeown, M. J., Iragui, V., & Sejnowski, T.J. 2000. Removing electroencephalographic artifacts by blind source separation, Psychophysiology, 37(2), pp. 163–178. DOI: 10.1111/1469-8986.3720163
• [32] Benbadis, S.R. et.al. 2019. EEG Artifacts, http://emedicine.medscape.com/article/1140247-overview (Accessed March 5, 2022)