Application of sibgle wireless holter to simultaneous EMG, MMG and eim measurement of human muscles activity
Autoři:
Erik Vavrinsky 1,2; Helena Svobodova 2; Martin Donoval 1,3; Martin Daricek 1,3; Martin Kopani 2; Peter Miklovic 4; Frantisek Horinek 1; Peter Telek 1
Působiště autorů:
Institute of Electronics and Photonics, Slovak University of Technology, Bratislava, Slovakia
1; Institute of Medical Physics, Biophysics, Informatics and Telemedicine, Comenius University, Bratislava, Slovakia
2; NanoDesign ltd., Bratislava, Slovakia
3; Technological Institute of Sports, Slovak University of Technology, Bratislava, Slovakia
4
Vyšlo v časopise:
Lékař a technika - Clinician and Technology No. 2, 2018, 48, 52-58
Kategorie:
Original research
Souhrn
This paper describes presentation, application and design of wireless holter with innovative functionality, used it in field of human muscular monitoring. In our experiments we monitored EMG (electromyography), MMG (mechanomyography) and EIM (electrical impedance myography), all by single device. New design of our holter allows measure with high quality and ultra-low power consumption. In this study we compared fatigue, load, total power, mean frequency and dependency of amplitude of human muscles. It is the first time when these all parameters were monitored simultaneously taking advantage of the holter device data output in order to find the signals interconnection. Data were compared with normally used medical devices and signal quality was verified. Our results confirmed that our device can precisely monitor muscle activity. The holter has a scientific potential and it can be applied in kinesiology or for control of electrical devices such as robotic and prosthetic body-parts.
Keywords:
EMG, MMG, EIM, single device, holter monitoring
Zdroje
[1] Bilgin, G., Hindistan, E. , Ozkaya, Y. G., Koklukaya, E., Polat, O., Colak, O. H.: Determination of Fatigue Following Maximal Loaded Treadmill Exercise by Using Wavelet Packet Transform Analysis and MLPNN from MMG-EMG Data Combinations. Journal of Medical Systems, 2015, vol. 39, no. 10.
[2] Konrad, P.: The ABC of EMG, no. March. 2006.
[3] Merlo, A., Campanini, I.: Technical Aspects of Surface Electromyography for Clinicians. The Open Rehabilitation Journal, 2010, vol. 3, no. 1, pp. 98–109.
[4] Kamen, G., Gabriel, D. A.: Essentials of electromyography: Champaign. Human Kinetics, 2010.
[5] Quasthoff, S.: Grundlagen der EMG Untersuchung. Das Neurophysiology, 2010, vol. 32, no. 1, pp. 1–27.
[6] Uchiyama, T., Hashimoto, E.: System identification of the mechanomyogram from single motor units during voluntary isometric contraction. Medical & Biological Engineering & Computing, 2011, vol. 49, no. 9, pp. 1035–1043.
[7] Esposito, F., Limonta, E., Cè, E.: Time course of stretching-induced changes in mechanomyogram and force characteristics. Journal of Electromyography and Kinesiology, 2011, vol. 21, no. 5, pp. 795–802.
[8] Longo, S., Cè, E., Rampichini, S., Devoto, M., Venturelli, M., Limonta, E., Esposito, F.: Correlation between stiffness and electromechanical delay components during muscle contraction and relaxation before and after static stretching. Journal of Electromyography and Kinesiology, 2017, vol. 33, pp. 83–93.
[9] Sanchez, B., Rutkove, S. B.: Present Uses, Future Applications, and Technical Underpinnings of Electrical Impedance Myography. Current Neurology &. Neuroscience Reports, 2017, vol. 17, no. 11, pp. 86.
[10] Li, X., Shin, H., Li, L., Magat, E., Li, S. and Zhou, P.: Assessing the immediate impact of botulinum toxin injection on impedance of spastic muscle. Med. Eng. Phys., vol. 43, pp. 97–102, 2017.
[11] Zhang, X., Li, X., Samuel, O. W., Huang, Z., Fang, P., Li, G.: Improving the robustness of electromyogram-pattern recogni-tion for prosthetic control by a postprocessing strategy, Front. Neurorobot., vol. 11, no. SEP, pp. 1–15, 2017.
[12] Abad, S. L. M., Maghooli, K.: Low Supply Voltage Electro-cardiogram Signal Amplifier.1st International Conference on Bioinformatics and Biomedical Engineering, 2007, pp. 798–801.
[13] De Luca, C. J.: Electromyography. Encyclopedia of Medical devices and Instrumentation, 2006, pp. 98–109.
[14] Stegeman, D. F., Blok, J. H., Hermens, H. J., Roeleveld, K.: Surface EMG models: Properties and applications. Journal of Electromyography and Kinesiology, 2000, vol. 10, no. 5, pp. 313–332.
[15] Merlo, A., Farina, D., Merletti, R.: A fast and reliable technique for muscle activity detection from surface EMG signals. IEEE Transactions on Biomedical Engineering, 2003, vol. 50, no. 3, pp. 316–323.
[16] Kimoto, A., Yamada, Y.: A new layered sensor for simultaneous measurement of EMG, MMG and oxygen consumption at the same position. Medical & Biological Engineering & Computing, 2015, vol. 53, no. 1, pp. 15–22.
[17] Lee, K.-S.: EMG-based speech recognition using hidden markov models with global control variables. IEEE Transactions on Biomedical Engineering, 2008, vol. 55, no. 3, pp. 930–940.
[18] Cifrek, M., Medved, V., Tonković, S., Ostojić, S.: Surface EMG based muscle fatigue evaluation in biomechanics. Clinical Biomechanics, 2009, vol. 24, no. 4, pp. 327–340.
[19] Hill, E. C., Housh, T. J., Smith, C. M., Cochrane, K. C., Jenkins, N. D. M., Cramer, J. T., Schmidt, R. J., Johnson, G. O.: Effect of sex on torque, recovery, EMG, and MMG responses to fatigue. Journal of Musculoskeletal and Neuronal Interactions, 2016, vol. 16, no. 4, pp. 310–317.
[20] Vavrinsky, E., Daricek, M., Horinek, F., Donoval, M.: Design of Very Precise and Miniature Low Power ECG Holter. In Electrocardiology 2014 : Proceedings of the 41st International Congress on Electrocardiology, 2014, pp. 257–260.
[21] Gupta, A. K.: Respiration Rate Measurement Based on Impedance Pneumography. Application Report, Texas Instruments, 2011, pp. 1–11.
[22] Kugelstadt, T.: Getting the most out of your instrumentation amplifier design. Analog Applications Journal, 2005, vol. 1, no. 5, pp. 25–30.
[23] Vavrinsky, E., Stopjakova, V., Donoval, M., Daricek, M., Svobodova, H., Mihalov, J., Hanic, M., Tvarozek, V.: Design of sensor systems for long time electrodermal activity monitoring. Advances in Electrical and Electronic Engineering, 2017, vol. 15, no. 2, pp. 184–191.
[24] Tosovic, D., Than, C., Brown, J. M. M.: The effects of accumulated muscle fatigue on the mechanomyographic waveform: implications for injury prediction. European Journal of Applied Physiology, 2016, vol. 116, no. 8, pp. 1485–1494.
[25] Ray, G. C., Guha, S. K.: Relationship between the surface e.m.g. and muscular force. Medical & Biological Engineering & Computing, 1983, vol. 21, no. 5, pp. 579–586.
[26] Enoka, R. M.: Muscle fatigue - from motor units to clinical symptoms.Journal of Biomechanic, 2012, vol. 45, no. 3, pp.
427–433.
[27] Calvert, T. W., Chapman, A. E.: The relationship between the surface EMG and force transients in muscle: Simulation and experimental studies. Proceedings of the. IEEE, 1977, vol. 65, no. 5, pp. 682–689.
[28] Han, H., Jo, S., Kim, J.: Comparative study of a muscle stiffness sensor and electromyography and mechanomyography under fatigue conditions. Medical &. Biological Engineering & Computing, 2015, vol. 53, no. 7, pp. 577–588.
[29] González-Izal, M., Malanda, A., Navarro-Amézqueta, I., Gorostiaga, E. M., Mallor, F., Ibañez, J., Izquierdo, M.: EMG spectral indices and muscle power fatigue during dynamic contractions. Journal of Electromyography and Kinesiology, 2010, vol. 20, no. 2, pp. 233–240.
[30] Al-Mulla, M. R., Sepulveda, F., Colley, M.: A review of non-invasive techniques to detect and predict localised muscle fatigue. Sensors, 2011, vol. 11, no. 4, pp. 3545–3594.
Štítky
BiomedicínaČlánek vyšel v časopise
Lékař a technika
2018 Číslo 2
Nejčtenější v tomto čísle
- Response by an automated inspired oxygen control system to hypoxemic episodes: assessment of damping
- Application of sibgle wireless holter to simultaneous EMG, MMG and eim measurement of human muscles activity
- Workflow for bioprinting of cell-ladem bioink
- Machine learning using speech utterances for parkinson disease detection