Decomposition of Motor Unit Firing Pattern Using Kalman Filtering

Interdischarge interval (IDI) is one of the basic parameters to study in motor unit firing analysis. Discharge intervals of a single motor unit vary over time and they are unpredictable. IDI sequences can be considered to comprise two components, namely a long term signal, an IDI trend, and a white noise process, instantaneous firing variability (IFV). In this paper a stochastic model of the IDI signal has been developed in order to estimate the elements of an IDI sequence. IDI sequences of several patients have been recorded at a clinic and a Kalman filter has been constructed based on the developed stochastic model. The Kalman filter is utilized to decompose the recorded IDI sequences into the IDI trend and IFV components. The obtained decomposed signals, especially the IDI trend component, may provide valuable information on motor unit firing performance and help diagnose neurological diseases. Key Words: Motor unit firing, IDI sequences, state-space modeling, Kalman filter. 

___

  • Datta, A.K., Farmer, S.F., Stephens, J.A., “Central nervous pathways underlying synchronization of human motor unit firing studied during voluntary contractions”, J. Physiol Lond., 432: 401-425 (1991).
  • Farmer, S.F., Swash, M., Ingram, D.A., “Changes in motor unit synchronization following central nervous lesion in man”, J. Physiol Lond., 463: 83- 105 (1993).
  • Dorfman, L.J., Howard, J.E., McGill, K.C., “Motor unit firing rates and firing rate variability in the detection Electroencephalogr Clin Neurophysiol, 73: 215- 224 (1989). disorder”,
  • Gamperline, J.J., Allen, S., Walk,D., Rymer,W.Z., “Characteristics of motor unit discharge in subject with hemiparesis”, Muscle Nerve, 18:1101-1114 (1995).
  • Clamann, H.P., “Statistical analysis of motor unit firing patterns in a human skeletal muscle”, Biophys J. 9: 1233-1251 (1969).
  • Englehart, K.B.,Parker, P.A., “Single motor unit myoelectric signal analysis with nonstationary data”, IEEE Trans Biomed Eng., 41:168-180 (1994).
  • Person, R.S., Kudina, L.P., “Discharge frequency and discharge pattern of human motor units during voluntary Electroencephalogr Clin Neurophysiol, 32: 471- 483 (1972). of muscle”,
  • Andreassen, S., Rosenfalck, A., “Regulation of firing pattern of single motor unit”, J. Neurol Neurosurg Phychiatry, 43: 897- 906 (1980).
  • Sahani, B.T., Wierzbricka, M.M., Parker,S.W., “Abnormal single motor unit behaviour in upper motor neuron syndrome”, Muscle Nerve, 14: 64-69 (1991).
  • Jazwinski, A.H., “Stochastic Processes and Filtering Theory”, Academic Press, Newyork, 199 (1970).
  • Aliev, F., Özbek, L., “Evaluation of Convergence Rate in the Central Limit Theorem for the Kalman Filter”, IEEE Transactions on Automatic Control. 44(10): 1905-1909 (1999).
  • Özbek L., Efe M., “An Adaptive Extended Kalman Filter with application to compartment models”, Communication in Statistics-Simulation and Computation, 3:145-158 (2004).
  • Ciocoiu, I.B., “RBF networks training using a dual extended Kalman Filter”, Neurocomputing, 48: 609-622 (2002).
  • Hinrichs, H., Feistner, H., Heinze H.J., “A trend- detection monitoring”, Med. Eng. Phy., 18: 626-631 (1996). intraoperative EEG
  • Tuckwell, H.C., “Stochastic Processes in the Neuroscience, Society for Industrial and Applied Mathematics, SIAM, Philadelphia, 73-102 (1989)
  • Fang, J., Sahani ,B.T., Bruyninckx, F.L., “Study of single motor unit discharge patterns using 1/F process model”, Muscle Nerve, 20:293-298 (1997).
  • Sun, T.Y., Chen, J.J., Lin, T.S., “Analysis of motor unit firing patterns in patients with central or peripheral decomposition”, Muscle Nerve, 23:1057-1068 (2000). using singular-value
  • Harrison, P.J., Stevens, C.F., “A Bayesian forecasting (with discussion)”, J. Roy. Stat. Soc., Ser B, 38: 205-247 (1976).
  • Anderson, B. D. O., Moore, J.B., “Optimal Filtering”, Prentice Hall, New Jersey, 36 (1979).