PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Fourier analysis of motor unit action potentials

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Electromyography (EMG) is a functional examination that plays a fundamental role in the diagnosis of neuromuscular disorders. The method allows for distinction between records of a healthy muscle and a changed muscle as well as for determination of whether pathological changes are of primary myopathic or neuropathic character. The statistical processing of electromyographic signal examination performed in the time domain ensures mostly correct classification of pathology; however, because of an ambiguity of most temporal parameter definitions, a diagnosis can include a significant error that strongly depends on the neurologist's experience. Then, selected temporat parameters are determined for each run, and their mean values are calculated. In the final stage, these mean values are compared with a standard and, including additional clinical information, a diagnosis is given. An inconvenience of this procedure is high time consumption that arises from, among other things, the necessity of determination of many parameters. Additionally, an ambiguity in determination of basic temporal parameters can cause doubts when parameters found by the physician are compared with standard parameters determined in other research centers. In this paper, we present a definition for single-point spectral discriminant that directly enables a unique diagnosis to be made. An essential advantage of the suggested discriminant is a precise and algorithmically realized definition that enables an objective comparison of examination results obtained by physicians with different experiences or working in different research centers. Therefore, the definition fulfills a fundamental criterion for the parameter used for preparation of a standard. A suggestion of the standard for selected muscle based on a population of 70 healthy cases is presented in the Results section.
Rocznik
Strony
127--141
Opis fizyczny
Bibliogr. 15 poz., wykr.
Twórcy
autor
  • Military University of Technology, Faculty of Electronics, 2 Kaliskiego Str., 00-908 Warsaw, Poland, ADobrowolski@wat.edu.pl
Bibliografia
  • 1. E. Zalewska, I. Hausmanowa-Petrusewicz: Evaluation of MUAP shape irregularity - a new concept of quantification, IEEE TBME, vol. 42(6), 1995, pp. 616-620.
  • 2. E. Stalberg, S.D. Nandedkar, D.B. Sanders, B. Falck: Quantitative motor unit potential analysis, J. Clinical Neurophysiology, vol. 13(5), 1996, pp. 401-422.
  • 3. E. Zalewska, I. Hausmanowa-Petrusewicz: Effectiveness of motor unit potentials classification us-ing various parameters and indexes, J. Clinical Neurophysiology, vol. 111(8), 2000, pp. 1380-1387.
  • 4. S. Shahid, J. Walker, G.M. Lyons, C.A. Byrne, A.V. Nene: Application of higher order statistics techniques to EMG signals to characterize the motor unit action potential, IEEE TBME, vol. 52(7), 2005, pp. 1195-1209.
  • 5. I. Christodoulou, C.S. Pattichis: A new technique for the classification and decomposition of EMG signals, Proc. in IEEE Int. Conf. on Neural Networks, vol. 5, 1995, pp. 2303-2308.
  • 6. C.S. Pattichis, C.N. Schizas, L.T. Middleton: Neural Network Models in EMG Diagnosis, IEEE TBME, vol. 42(5), 1995, pp. 486-496.
  • 7. C.I. Christodoulou, C.S. Pattichis: Unsupervised pattern recognition for the classification of EMG signals, IEEE TBME, vol. 46(2), 1999, pp. 169-178.
  • 8. N.F. Giiler, S. Kocer: Classification of EMG signals using PCA and FFT, Journal of Medical Systems, vol. 29 (3), 2005, pp. 241-250.
  • 9. C.S. Pattichis, A.G. Elia: Autoregressive and cepstral analyses of motor unit action potentials, Medical Engineering & Physics, vol. 21, 1999, pp. 405-419.
  • 10. P. Wellig, G.S. Moschytz, T. Laubli: Decomposition of EMG signals using time-frequency fea-tures, Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 20(3), 1998, pp.1497-1500.
  • 11. C.S. Pallichis, M.S. Pattichis: Time-scale analysis of motor unit action potentials, IEEE TBME, vol. 46(11), 1999, pp. 1320-1329.
  • 12. I. Rodriguez-Carreno, A. Malanda-Trigueros, L. Gila-Useros, J. Navallas-Irujo, J. Rodriguez-Falces: Filter design for cancellation of baseline-fluctuation in needle EMG recordings, Computer methods and programs in biomedicine, vol. 81(1), 2006, pp. 79-93.
  • 13. E. Zalewska, I. Hausmanowa-Petrusewicz, E. Stalberg: Modeling studies on irregular motor unit potentials, J. Clinical Neurophysiology, vol. 115(3), 2004, pp. 543-556.
  • 14. Ch. Bischoff, E. Stalberg, B. Falck, K. Edebol Eeg-Olofsson: Reference values of motor unit action potentials obtained with multi-MUAP analysis, Muscle & Nerve, vol. 17(8), 1994, pp. 842-851.
  • 15. S.W. Smith: The scientist and engineer's guide to digital signal processing, California Technical Publishing, San Diego, 1999.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BWA0-0022-0028
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.