Analiza falkowa potencjałów czynnościowych jednostek ruchowych

Statystyczne opracowanie parametrów przebiegów czasowych potencjałów czynnościowych jednostek ruchowych zapewnia w wiekszości przypadków diagnozę, ale ze względu na niejednoznaczność w określaniu parametrów czasowych oraz ich liczbę, niezbędne jest duże doświadczenie neurologa interpretującego wyniki. W artykule zaprezentowano nowa metodę diagnozowania chorób nerwowo-mięśniowych, opartą na liniowej analizie dyskryminacyjnej skalogramów wyznaczonych za pomocą falek Symlet 4 z rzędu. Z otrzymanych w wyniku transformacji falkowej skalogramów wyekstrahowano sześć parametrów falkowych, które sprowadzono do pojedynczego parametru umożliwiającego dyskryminację przypadków prawidłowych, miogennych i neurogennych. Implementacja programowa proponowanej metody umożliwiła stworzenie komputerowego narzędzia diagnostycznego wspomagającego badanie elektromiograficzne o bardzo wysokim prawdopodobieństwie prawidłowej oceny stanu mięśnia.

This paper presents a new approach to the computer aided diagnostic systems for the needs of quantitative electromyography. Electromyography is a functional examination which plays a fundamental role in diagnostics of muscles and nerves diseases. The method allows for distinction between records of a healthy muscle and a changed one as well as for determination whether pathological changes are of primary myogenic or neurogenic character. Statistical processing of electromyography examination performed traditionally in the time domain ensures mostly correct classification of pathology without determination of a disease progression. However, because of an ambiguity of temporal parameters definitions a diagnosis can include a significant error which depends strongly on physician experience. So far, medical practice imposes, as a consensus, registration of at least 20 different motor unit action potentials belonging to one muscle. Them selected temporal parameters (presented in the paper) 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 also additional electromyography information a diagnosis is given. An inconvenience of this procedure in a clinical practice consist in high time-consumption arising, among others, from the necessity of determination of many parameters, usually between 4 and 7. Additionally, as it was mentioned above, an ambiguity in determination of basic temporal parameters can cause doubts during comparison of parameters found by the physician with standard ones determined in other research center which mostly uses equipment of older generation. A new approach we presented is based on the analysis of wavelet scalograms of the motor unit action potentials calculated on the basis of Symlet 4. The scalograms provide the vector consisting of five features describing the state of a muscle that can be reduced to one feature. In consequence, the healthy, myogenic and neurogenic cases can be successfully classified with the use of a linear method. A final effect of the first research stage was development of a definition for single point discriminant directly enabling a unique diagnosis to be made. An essential advantage of the suggested discriminant is a precise and algorithmically realized definition which enables an objective comparison of examination results obtained by physicians with different experience and working in different research centers. So, the definition fulfils a fundamental criterion for the parameter used for standard preparation. A suggestion of the standard for selected muscle is presented in the last part of the paper. The aim of next studies is a definition of standards which could allow a unique classification of myogenic, neurogenic and physiological cases for a large group of muscles based on a more numerous population. Currently, the authors are working on implementation of suggested procedures into diagnostic software that could be compatible with Viking IV D system developed by the Nicolet BioMedical Inc.