- Tytuł:
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Analiza wielorozdzielcza i sieć SVM w zastosowaniu do klasyfikacji potencjałów czynnościowych
Multiresolution analysis and Support Vector Machine for motor unit classification - Autorzy:
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Dobrowolski, A. P.
Wierzbowski, M.
Tomczykiewicz, K. - Powiązania:
- https://bibliotekanauki.pl/articles/209509.pdf
- Data publikacji:
- 2009
- Wydawca:
- Wojskowa Akademia Techniczna im. Jarosława Dąbrowskiego
- Tematy:
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elektromiografia ilościowa (QEMG)
potencjał czynnościowy jednostki ruchowej (PJR)
analiza wielorozdzielcza
aproksymacja falkowa
skalogram
sieć SVM
diagnostyka medyczna
quantitative electromyography (QEMG)
motor unit action potential (MUAP)
multiresolution analysis
wavelet approximation
scalogram
support vector machine (SVM)
medical diagnosis - Opis:
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W artykule przedstawiono nową metodę diagnozowania chorób nerwowo-mięśniowych opartą na analizie skalogramów wyznaczonych za pomocą falek Symlet 4. Z otrzymanych skalogramów wyekstrahowano 5 cech, które po analizie w sieciach SVM sprowadzono do pojedynczego parametru klasyfikującego analizowane przypadki do grupy miogennej, neurogennej i prawidłowej. Implementacja programowa metody stworzyła narzędzie diagnostyczne wspomagające badanie EMG o bardzo wysokim prawdopodobieństwie prawidłowej oceny stanu mięśnia (błąd całkowity wyniósł 0,66% - dwie błędne klasyfikacje na 300 badanych pacjentów).
The paper presents a new approach to the computer aided diagnostic systems for the needs of quantitative electromyography. The approach is based on the analysis of wavelet scalograms of the motor unit action potentials calculated on the basis of 4th order Symlet wavelet. The scalograms provide the vector consisting of five features describing the state of a muscle. The vectors serve to carry out a classification of pathology by using Support Vector Machine method. The QEMG examination consists of the insertion of a needle electrode into a muscle and a registration of muscle potentials during low effort. Registered potentials are called motor unit action potentials (MUAPs). A diagnosis is usually preceded by a statistical analysis of a MUAP shape. An inconvenience of this procedure in a clinical practice is caused by high time- consumption arising, among others, from the necessity of determination of many parameters, usually between 4 and 7. Additionally, 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 centre, which mostly uses equipment of older generation. Measurement results on diagnostic method deprived of above - mentioned disadvantages are described in the paper. The aim of our work was a development of new methods for transformation of action potential signals observed in EMG records for healthy muscles and changed ones. The multiresolution decomposition method was devoted to determination of a vector of characteristic features of signals corresponding to analyzed categories. Then, this vector was used for effective recognition of these categories using linear Support Vector Machine technique. The final effect of research is development of a definition for numerical classificator directly enabling a unique diagnosis to be made. An essential advantage of the suggested classificator 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 centres. The presented diagnostic method ensures significantly better distinction between pathological and healthy cases as compared to methods using traditional parameters defined in time and frequency domains. Sensitivity of the wavelet method, for 100% specificity, amounts to 100% for myogenic and to 97% for neurogenic pathological states. - Źródło:
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Biuletyn Wojskowej Akademii Technicznej; 2009, 58, 3; 275-302
1234-5865 - Pojawia się w:
- Biuletyn Wojskowej Akademii Technicznej
- Dostawca treści:
- Biblioteka Nauki
Artykuł