Application of support vector machines and autoregressive moving average models in electromyography signal classification.

Author(s)

Mateus Ymanaka Barretto

• Document type: Master's Dissertation
• Press: São Paulo.
• Institution: Universidade de São Paulo (USP). Escola Politécnica (EP/BC)
• Defense date: 2007-12-10

Examining board members

Cinthia Itiki; Andre Fabio Kohn; Mauro Roberto Ushizima

• Advisor: Cinthia Itiki

Abstract

The diagnosis of neuromuscular diseases is attained by the combined use of several tools. Among these tools, clinical electromyography provides key information to the diagnosis. In the literature, the application of some classifiers (linear discriminant and artificial neural networks) to a variety of electromyography parameters (number of phases, turns and zero crossings; median frequency, auto-regressive coefficients) has provided promising results. Nevertheless, the need of a large number of auto-regressive coefficients has guided this Master\'s thesis to the use of a smaller number of auto-regressive moving-average coefficients. The classification task (into normal, neuropathic or myopathic) was achieved by support vector machines, a type of artificial neural network recently proposed. This work\'s objective was to study if low-order auto-regressive moving-average (ARMA) models can or cannot be used to substitute high-order auto-regressive models, in combination with support vector machines, for diagnostic purposes. Results point that support vector machines have better performance than Fisher linear discriminants. They also show that ARMA(1,11) and ARMA(1,12) models provide high classification rates (81.5%). These values are close to the maximum obtained by using 39 auto-regressive coefficients. So, we recommend the use of support vector machines and ARMA(1,11) or ARMA(1,12) to the classification of 800ms needle electromyography signals acquired at 25kHz. (AU)