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Abstract ▼ AI-Helper

This paper describes an informatlon compression of electrically evoked myoelectric signal, M-wave. This wave shows a direct response m lato-response of nerve conductlQn study and has a characteristic with finite time support. M-wave is a useful factor for investing neurodi~ease and is often desirabl...

This paper describes an informatlon compression of electrically evoked myoelectric signal, M-wave. This wave shows a direct response m lato-response of nerve conductlQn study and has a characteristic with finite time support. M-wave is a useful factor for investing neurodi~ease and is often desirable to have a compact description of its shape and time evolution. The aim of this paper is to show that the AR modeling IS a effective method for compressing an information of M-wave. First, AR model parameters of real M-wave are estimated. And then. they are verified by approximatmg a M-wave using estimated AR parameters and by comparing to other melhod, Hermite tlansform[4]. To concretely evaluate the proposed method, the NMSE(normalized mean square error) of approximation curves are compared. As a result, AR modeling is effective for M-wave assessment because of its capability for the information compression.

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