Individualización de patrones neuromusculares de señales sEMG a través de la transformada Hilbert Huang

Abstract

This project investigates an emerging methodology to individualize neuromuscular patterns highlighting as a fundamental and differential purpose, a case study of individuals diagnosed with carpal tunnel syndrome. The proposed method performs time-frequency analysis of electro-miographic surface signals (sEMG) using the Hilbert-Huang transform (HHT), a valid procedure for time series analysis of non-linear and non-stationary topology, which extracts underlying information from processes acting locally and adaptively to extract functions intrinsically (IMF) through empirical mode decomposition (EMD). Given the inexistence of a sufficiently broad and valid precedent, initially it is experimented to analyze in a deep way the processes that underlie the HHT, first from its fundamental principle: the empirical decomposition of modes, and then under the optics of the behavior that this one takes before contaminated phenomena, finding relevant results and setting an own and useful antecedent for the development of investigations that apply the HHT or specifically the EMD. Subsequently, sEMG signals of muscles of the upper limb of a population that includes healthy subjects affected by carpal tunnel syndrome (CTS) are studied. The signals are broken down to establish a multi-scale base that allows the extraction of knowledge from multiple approaches, highlighting the spectral representation and those evoked to the forms of frequency and power models analyzed through statistics. Thanks to these it is possible to establish a novel and efficient methodology for the individualization of muscular patterns, emerging as a way for the diagnosis of neuropathies, and with general extension to the study of non-linear and non-stationary models. This project was developed under the framework of the research structure DIGITI of the Universidad Distrital Francisco José de Caldas.