Non-invasive ambulatory monitoring of complex sEMG patterns and its potential application in the detection of vocal dysfunctions

Abstract

Voice disorders are non-trivial when it comes to their early detection. Symptoms range from slight hoarseness to complete loss of voice, and may seriously impact personal and professional life. To date, we are still largely missing reliable data to help us better understand and screen voice pathologies. In this paper, we present an ambulatory voice monitoring system using surface electromyography (sEMG) and a robust algorithm for pattern recognition of vocal gestures. The system, which can process up to four sEMG channels simultaneously, also can store large amounts of data (up to 13 hours of continuous use) and in the future will be used to analyze on-the-fly various patterns of sEMG activation in the search for maladaptive laryngeal activity that may lead to voice disorders. In the preliminary results presented here, our pattern recognition algorithm (Hierarchical GUSSS) detected six different sEMG patterns of activation, and it achieved 90% accuracy.