Premium
Time–frequency analysis of rhythmic masticatory muscle activity
Author(s) -
Farella Mauro,
Palla Sandro,
Gallo Luigi Maria
Publication year - 2009
Publication title -
muscle and nerve
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.025
H-Index - 145
eISSN - 1097-4598
pISSN - 0148-639X
DOI - 10.1002/mus.21262
Subject(s) - electromyography , receiver operating characteristic , rhythm , masticatory force , pattern recognition (psychology) , computer science , frequency analysis , signal (programming language) , artificial intelligence , speech recognition , medicine , physical medicine and rehabilitation , algorithm , orthodontics , machine learning , programming language
The aim of this study was to develop and validate under laboratory conditions an algorithm for a time–frequency analysis of rhythmic masticatory muscle activity (RMMA). The algorithm baseband demodulated the electromyographic (EMG) signal to provide a frequency versus time representation. Using appropriate thresholds for frequency and power parameters, it was possible to automatically assess the features of RMMA without examiner interaction. The algorithm was first tested using synthetic EMG signals and then using real EMG signals obtained from the masticatory muscles of 11 human subjects who underwent well‐defined rhythmic, static, and possible confounding oral tasks. The accuracy of detection was quantified by receiver operating characteristics (ROC) curves. Sensitivity and specificity values were ≥90% and ≥96%, respectively. The areas under the ROC curves were ≥95% (standard error ±0.1%). The proposed approach represents a promising tool to effectively investigate rhythmical contractions of the masticatory muscles. Muscle Nerve, 2009