Detection of Simulated Vocal Dysfunctions Using Complex sEMG Patterns

• Published in: IEEE journal of biomedical and health informatics Vol. 20; no. 3; pp. 787 - 801
• Main Authors: Smith, Nicholas R., Rivera, Luis A., Dietrich, Maria, Chi-Ren Shyu, Page, Matthew P., DeSouza, Guilherme N.
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.05.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Activation; Adolescent; Adult; Algorithms; Computer simulation; Disorders; Electromyography - methods; Feature extraction; Female; Humans; Informatics; Male; Middle Aged; Monitoring; Muscles; Neck; Necks; Pattern recognition; Pattern Recognition, Automated - methods; Signal Processing, Computer-Assisted; Speech; Speech disorders; Voice; Voice Disorders - diagnosis; Young Adult; Source Separation; Electromyography; Biomedical telemetry; Pattern recognition; Medical diagnosis; Biomedical monitoring
• ISSN: 2168-2194; 2168-2208; 2168-2208
• DOI: 10.1109/JBHI.2015.2490087

Symptoms of voice disorder may range from slight hoarseness to complete loss of voice; from modest vocal effort to uncomfortable neck pain. But even minor symptoms may still impact personal and especially professional lives. While early detection and diagnosis can ameliorate that effect, to date, we are still largely missing reliable and valid data to help us better screen for voice disorders. In our previous study, we started to address this gap in research by introducing an ambulatory voice monitoring system using surface electromyography (sEMG) and a robust algorithm (HiGUSSS) for pattern recognition of vocal gestures. Here, we expand on that work by further analyzing a larger set of simulated vocal dysfunctions. Our goal is to demonstrate that such a system has the potential to recognize and detect real vocal dysfunctions from multiple individuals with high accuracy under both intra and intersubject conditions. The proposed system relies on four sEMG channels to simultaneously process various patterns of sEMG activation in the search for maladaptive laryngeal activity that may lead to voice disorders. In the results presented here, our pattern recognition algorithm detected from two to ten different classes of sEMG patterns of muscle activation with an accuracy as high as 99%, depending on the subject and the testing conditions.