Surface electromyography quantification methods for evaluating muscle activity in dysphagia

• Main Authors: Suprijanto,, Noor, Azizah S., Mandasari, Miranti I, Hesty Susanti,
• Format: Article
• Language: English
• Published: Penerbit Universiti Kebangsaan Malaysia 2021
• Online Access: http://journalarticle.ukm.my/18321/1/5.pdf; http://journalarticle.ukm.my/18321/; https://www.ukm.my/jsm/malay_journals/jilid50bil12_2021/KandunganJilid50Bil12_2021.html

Quantitative evaluation of stroke patients with the risk of swallowing disorder or dysphagia is required to support diagnosis and further rehabilitation planning. Fluoroscopy X-ray imaging usually is used for swallowing diagnosis, though it gives radiation exposure to patients. Therefore, quantification of muscle coordination patterns involved in swallowing based on surface electromyography (sEMG) was introduced. However, an adequate quantification of sEMG for dysphagia diagnosis still lacks standardization. In this work, potential sEMG signal features, namely the contraction duration (DUR), the time to peak of maximum contraction (TTP), and the total RMS power (TP), were further investigated to evaluate the swallowing processes in healthy subjects and post-stroke patients. The experimental scheme instructed the participant, i.e. 20 healthy subjects and 20 patients, to swallow 3 mL of water in normal swallowing mode and swallow saliva in dry swallowing mode. The proposed signal processing procedure helps to establish the feature extraction of the three features mentioned earlier. For dysphagia assessment, with the support of our proposed signal processing procedure, DUR and TTP can be used together to improve diagnosis reliability. The characteristic of both features in healthy subjects was shorter than in post-stroke patients. Also, the TP feature is useful as additional information to evaluate the role of suprahyoid (SUP) and infrahyoid (INF) muscle groups which are very important in the swallowing process. These results are promising to provide a reliable set of features in the time domain for swallowing analysis. Notably, this can also be utilized as a feature for supporting the automatic classification of dysphagia diagnosis.