KEYWORDS: Muscles, Visualization, Signal detection, Electromyography, Electrodes, Detection and tracking algorithms, Signal to noise ratio, Algorithm development, Stomach, Signal processing
Dysphagia is defined as difficulty moving food from the mouth to the stomach. The confirmatory test for the diagnosis of this condition is videofluoroscopy (VFSS) being highly invasive since it exposes the patient to ionizing radiation. Surface electromyography (sEMG) records the electrical activity of the muscles in a non-invasive way, so it can offer an alternative to evaluate swallowing and can be applied in dysphagia. We analyzed sEMG signals from 5 patients who were simultaneously acquired with VFSS. Yogurt and water in volumes of 5, 10, and 20 ml, and 3 g of cracker were administered. Masseter, suprahyoid, and infrahyoid muscle groups were studied bilaterally. An algorithm for automatic detection of the onset and offset was developed. The degree of overlap between the automatic segmentation and visual segmentation was calculated for each muscle group. The average activation time of these muscle groups was compared and plotted concerning the bolus pass through the reference points calculated by VFSS: mandibular line (LM), vallecula (V), and cricopharyngeal muscle (part of upper esophageal sphincter). It was found a higher degree of overlap for the larger volumes (20 ml) and in the following order: masseter, suprahyoid, and infrahyoid. The results show a previous activation in most of the muscle groups before LM and a deactivation when the food passes through the cricopharyngeal sphincter (EC). This study showed that the implemented algorithm served to know the time of muscle activity and the sequence of muscle groups involved in swallowing by using sEMG validated with VFSS.
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