Using Muscle Synergy to Evaluate the Neck Muscular Activities during Normal Swallowing

A robust HD-sEMG sensor suitable for convenient acquisition of muscle activity in clinical post-stroke dysphagia

Objective.A flexible high-density surface electromyography (HD-sEMG) sensor combined with an adaptive algorithm was used to collect and analyze the swallowing activities of patients with Post-stroke dysphagia.Approach.The electrode frame, modified electrode, and bonded substrate of the sensor were fabricated using a flexible printed circuit process, controlled drop coating, and molding, respectively. The adaptation algorithm was achieved by using Laplace and Teager-Kaiser energy operators to extract active segments, a cross-correlation coefficient matrix (CCCM) to evaluate synergy, and multi-frame real-time dynamic root mean square (RMS) to visualize spatiotemporal information to screen lesions and level of dysphagia. Finally, support vector machines (SVM) were adopted to explore the classification accuracy of sex, age, and lesion location with small sample sizes.Main results.The sensor not only has a basic low contact impedance (0.262 kΩ) and high signal-to-noise ratio (37.284 ± 1.088 dB) but also achieves other characteristics suitable for clinical applications, such as flexibility (747.67 kPa) and durability (1000 times) balance, simple operation (including initial, repeated, and replacement use), and low cost ($ 15.2). The three conclusions are as follows. CCCM can be used as a criterion for judging the unbalanced muscle region of the patient's neck and can accurately locate unbalanced muscles. The RMS cloud map provides the time consumption, swallowing times, and unbalanced areas. When the lesion location involves the left and right hemispheres simultaneously, it can be used as an evidence of relatively severely unbalanced areas. The classification accuracy of SVM in terms of sex, age, and lesion location was as high as 100%.Significance.The HD-sEMG sensor in this study and the adaptation algorithm will contribute to the establishment of a larger-scale database in the future to establish more detailed and accurate quantitative standards, which will be the basis for developing more optimized screening mechanisms and rehabilitation assessment methods.

Quantification of the Swallowing Mechanism Through Muscle Synergy Analysis

Decreased swallowing function increases the risk of choking and aspiration pneumonia. Videofluoroscopy and computed tomography allow for detailed observation of the swallowing movements but have radiation risks. Therefore, we developed a method using surface electromyography (sEMG) to noninvasively assess swallowing function without radiation exposure. A 44-channel flexible sEMG sensor was used to measure the sEMG signals of the hyoid muscles during swallowing in 14 healthy young adult and 14 elderly subjects. Muscle synergy analysis was performed to extract the muscle synergies from the sEMG signals, and the three synergies were extracted from the hyoid muscle activities during the swallowing experiments. The experimental results showed that the three synergies represent the oral, early pharyngeal, and late pharyngeal swallowing phases and that swallowing strength is tuned by the strength of the muscle activities, whereas swallowing volume is controlled by adjusting muscle activation timing. In addition, the timing of the swallowing reflex is slower in elderly individuals. The results confirm that the proposed approach successfully quantifies swallowing function from sEMG signals, mapping the signals to the swallowing phases.

Towards Evaluating Pitch-Related Phonation Function in Speech Communication Using High-Density Surface Electromyography

Pitch, as a sensation of the sound frequency, is a crucial attribute toward constructing a natural voice for communication. Producing intelligible sounds with normal pitches depend on substantive interdependencies among facial and neck muscles. Clarifying the interrelations between the pitches and the corresponding muscular activities would be helpful for evaluating the pitch-related phonating functions, which would play a significant role both in training pronunciation and in assessing dysphonia. In this study, the speech signals and the high-density surface electromyography (HD sEMG) signals were synchronously acquired when phonating [a:], [i:], and [ә:] vowels with increasing pitches, respectively. The HD sEMG energy maps were constructed based on the root mean square values to visualize spatiotemporal characteristics of facial and neck muscle activities. Normalized median frequency (nMF) and root-mean square (nRMS) were correspondingly extracted from the speech and sEMG recordings to quantitatively investigate the correlations between sound frequencies and myoelectric characteristics. The results showed that the frame-wise energy maps built from sEMG recordings presented that the muscle contraction strength increased monotonously across pitch-rising, with left-right symmetrical distribution for the face/neck. Furthermore, the nRMS increased at a similar rate to the nMF when there were rising pitches, and the two parameters had a significant correlation across different vowel tasks [(a:) (0.88 ± 0.04), (i:) (0.89 ± 0.04), and (ә:) (0.87 ± 0.05)]. These findings suggested the possibility of utilizing muscle contraction patterns as a reference for evaluating pitch-related phonation functions. The proposed method could open a new window for developing a clinical approach for assessing the muscular functions of dysphonia.

Towards optimizing electrode configurations for silent speech recognition based on high-density surface electromyography

Objective. Silent speech recognition (SSR) based on surface electromyography (sEMG) is an attractive non-acoustic modality of human-machine interfaces that convert the neuromuscular electrophysiological signals into computer-readable textual messages. The speaking process involves complex neuromuscular activities spanning a large area over the facial and neck muscles, thus the locations of the sEMG electrodes considerably affected the performance of the SSR system. However, most of the previous studies used only a quite limited number of electrodes that were placed empirically without prior quantitative analysis, resulting in uncertainty and unreliability of the SSR outcomes. Approach. In this study, the technique of high-density sEMG was proposed to provide a full representation of the articulatory muscle activities so that the optimal electrode configuration for SSR could be systemically explored. A total of 120 closely spaced electrodes were placed on the facial and neck muscles to collect the high-density sEMG signals for classifying ten digits (0–9) silently spoken in both English and Chinese. The sequential forward selection algorithm was adopted to explore the optimal electrodes configurations. Main Results. The results showed that the classification accuracy increased rapidly and became saturated quickly when the number of selected electrodes increased from 1 to 120. Using only ten optimal electrodes could achieve a classification accuracy of 86% for English and 94% for Chinese, whereas as many as 40 non-optimized electrodes were required to obtain comparable accuracies. Also, the optimally selected electrodes seemed to be mostly distributed on the neck instead of the facial region, and more electrodes were required for English recognition to achieve the same accuracy. Significance. The findings of this study can provide useful guidelines about electrode placement for developing a clinically feasible SSR system and implementing a promising approach of human-machine interface, especially for patients with speaking difficulties.