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

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.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.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%.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.