[Neuromuscular electric stimulation therapy in otorhinolaryngology]

Analysis of the Improvement Effect of Combined Application of Oral Rehabilitation Training and Neuromuscular Electrical Stimulation on Pediatric Swallowing Disorders

Pediatric swallowing disorders are common yet often overlooked neuro-muscular system diseases that significantly impact the quality of life and development of affected children. This study aims to explore the effect of combined application of oral rehabilitation training and neuromuscular electrical stimulation on improving pediatric swallowing disorders. Children meeting the inclusion criteria for swallowing disorders were divided into control and experimental groups based on different intervention protocols. The experimental group received combined oral rehabilitation training and neuromuscular electrical stimulation, while the control group received only oral rehabilitation training. Results showed that the intervention was more effective in the experimental group, with shorter recovery time for normal swallowing function and improved nutritional status and quality of life. This study provides scientific evidence for clinical treatment of pediatric swallowing disorders. In conclusion, the combined application of oral rehabilitation training and neuromuscular electrical stimulation effectively improves pediatric swallowing disorders, with superior efficacy compared to single treatment methods. Further research is needed to elucidate the mechanism of action and optimize treatment protocols to enhance the therapeutic outcomes and prognosis of pediatric swallowing disorders.

Advanced Statistical Analysis of 3D Kinect Data: A Comparison of the Classification Methods

This paper focuses on the statistical analysis of mimetic muscle rehabilitation after head and neck surgery causing facial paresis in patients after head and neck surgery. Our work deals with an evaluation problem of mimetic muscle rehabilitation that is observed by a Kinect stereo-vision camera. After a specific brain surgery, patients are often affected by face palsy, and rehabilitation to renew mimetic muscle innervation takes several months. It is important to be able to observe the rehabilitation process in an objective way. The most commonly used House–Brackmann (HB) scale is based on the clinician’s subjective opinion. This paper compares different methods of supervised learning classification that should be independent of the clinician’s opinion. We compare a parametric model (based on logistic regression), non-parametric model (based on random forests), and neural networks. The classification problem that we have studied combines a limited dataset (it contains only 122 measurements of 93 patients) of complex observations (each measurement consists of a collection of time curves) with an ordinal response variable. To balance the frequencies of the considered classes in our data set, we reclassified the samples from HB4 to HB3 and HB5 to HB6—it means that only four HB grades are used for classification algorithm. The parametric statistical model was found to be the most suitable thanks to its stability, tractability, and reasonable performance in terms of both accuracy and precision.