Objectives: Liuzijue Qigong (LQG), a kind of traditional Chinese health exercise (TCHE), is not only widely used to strengthen physical fitness and maintain psychological well-being in the elderly but has also been utilized to help improve respiratory function. As respiratory support is an important driving force for speech production, it is logical to postulate that the LQG training method with 6 monosyllabic speech sounds, xū, hē, hū, sī, chuī, and xī, can help individuals (1) experience a relaxing and natural state of speech production, (2) eliminate voice symptoms, and (3) improve their overall body function and mood. In the current study, we hypothesized that the LQG method with these 6 sounds can be effective in improving vocal function in subjects with unilateral vocal fold paralysis (UVFP) in comparison with a conventional voice therapy method.
Methods: A total of 48 patients with UVFP who met the inclusion criteria were randomly divided into 2 groups. Twenty-four subjects in the experimental group were trained with LQG, and those in the control group received conventional voice training (abdominal breathing and yawn-sign exercises) for a total of 4 sessions, twice a week. Patients in both groups were assessed with acoustic tests, the GRBAS scale, the Voice Handicap Index (VHI-10), and the Hospital Anxiety and Depression Scale (HADS) pre- and posttreatment. Statistical analysis was conducted using nonparametric tests and t tests.
Results: There existed significant changes in maximum phonation time (MPT), jitter, shimmer, normalized noise energy (NNE), GRBAS scores, VHI-10 scores, and grade of A in HADS scores pre- and posttreatment in both the experimental group and the control group ( P < .004). However, no significant changes were seen posttreatment between the 2 groups ( P > .05).
Conclusions: LQG could help improve vocal function in UVFP patients as our preliminary data showed no significant differences between LQG and conventional voice therapy methods.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6542000 | PMC |
http://dx.doi.org/10.1177/0003489419837265 | DOI Listing |
Light Sci Appl
January 2025
Spin-Optics laboratory, St. Petersburg State University, St. Petersburg, 198504, Russia.
We introduce a novel neuromorphic network architecture based on a lattice of exciton-polariton condensates, intricately interconnected and energized through nonresonant optical pumping. The network employs a binary framework, where each neuron, facilitated by the spatial coherence of pairwise coupled condensates, performs binary operations. This coherence, emerging from the ballistic propagation of polaritons, ensures efficient, network-wide communication.
View Article and Find Full Text PDFHead Neck
January 2025
THANC (Thyroid, Head and Neck Cancer) Foundation, New York, New York, USA.
Tracheoesophageal puncture (TEP) with voice prosthesis (VP) placement is commonly used to restore voice in laryngectomy patients. The conventional procedure utilizes a rigid esophagoscope to open and visualize the pharyngeal inlet. However, this approach is challenging in patients with postradiation changes, reduced neck extension, or trismus.
View Article and Find Full Text PDFProc ACM Symp User Interface Softw Tech
October 2024
Department of Computer Science, Stony Brook University New York, USA.
While gesture typing is widely adopted on touchscreen keyboards, its support for low vision users is limited. We have designed and implemented two keyboard prototypes, layout-magnified and key-magnified keyboards, to enable gesture typing for people with low vision. Both keyboards facilitate uninterrupted access to all keys while the screen magnifier is active, allowing people with low vision to input text with one continuous stroke.
View Article and Find Full Text PDFMachine learning approaches including deep learning models have shown promising performance in the automatic detection of Parkinson's disease. These approaches rely on different types of data with voice recordings being the most used due to the convenient and non-invasive nature of data acquisition. Our group has successfully developed a novel approach that uses convolutional neural network with transfer learning to analyze spectrogram images of the sustained vowel /a/ to identify people with Parkinson's disease.
View Article and Find Full Text PDFJ Voice
January 2025
Division of Phoniatrics and Pediatric Audiology at the Department of Otorhinolaryngology, Head and Neck Surgery, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91054 Erlangen, Germany.
Objectives: This study investigates the use of sustained phonations recorded during high-speed videoendoscopy (HSV) for machine learning-based assessment of hoarseness severity (H). The performance of this approach is compared with conventional recordings obtained during voice therapy to evaluate key differences and limitations of HSV-derived acoustic recordings.
Methods: A database of 617 voice recordings with a duration of 250 ms was gathered during HSV examination (HS).
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!