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Objectives: Several studies have assessed adult vocal fold movement using transcutaneous laryngeal ultrasonography (TLUSG) during the perioperative period of thyroidectomy. However, the movement was not objectively quantified. This study aimed to provide a feasible and objective method for assessing vocal fold movement using TLUSG., Study Design: Feasibility study., Methods: TLUSG was performed during calm breathing and breath-holding in healthy adult volunteers. The morphology and anatomy of the larynx were observed and measured using an ultrasonic self-contained measurement function. At the end of the calm inspiratory and breath-holding phases, vocal fold angle, vocal fold length, distance from vocal process to the midline, distance from anterior vocal commissure to arytenoid cartilage, distance from false vocal fold to the midline, and distance from the anterior horn of thyroid cartilage to false vocal fold were measured. Data were analyzed using a t test (significance <0.05)., Results: The ultrasonic images were satisfactory in all 40 healthy adult volunteers (age 19-35 years; body mass index 18.55-23.93 kg/m 2 ; 20 men and 20 women). There were no significant differences in all laryngeal parameters between the left and right sides in both phases (P > 0.05). Moreover, all differences in laryngeal parameters between the end of the calm inspiratory phase and the breath-holding phase were statistically significant (P < 0.05), regardless of sex., Conclusion: The relevant positional parameters of the vocal fold, arytenoid cartilage, and false vocal fold and their differences before and after vocal fold movement in healthy adult volunteers can be obtained objectively using TLUSG., Competing Interests: Declaration of Competing Interest None to declare., (Copyright © 2023 The Voice Foundation. Published by Elsevier Inc. All rights reserved.)

This study was aimed at characterizing spontaneous electroencephalography (EEG) activity in Alzheimer's disease (AD) using a novel approach named weighted visibility graph (WVG). More than 10 minutes of spontaneous EEG were recorded from 15 AD patients and 15 age-matched normal controls. Two graph metrics, clustering coefficient and average weighted degree, are extracted in different frequency bands for each EEG channel based on the WVG methodology. Furthermore, statistical analysis was performed in different bands and channels for both groups. It is demonstrated that AD patients are characterized with a significant increase of clustering coefficient and degree in theta band, which can be observed in most brain regions. Our results suggest that the WVG method can be are effective to distinguish different brain states (AD and normal) and may provide further insights into the underlying brain dynamics in AD.

In this paper, experimental neurophysiologic recording and statistical analysis are combined to investigate the nonlinear characteristic and the cognitive function of the brain. Fuzzy approximate entropy and fuzzy sample entropy are applied to characterize the model-based simulated series and electroencephalograph (EEG) series of Alzheimer's disease (AD). The effectiveness and advantages of these two kinds of fuzzy entropy are first verified through the simulated EEG series generated by the alpha rhythm model, including stronger relative consistency and robustness. Furthermore, in order to detect the abnormality of irregularity and chaotic behavior in the AD brain, the complexity features based on these two fuzzy entropies are extracted in the delta, theta, alpha, and beta bands. It is demonstrated that, due to the introduction of fuzzy set theory, the fuzzy entropies could better distinguish EEG signals of AD from that of the normal than the approximate entropy and sample entropy. Moreover, the entropy values of AD are significantly decreased in the alpha band, particularly in the temporal brain region, such as electrode T3 and T4. In addition, fuzzy sample entropy could achieve higher group differences in different brain regions and higher average classification accuracy of 88.1% by support vector machine classifier. The obtained results prove that fuzzy sample entropy may be a powerful tool to characterize the complexity abnormalities of AD, which could be helpful in further understanding of the disease.