Classification of Unilateral Vocal Fold Paralysis by Endoscopic Digital High-Speed Recordings and Inversion of a Biomechanical Model [Електронний ресурс] / Raphael Schwarz, Ulrich Hoppe, Maria Schuster и др. // IEEE Transactions on Biomedical Engineering [Електронний ресурс]. – 2006. – № 6. – Pp. 1099 – 1108
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Статистика використання: Завантажень: 2
Складова документа:
IEEE Transactions on Biomedical Engineering [Електронний ресурс] : вестник ин-та радиоинженеров. № 6. 53 / IEEE Engineering in medicine and Biology Group // IEEE Transactions on Biomedical Engineering. – USA, 2006
Анотація:
Hoarseness in unilateral vocal fold paralysis is mainly due to irregular vocal fold vibrations caused by asymmetries within the larynx physiology. By means of a digital high-speed
camera vocal fold oscillations can be observed in real-time. It is possible to extract the irregular vocal fold oscillations from the high-speed recordings using appropriate image processing techniques. An inversion procedure is developed which adjusts the parameters of a biomechanical model of the vocal folds to reproduce the irregular vocal fold oscillations. Within the inversion procedure a first parameter approximation is achieved through a knowledge-based algorithm. The final parameter optimization is performed using a genetic algorithm. The performance of the inversion procedure is evaluated using 430 synthetically generated data sets. The evaluation results comprise an error estimation of
the inversion procedure and show the reliability of the algorithm. The inversion procedure is applied to 15 healthy voice subject
camera vocal fold oscillations can be observed in real-time. It is possible to extract the irregular vocal fold oscillations from the high-speed recordings using appropriate image processing techniques. An inversion procedure is developed which adjusts the parameters of a biomechanical model of the vocal folds to reproduce the irregular vocal fold oscillations. Within the inversion procedure a first parameter approximation is achieved through a knowledge-based algorithm. The final parameter optimization is performed using a genetic algorithm. The performance of the inversion procedure is evaluated using 430 synthetically generated data sets. The evaluation results comprise an error estimation of
the inversion procedure and show the reliability of the algorithm. The inversion procedure is applied to 15 healthy voice subject