Panek D, Skalski A, Zielinski T, Deliyski D D
Annu Int Conf IEEE Eng Med Biol Soc. 2015 Aug;2015:735-8. doi: 10.1109/EMBC.2015.7318467.
This work presents a method for automatical and objective classification of patients with healthy and pathological vocal fold vibration impairments using High-Speed Videoendoscopy of the larynx. We used an image segmentation and extraction of a novel set of numerical parameters describing the spatio-temporal dynamics of vocal folds to classification according to the normal and pathological cases and achieved 73,3% cross-validation classification accuracy. This approach is promising to develop an automatic diagnosis tool of voice disorders.
这项工作提出了一种利用喉部高速视频内窥镜对声带振动健康和病理性损伤患者进行自动、客观分类的方法。我们使用了一种图像分割方法，并提取了一组描述声带时空动态的新数值参数，用于根据正常和病理情况进行分类，交叉验证分类准确率达到了73.3%。这种方法有望开发出一种语音障碍自动诊断工具。
