Aghazadeh Babak Seyed, Heris Hossein Khadivi
Department of Mechanical Engineering, Virginia Commonwealth University, Virginia, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:328-31. doi: 10.1109/IEMBS.2009.5333867.
In this paper an efficient fuzzy wavelet packet (WP) based feature extraction method and fuzzy logic based disorder assessment technique were used to investigate voice signals of patients suffering from unilateral vocal fold paralysis (UVFP). Mother wavelet function of tenth order Daubechies (d10) was employed to decompose signals in 5 levels. Next, WP coefficients were used to measure energy and Shannon entropy features at different spectral sub-bands. Consequently, using fuzzy c-means method, signals were clustered into 2 classes. The amount of fuzzy membership of pathological and normal signals in their corresponding clusters was considered as a measure to quantify the discrimination ability of features. A classification accuracy of 100 percent was achieved using an artificial neural network classifier. Finally, fuzzy c-means clustering method was used as a way of voice pathology assessment. Accordingly, fuzzy membership function based health index is proposed.
在本文中，一种基于高效模糊小波包（WP）的特征提取方法和基于模糊逻辑的病症评估技术被用于研究单侧声带麻痹（UVFP）患者的语音信号。采用十阶Daubechies（d10）母小波函数对信号进行5层分解。接下来，利用小波包系数测量不同频谱子带处的能量和香农熵特征。随后，使用模糊c均值方法将信号聚类为两类。将病理信号和正常信号在其相应聚类中的模糊隶属度作为量化特征判别能力的一种度量。使用人工神经网络分类器实现了100%的分类准确率。最后，将模糊c均值聚类方法用作语音病理评估的一种方式。据此，提出了基于模糊隶属函数的健康指数。
