Soumaya Zayrit, Taoufiq Belhoussine Drissi, Benayad Nsiri, Achraf Benba, Ammoumou Abdelkrim
Laboratory Industrial Engineering, Information Processing and Logistics (GITIL), Faculty of Science Ain Chok. University Hassan II - Casablanca, Morocco.
Laboratory Research Center STIS, M2CS, Higher School of Technical Education of Rabat (ENSET), Mohammed V University in Rabat, Morocco.
J Med Signals Sens. 2020 Feb 6;10(1):60-66. doi: 10.4103/jmss.JMSS_61_18. eCollection 2020 Jan-Mar.
The vibrations of hands and arms are the main symptoms of Parkinson's ailment. Nevertheless, the affection of the vocal cords leads to troubles and defects in the speech, which is another accurate symptom of the disease. This article presents a diagnostic model of Parkinson's disease (PD) and proposes the time-frequency transform (wavelet WT) and Mel-frequency cepstral coefficients (MFCC) treatment for this disease. The proposed treatment is centered on the vocal signal transformation by a method based on the WT and to extract the coefficients of the MFCC and eventually the categorization of the sick and healthy patients by the use of the classifier K-nearest neighbor (KNN). The analysis used in this article uses a database that contains 18 healthy patients and twenty patients. The Daubechies mother WT is used in treatments to compress the vocal signal and extract the MFCC cepstral coefficients. As far as, the diagnosis of Parkinson's ailment is concerned the KNN classifying performance gives 89% accuracy when applied to 52% of the database as training data, whereas when we increase this percentage from 52% to 73%, we reach 98.68% accuracy which is higher than using the support-vector machine classifier. The KNN is conclusive in the determination of the PD. Moreover, the higher the training data is, the more precise the results are.
手部和手臂的震颤是帕金森病的主要症状。然而，声带受到影响会导致言语方面的问题和缺陷，这是该疾病的另一个确切症状。本文提出了一种帕金森病（PD）的诊断模型，并针对该疾病提出了时频变换（小波变换WT）和梅尔频率倒谱系数（MFCC）的治疗方法。所提出的治疗方法以基于小波变换的方法对语音信号进行变换为核心，提取MFCC系数，并最终使用K近邻（KNN）分类器对患病和健康患者进行分类。本文所使用的分析采用了一个包含18名健康患者和20名患者的数据库。在治疗中使用Daubechies母小波变换来压缩语音信号并提取MFCC倒谱系数。就帕金森病的诊断而言，当将KNN分类性能应用于数据库的52%作为训练数据时，准确率为89%，而当我们将该百分比从52%提高到73%时，准确率达到98.68%，高于使用支持向量机分类器时的准确率。KNN在帕金森病的判定中具有决定性作用。此外，训练数据越高，结果就越精确。
