PDF(Pubmed)
Abstract：
Parkinson\'s disease (PD) is the second most prevalent neurodegenerative disorder in the world after Alzheimer\'s disease. Early diagnosing PD is challenging as it evolved slowly, and its symptoms eventuate gradually. Recent studies have demonstrated that changes in speech may be utilized as an excellent biomarker for the early diagnosis of PD. In this study, we have proposed a Chirplet transform (CT) based novel approach for diagnosing PD using speech signals. We employed CT to get the time-frequency matrix (TFM) of each speech recording, and we extracted time-frequency based entropy (TFE) features from the TFM. The statistical analysis demonstrates that the TFE features reflect the changes in speech that occurs in the speech due to PD, hence can be used for classifying the PD and healthy control (HC) individuals. The effectiveness of the proposed framework is validated using the vowels and words from the PC-GITA database. The genetic algorithm is utilized to select the optimum features subset, while a support vector machine (SVM), decision tree (DT), K-Nearest Neighbor (KNN), and Naïve Bayes (NB) classifiers are employed for classification. The TFE features outperform the breathiness and Mel frequency cepstral coefficients (MFCC) features. The SVM classifier is most effective compared to other machine-learning classifiers. The highest classification accuracy rates of 98% and 99% are achieved using the vowel /a/ and word /atleta/, respectively. The results reveal that the proposed CT-based entropy features effectively diagnose PD using the speech of a person.
摘要：
帕金森病(Parkinson’sdisease,PD)是世界上仅次于阿尔茨海默病的第二大神经退行性疾病。早期诊断PD具有挑战性，因为它进化缓慢，其症状逐渐消失。最近的研究表明，语音变化可用作PD早期诊断的出色生物标志物。在这项研究中,我们提出了一种基于Chirplet变换（CT）的新方法，用于使用语音信号诊断PD。我们使用CT来获得每个语音记录的时频矩阵（TFM），我们从TFM中提取了基于时频的熵(TFE)特征。统计分析表明，TFE特征反映了由于PD而在语音中发生的语音变化，因此可用于对PD和健康对照（HC）个体进行分类。使用PC-GITA数据库中的元音和单词验证了所提出框架的有效性。利用遗传算法选择最优特征子集,而支持向量机(SVM)，决策树(DT)，K-近邻(KNN),和朴素贝叶斯(NB)分类器用于分类。TFE特征优于呼吸和梅尔频率倒谱系数(MFCC)特征。与其他机器学习分类器相比，SVM分类器最有效。使用元音/a/和单词/atleta/可以达到98％和99％的最高分类准确率，分别。结果表明，提出的基于CT的熵特征使用人的语音有效地诊断PD。
