Azadi Hamid, Akbarzadeh-T Mohammad-R, Shoeibi Ali, Kobravi Hamid Reza
Department of Electrical Engineering, Biomedical Engineering Group, Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University of Mashhad, Mashhad, Iran.
Department of Neurology. School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Adv Biomed Res. 2021 Dec 25;10:54. doi: 10.4103/abr.abr_254_21. eCollection 2021.
Parkinson's disease (PD) is a neurological disorder caused by decreasing dopamine in the brain. Speech is one of the first functions that are disrupted. Accordingly, speech features are a promising indicator in PD diagnosis for telemedicine applications. The purpose of this study is to investigate the impact of Parkinson's disease on a minimal set of Jitter and Shimmer voice indicators and studying the difference between male and female speech features in noisy/noiseless environments.
Our data includes 47 samples from nursing homes and neurology clinics, with 23 patients and 24 healthy individuals. The optimal feature for each category is studied separately for the men's and women's samples. The focus here is on the phonation in which the vowel/a/is expressed by the participants. The main features, including Jitter and Shimmer perturbations, are extracted. To find an optimal pair under both noisy and noiseless circumstance, we use the Relief feature selection strategy.
This research shows that the Jitter feature for men and women with Parkinson's is 21 and 33.4, respectively. While the Shimmer feature is 0.1 and 0.06. In addition, by using these two features alone, we reach a correct diagnosis rate of 79% and 81% for noisy and noiseless states, respectively.
The PD effects on the speech features can be accurately identified. Evaluating the extracted features suggests that the absolute value of the selected feature in men with PD is higher than for healthy ones. Whereas, in the case of women, this is the opposite.
帕金森病(PD)是一种由大脑中多巴胺减少引起的神经紊乱疾病。言语是最早受到干扰的功能之一。因此,言语特征是远程医疗应用中帕金森病诊断的一个有前景的指标。本研究的目的是调查帕金森病对一组最少的抖动和闪烁语音指标的影响,并研究在有噪声/无噪声环境中男性和女性言语特征的差异。
我们的数据包括来自养老院和神经科诊所的47个样本,其中有23名患者和24名健康个体。分别针对男性和女性样本研究每个类别的最佳特征。这里关注的是参与者发出元音/a/时的发声情况。提取包括抖动和闪烁扰动在内的主要特征。为了在有噪声和无噪声两种情况下找到最佳特征对,我们使用Relief特征选择策略。
本研究表明,患有帕金森病的男性和女性的抖动特征分别为21和33.4。而闪烁特征分别为0.1和0.06。此外,仅使用这两个特征,我们在有噪声和无噪声状态下的正确诊断率分别达到79%和81%。
可以准确识别帕金森病对言语特征的影响。对提取特征的评估表明,帕金森病男性患者所选特征的绝对值高于健康男性。而对于女性来说,情况则相反。