Department of Electronics and Communication Engineering, QIS Institute of Technology, Ongole 523 272, Andhra Pradesh, India.
Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India.
Comput Intell Neurosci. 2022 May 31;2022:7223197. doi: 10.1155/2022/7223197. eCollection 2022.
Parkinson's disease (PD) is a neurodegenerative illness that progresses and is long-lasting. It becomes more difficult to talk, write, walk, and do other basic functions when the brain's dopamine-generating neurons are injured or killed. There is a gradual rise in the intensity of these symptoms over time. Using Parkinson's Telemonitoring Voice Data Set from UCI and deep neural networks, we provide a strategy for predicting the severity of Parkinson's disease in this research. An unprocessed speech recording contains a slew of unintelligible data that makes correct diagnosis difficult. Therefore, the raw signal data must be preprocessed using the signal error drop standardization while the features can be grouped by using the wavelet cleft fuzzy algorithm. Then the abnormal features can be selected by using the firming bacteria foraging algorithm for feature size decomposition process. Then classification was made using the deep brooke inception net classifier. The performances of the classifier are compared where the simulation results show that the proposed strategy accuracy in detecting severity of the Parkinson's disease is better than other conventional methods. The proposed DBIN model achieved better accuracy compared to other existing techniques. It is also found that the classification based on extracted voice abnormality data achieves better accuracy (99.8%) over PD prediction; hence it can be concluded as a better metric for severity prediction.
帕金森病（PD）是一种进行性和长期性的神经退行性疾病。当大脑中产生多巴胺的神经元受损或死亡时，说话、写作、行走和进行其他基本功能会变得更加困难。这些症状的强度会随着时间的推移逐渐加剧。在这项研究中，我们使用 UCI 的帕金森氏症远程监测语音数据集和深度神经网络，提供了一种预测帕金森病严重程度的策略。未经处理的语音记录包含大量难以理解的数据，使得正确诊断变得困难。因此，在使用小波裂缝模糊算法对特征进行分组的同时，必须使用信号误差下降标准化对原始信号数据进行预处理。然后，使用坚固细菌觅食算法对特征大小分解过程进行异常特征选择。然后使用深度布鲁克 inception 网络分类器进行分类。比较了分类器的性能，模拟结果表明，所提出的策略在检测帕金森病严重程度方面的准确性优于其他传统方法。与其他现有技术相比，所提出的 DBIN 模型具有更高的准确性。还发现，基于提取的语音异常数据的分类在 PD 预测方面实现了更高的准确性（99.8%）；因此，可以得出结论，它是一种更好的严重程度预测指标。
