Ali Liaqat, Zhu Ce, Zhang Zhonghao, Liu Yipeng
School of Information and Communication EngineeringUniversity of Electronic Science and Technology of China (UESTC)Chengdu611731China.
IEEE J Transl Eng Health Med. 2019 Oct 7;7:2000410. doi: 10.1109/JTEHM.2019.2940900. eCollection 2019.
Parkinson's disease (PD) is a serious neurodegenerative disorder. It is reported that most of PD patients have voice impairments. But these voice impairments are not perceptible to common listeners. Therefore, different machine learning methods have been developed for automated PD detection. However, these methods either lack generalization and clinically significant classification performance or face the problem of subject overlap.
To overcome the problems discussed above, we attempt to develop a hybrid intelligent system that can automatically perform acoustic analysis of voice signals in order to detect PD. The proposed intelligent system uses linear discriminant analysis (LDA) for dimensionality reduction and genetic algorithm (GA) for hyperparameters optimization of neural network (NN) which is used as a predictive model. Moreover, to avoid subject overlap, we use leave one subject out (LOSO) validation.
The proposed method namely LDA-NN-GA is evaluated in numerical experiments on multiple types of sustained phonations data in terms of accuracy, sensitivity, specificity, and Matthew correlation coefficient. It achieves classification accuracy of 95% on training database and 100% on testing database using all the extracted features. However, as the dataset is imbalanced in terms of gender, thus, to obtain unbiased results, we eliminated the gender dependent features and obtained accuracy of 80% for training database and 82.14% for testing database, which seems to be more unbiased results.
Compared with the previous machine learning methods, the proposed LDA-NN-GA method shows better performance and lower complexity. Clinical Impact: The experimental results suggest that the proposed automated diagnostic system has the potential to classify PD patients from healthy subjects. Additionally, in future the proposed method can also be exploited for prodromal and differential diagnosis, which are considered challenging tasks.
帕金森病（PD）是一种严重的神经退行性疾病。据报道，大多数帕金森病患者存在语音障碍。但这些语音障碍普通听众难以察觉。因此，已开发出不同的机器学习方法用于帕金森病的自动检测。然而，这些方法要么缺乏泛化能力和具有临床意义的分类性能，要么面临受试者重叠的问题。
为克服上述问题，我们尝试开发一种混合智能系统，该系统可自动对语音信号进行声学分析以检测帕金森病。所提出的智能系统使用线性判别分析（LDA）进行降维，并使用遗传算法（GA）对用作预测模型的神经网络（NN）进行超参数优化。此外，为避免受试者重叠，我们采用留一受试者法（LOSO）验证。
所提出的LDA-NN-GA方法在多种类型的持续发声数据的数值实验中，根据准确率、灵敏度、特异性和马修相关系数进行了评估。使用所有提取的特征，该方法在训练数据库上的分类准确率达到95%，在测试数据库上达到100%。然而，由于数据集在性别方面不均衡，因此，为获得无偏结果，我们消除了与性别相关的特征，训练数据库的准确率为80%，测试数据库的准确率为82.14%，这似乎是更无偏的结果。
与先前的机器学习方法相比，所提出的LDA-NN-GA方法表现出更好的性能和更低的复杂度。临床影响：实验结果表明，所提出的自动诊断系统有潜力将帕金森病患者与健康受试者区分开来。此外，未来所提出的方法还可用于前驱期和鉴别诊断，这被认为是具有挑战性的任务。
