Automated Detection of Parkinson's Disease Based on Multiple Types of Sustained Phonations Using Linear Discriminant Analysis and Genetically Optimized Neural Network

• Published in: IEEE journal of translational engineering in health and medicine Vol. 7; pp. 1 - 10
• Main Authors: Ali, Liaqat, Zhu, Ce, Zhang, Zhonghao, Liu, Yipeng
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial intelligence; Automation; Classification; Correlation coefficients; deep neural network; Diagnostic systems; Dimensionality reduction; Discriminant analysis; Diseases; Feature extraction; genetic algorithm; Genetic algorithms; Hybrid systems; hyper-parameter optimization; linear discriminant analysis; Machine learning; Neural networks; Optimization; Parkinson's disease; Support vector machines; Testing; Training; Voice; Dimensionality reduction; genetic algorithm; hyper-parameter optimization; linear discriminant analysis; Parkinson’s disease; deep neural network
• ISSN: 2168-2372; 2168-2372
• DOI: 10.1109/JTEHM.2019.2940900

Objective: 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. Methods: 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. Results: 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. Conclusion: 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.