Ahmed S. Almasoud1, Taiseer Abdalla Elfadil Eisa2, Fahd N. Al-Wesabi3,4, Abubakar Elsafi5, Mesfer Al Duhayyim6, Ishfaq Yaseen7, Manar Ahmed Hamza7,*, Abdelwahed Motwakel7
CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 871-886, 2022, DOI:10.32604/cmc.2022.024596
Abstract Early detection of Parkinson's Disease (PD) using the PD patients’ voice changes would avoid the intervention before the identification of physical symptoms. Various machine learning algorithms were developed to detect PD detection. Nevertheless, these ML methods are lack in generalization and reduced classification performance due to subject overlap. To overcome these issues, this proposed work apply graph long short term memory (GLSTM) model to classify the dynamic features of the PD patient speech signal. The proposed classification model has been further improved by implementing the recurrent neural network (RNN) in batch normalization layer of GLSTM and optimized with adaptive moment… More >
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