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 estimation (ADAM) on network hidden layer. To consider the importance of feature engineering, this proposed system use Linear Discriminant analysis (LDA) for dimensionality reduction and Sparse Auto-Encoder (SAE) for extracting the dynamic speech features. Based on the computation of energy content transited from unvoiced to voice (onset) and voice to voiceless (offset), dynamic features are measured. The PD datasets is evaluated under 10 fold cross validation without sample overlap. The proposed smart PD detection method called RNN-GLSTM-ADAM is numerically experimented with persistent phonations in terms of accuracy, sensitivity, and specificity and Matthew correlation coefficient. The evaluated result of RNN-GLSTM-ADAM extremely improves the PD detection accuracy than static feature based conventional ML and DL approaches.
Highlights
Parkinson‘s disease (PD) is a neurodegenerative disorder which affects the human brain nerve cells
(ρ)log ρj 1 − ρj Sparse Auto-Encoder (SAE) has been trained with the cost function stated in Eq (9) which consists of three terms such as Mean Squared Error (MSE) denoted in Eq (10) which reconstruct input X into Xover entire training dataset [45], second is lasso regression term denoted in Eq (11) and third term is sparsity transformation stated in Eq (7)
H1 is the number of neurons in first hidden layer, H2 is the number of neurons in second hidden layer and H3 is the number of neurons in third hidden layer of recurrent neural network (RNN)-Long Short Term Memory (LSTM)-adaptive moment estimation (ADAM) in Tab. 4
Summary
Parkinson‘s disease (PD) is a neurodegenerative disorder which affects the human brain nerve cells. In order to solve human-computer interaction (HCI) issues, more methods are developed by many researchers [7] Based on this many Machine Learning (ML) algorithms are applied to detect PD patients [8,9,10,11,12,13,14,15]. ML methods such as Artificial Neural Network [18], K-Nearest Neighbors (KNN) [19], Extreme Gradient Boosting (XGBoost) [20], Random Forest (RF) [21], Support Vector Machine (SVM), Decision Tree (DT) [22], Genetic algorithm (GA) [23] were used for PD classification using speech signals. Proposed a classification model called RNN-Graph LSTM to use the dynamic features of speech signals for PD detection. The rest of the paper is as follows: Section 2 discusses about the review of the literature, Section 3 stated about the dataset used for evaluation, Section 4 proposed an efficient PD classification model, Section 5 discusses about the experimented results and Section 6 concludes the work with future directions
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