An optimized rnn-lstm approach for parkinson’s disease early detection using speech features

Abstract

Parkinson's disease (PD) is the second most common neurodegenerative disorder disease right after Alzheimer's and the most common movement disorder for elderly people. It is characterized as a progressive loss of muscle control, which leads to trembling characterized by uncontrollable shaking, or (tremors) in different parts of the body. In recent years, deep learning (DL) models achieved significant progress in automatic speech recognition, however, limited studies addressed the problem of distinguishing people with PD for further clinical diagnosis. In this paper, an approach for the early detection of patients with PD using speech features was proposed, a recurrent neural network (RNN) with long short-term memory (LSTM) is applied with the batch normalization layer and adaptive moment estimation (ADAM) optimization algorithm used after the network hidden layers to improve the classification performance. The proposed approach is applied with 2 benchmark datasets of speech features for patients with PD and healthy control subjects. The proposed approach achieved an accuracy of 95.8% and MCC=92.04% for the testing dataset. In future work, we aim to increase the voice features that will be worked on and consider using handwriting kinematic features.