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.

Bias Investigation in Artificial Intelligence Systems for Early Detection of Parkinson’s Disease: A Narrative Review

“…In the secondary objective, the goal was to automatically detect the studies that lie in the three categories of bias, such as low, moderate, and high bias. Other exclusion criteria included having no correlation between Parkinson’s disease with other neurological diseases mentioned in the manuscript, and if the article was written in a different language other than English [ 1 , 41 ]. The information considered for the PD studies’ data extraction was (i) author name, (ii) year of publication, (iii) objective of the studies, (iv) demographic discussion, (v) data types, (vi) data source, (vii) diagnosis method, (viii) bias studies, and (ix) attribute studies.…”

“…It is seen that many of the AI models show high accuracy, but the data size used for the testing and training of the algorithm is small, and the model fails to explain scientific validation. Hence, it results in High-Moderate (HM) in the studies [ 1 , 5 , 9 , 37 , 62 , 65 ]. The cumulative cutoff for the studies was determined by using various factors such as (i) associated studies of the PD, (ii) impact factor, (iii) the selected data, (iv) performance indicators, (v) clinical trials, etc.…”

“…The Low-Moderate (LM) studies [ 1 , 5 , 9 , 37 , 62 , 65 ] observations are the articles containing information such as (i) high data count of the PD vs. normal; (ii) performance measures; (iii) comparative analysis with various ML, DL, and HDL algorithms; (iv) explanations of the benchmarking studies. The Moderate-bias studies [ 1 , 5 , 9 , 37 , 62 , 65 , 66 ] observations were (i) sufficient data, (ii) average impact factor, and (iii) comparison of the input parameters.…”

“…The Low-Moderate (LM) studies [ 1 , 5 , 9 , 37 , 62 , 65 ] observations are the articles containing information such as (i) high data count of the PD vs. normal; (ii) performance measures; (iii) comparative analysis with various ML, DL, and HDL algorithms; (iv) explanations of the benchmarking studies. The Moderate-bias studies [ 1 , 5 , 9 , 37 , 62 , 65 , 66 ] observations were (i) sufficient data, (ii) average impact factor, and (iii) comparison of the input parameters. The High-Moderate (HM) studies [ 3 , 6 , 54 , 60 , 67 , 68 ] observations associated with the articles were (i) a smaller number of data, (ii) insufficient dissuasion on the selected model, (iii) improper explanation of the algorithm, (iv) insufficient performance analysis, (v) lack of demographic discussion, and (vi) insufficient discussion on clinical evaluation.…”

“…Parkinson’s disease (PD) is a neurodegenerative disorder; James Parkinson first portrayed it in 1817 [ 1 , 2 ]. Globally, over 2% of the population is more than 65 years of age, and around 5–20 people per 100,000 each year are affected by this illness, demonstrating its predominance and frequency rate with maturity [ 3 , 4 , 5 ].…”