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Prediction of Parkinson’s disease based on artificial neural networks using speech datasets

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Abstract

Parkinson’s disease (PD) is a progressive disorder of the nervous system that affects movement. Early prediction of PD can increase the chances of earlier intervention and delay the onset of the disease. Vocal impairment is one of the most important signs in the early stages of PD. Therefore, PD detection based on speech analysis and vocal patterns has attracted significant attention recently. In this paper, we propose a vowel-based artificial neural network (ANN) model for PD prediction based on single vowel phonation. Firstly, we propose a novel multi-layer neural network based on speech features to predict PD. The speech samples from 48 PD patients and 20 healthy individuals are processed into four types: vowel, number, word, and short sentence. Secondly, we establish ANN models with single-type speech samples versus combinations of multi-type speech samples, respectively. Comparative experiments demonstrate that the single-type vowel model is superior to other single-type models as well as multi-type models. Finally, we build a vowel-based ANN model for PD prediction and evaluate its performance. Extensive experiments demonstrate that the proposed model has a prediction accuracy of 91%, sensitivity of 99%, specificity of 82%, and area under the receiver operating characteristic curve (AUC) of 91%, which is superior to the performance of previous methods. Overall, this study demonstrates that the proposed model can provide good classification accuracy for predicting PD and can improve the rate of early diagnosis.

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Abbreviations

PD:

Parkinson’s disease

ANN:

Artificial neural network

AUC:

Area under the receiver operating

ROC:

Receiver operating characteristic characteristic curve

RF:

Random Forest

SVM:

Support vector machines

K-NN:

K-Nearest neighbor

LOSO:

Leave-one-subject-out

CV:

Cross validation

PCA:

Principal component analysis

LDA:

Latent Dirichlet allocation

MFCC:

Mel frequency cepstral coefficient

DNN:

Deep neural network

UPDRS:

The unified PD rating scale

ADL:

Activities of daily life

MLP:

MultiLayer perceptron

DA:

Dopamine

L–M:

Levenberg–Marquardt

TP:

True-positive

TN:

True-negative

FP:

False-positive

FN:

False-negative

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 61802076 and 61632009, in part by the Guangdong Provincial Natural Science Foundation under Grant 2017A030308006, in part by the High-Level Talents Program of Higher Education in Guangdong Province under Grant 2016ZJ01, and in part by Hainan Provincial Natural Science Foundation of China under Grant number 619MS057.

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Authors and Affiliations

  1. School of Information and Communication Engineering, Hainan University, Haikou, 570228, Hainan, People’s Republic of China

    Wei Liu

  2. School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, 510006, Guangdong, People’s Republic of China

    Jierong Liu, Tao Peng & Guojun Wang

  3. College of Biomedical Information and Engineering, Hainan Medical University, Street, Haikou, 571199, Hainan, People’s Republic of China

    Wei Liu

  4. Department of Automation and Applied Informatics, Aurel Vlaicu University of Arad, 310130, Arad, Romania

    Valentina Emilia Balas

  5. Department of Health and Human Development, Stefan cel Mare University of Suceava, 720229, Suceava, Romania

    Oana Geman

  6. Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan

    Hung-Wen Chiu

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  1. Wei Liu
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  2. Jierong Liu
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  3. Tao Peng
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  4. Guojun Wang
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  5. Valentina Emilia Balas
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  7. Hung-Wen Chiu
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Correspondence to Tao Peng.

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Liu, W., Liu, J., Peng, T. et al. Prediction of Parkinson’s disease based on artificial neural networks using speech datasets. J Ambient Intell Human Comput 14, 13571–13584 (2023). https://doi.org/10.1007/s12652-022-03825-w

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  • DOI: https://doi.org/10.1007/s12652-022-03825-w

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