Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

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

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

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

References

  • Ali L, Zhu C, Zhang Z, Liu Y (2019) Automated detection of Parkinson’s disease based on multiple types of sustained phonations using linear discriminant analysis and genetically optimized neural network. IEEE J Transl Eng Health Med 7:1–10

    Article  Google Scholar 

  • Amato F, López A, Peña-Méndez EM (2013) Artificial neural networks in medical diagnosis. J Appl Biomed 11(2):47–58

    Article  Google Scholar 

  • Ardiansyah S, Majid MA, Zain JM (2016) Knowledge of extraction from trained neural network by using decision tree. In: 2016 2nd international conference on science in information technology (ICSITech). IEEE, pp 220–225

  • Bastiaan BR, Okun MS, Christine K (2021) Parkinson’s disease. Lancet 397:2284–2303

    Article  Google Scholar 

  • Behroozi M, Sami A (2016) A multiple-classifier framework for Parkinson’s disease detection based on various vocal tests. Int J Telemed Appl 2016:6837498

    Google Scholar 

  • Benba A, Jilbab A, Hammouch A (2017) Using human factor cepstral coefficient on multiple types of voice recordings for detecting patients with Parkinson’s disease. IRBM 38(6):346–351

    Article  Google Scholar 

  • Berus L, Klancnik S, Brezocnik M, Ficko M (2019) Classifying Parkinson’s disease based on acoustic measures using artificial neural networks. Sensors 19(1):16

    Article  Google Scholar 

  • Caesarendra W, Ariyanto M, Setiawan JD, Arozi M, Chang CR (2014) A pattern recognition method for stage classification of parkinson’s disease utilizing voice features. In: 2014 IEEE conference on biomedical engineering and sciences (IECBES). IEEE, pp 87–92

  • Chiuchisan I, Geman O, Chiuchisan I, Iuresi AC, Graur A (2014) Neuroparkinscreen—a health care system for neurological disorders screening and rehabilitation. In: 2014 international conference and exposition on electrical and power engineering (EPE). IEEE, pp 536–540

  • De Keyser K, Santens P, Bockstael A, Botteldooren D, Talsma D, De Vos S, Van Cauwenberghe M, Verheugen F, Corthals P, De Letter M (2016) The relationship between speech production and speech perception deficits in Parkinson’s disease. J Speech Lang Hear Res 59(5):915–931

    Article  Google Scholar 

  • Deperlioglu O, Kose U, Gupta D, Khanna A, Sangaiah AK (2020) Diagnosis of heart diseases by a secure internet of health things system based on autoencoder deep neural network. Comput Commun 162:31–50

    Article  Google Scholar 

  • Fish J (2018) Encyclopedia of clinical neuropsychology. Unified Parkinson’s disease rating scale. Springer, New York, pp 3541–3543

    Book  Google Scholar 

  • Frid A, Kantor A, Svechin D, Manevitz LM (2016) Diagnosis of Parkinson’s disease from continuous speech using deep convolutional networks without manual selection of features. In: 2016 IEEE international conference on the science of electrical engineering (ICSEE). IEEE, pp 1–4

  • Geman O, Chiuchisan O (2015) Deep brain stimulation efficiency and Parkinson’s disease stage prediction using Markov models. In: 2015 E-health and bioengineering conference (EHB). IEEE, pp 1–4

  • Gunduz H (2019) Deep learning-based Parkinson’s disease classification using vocal feature sets. IEEE Access 7:115540–115551

    Article  Google Scholar 

  • Gupta D, Julka A, Jain S, Aggarwal T, Khanna A, Arunkumar N, de Albuquerque VH (2018) Optimized cuttlefish algorithm for diagnosis of Parkinson’s disease. Cogn Syst Res 52:36–48

    Article  Google Scholar 

  • Gupta D, Sundaram S, Khanna A, Hassanien AE, De Albuquerque VH (2018) Improved diagnosis of Parkinson’s disease using optimized crow search algorithm. Comput Electr Eng 68:412–424

    Article  Google Scholar 

  • Lahmiri S, Dawson DA, Shmuel A (2018) Performance of machine learning methods in diagnosing Parkinson’s disease based on dysphonia measures. Biomed Eng Lett 8(1):29–39

    Article  Google Scholar 

  • Li Y, Zhang C, Jia Y, Wang P, Zhang X, Xie T (2017) Simultaneous learning of speech feature and segment for classification of Parkinson disease. In: 2017 IEEE 19th international conference on e-health networking, applications and services (Healthcom). IEEE, pp 1–6

  • Malathi D, Logesh R, Subramaniyaswamy V, Vijayakumar V, Sangaiah AK (2019) Hybrid reasoning-based privacy-aware disease prediction support system. Comput Electr Eng 73:114–127

    Article  Google Scholar 

  • Mostafa SA, Mustapha A, Khaleefah SH, Ahmad MS, Mohammed MA (2018) Evaluating the performance of three classification methods in diagnosis of Parkinson’s disease. In: International conference on soft computing and data mining. Springer, pp 43–52

  • Mostafa SA, Mustapha A, Mohammed MA, Hamed RI, Arunkumar N, Abd G, Mohd K, Jaber MM, Khaleefah SH (2019) Examining multiple feature evaluation and classification methods for improving the diagnosis of Parkinson’s disease. Cogn Syst Res 54:90–99

    Article  Google Scholar 

  • Movement Disorder Society Task Force on Rating Scales for Parkinson’s Disease (2003) The unified Parkinson’s disease rating scale (updrs): status and recommendations. Mov Disord 18(7):738–750

  • Pereira CR, Pereira DR, Weber SAT, Hook C, de Albuquerque VHC, Papa JP (2019) A survey on computer-assisted Parkinson’s disease diagnosis. Artif Intell Med 95:48–63

    Article  Google Scholar 

  • Pérez-Sánchez B, Fontenla-Romero O, Guijarro-Berdiñas B (2018) A review of adaptive online learning for artificial neural networks. Artif Intell Rev 49(2):281–299

    Article  Google Scholar 

  • Rizek P, Kumar N, Jog MS (2016) An update on the diagnosis and treatment of Parkinson disease. CMAJ 188(16):1157–1165

    Article  Google Scholar 

  • Robert C, Wilson CS, Lipton RB, Arreto C-D (2018) Parkinson’s disease: evolution of the scientific literature from 1983 to 2017 by countries and journals. Parkinsonism Relat Disord 61:10–18

    Article  Google Scholar 

  • Isenkul ME, Sakar CO, Sertbas A, Gurgen F, Delil S, Apaydin H, Kursun O (2013) Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inform 17(4):828–834

    Article  Google Scholar 

  • Sangaiah AK, Arumugam M, Bian G-B (2020) An intelligent learning approach for improving ecg signal classification and arrhythmia analysis. Artif Intell Med 103:101788

    Article  Google Scholar 

  • Sangaiah AK, Dhanaraj JSA, Mohandas P, Castiglione A (2020) Cognitive iot system with intelligence techniques in sustainable computing environment. Comput Commun 154:347–360

    Article  Google Scholar 

  • Soumaya Z, Taoufiq BD, Benayad N, Yunus K, Abdelkrim A (2020) The detection of Parkinson disease using the genetic algorithm and svm classifier. Appl Acoust 171:107528

    Article  Google Scholar 

  • Toderean R, Geman O, Chiuchisan I, Balas VE, Beiu V (2016) Novel method for neurodegenerative disorders screening patients using hurst coefficients on eeg delta rhythm. In: International workshop soft computing applications. Springer, pp 349–358

  • Tsanas A, Little MA, McSharry PE, Spielman J, Ramig LO (2012) Novel speech signal processing algorithms for high-accuracy classification of Parkinson’s disease. IEEE Trans Biomed Eng 59(5):1264–1271

    Article  Google Scholar 

Download references

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.

Author information

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

Authors
  1. Wei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jierong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tao Peng
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guojun Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Valentina Emilia Balas
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Oana Geman
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Hung-Wen Chiu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Tao Peng.

Ethics declarations

Human participants or animals

This article does not contain any studies with human participants or animals performed by any of the authors. In this experiment, we did not collect any samples of human and animals.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

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

Keywords

Search

Navigation