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Link to original content: https://doi.org/10.1007/s00521-020-05233-7
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Multi-Variate vocal data analysis for Detection of Parkinson disease using Deep Learning

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Abstract

Machine learning (ML) and Deep learning (DL) methods are differently implemented with various decision-making abilities. Particularly, the usage of ML and DL techniques in disease detection is inevitable in the near future. This work uses the ability of acoustic-based DL techniques for detecting Parkinson disease symptoms. This disease can be identified by many DL techniques such as deep knowledge creation networks and recurrent networks. The proposed Deep Multi-Variate Vocal Data Analysis (DMVDA) System has been designed and implemented using Acoustic Deep Neural Network (ADNN), Acoustic Deep Recurrent Neural Network (ADRNN), and Acoustic Deep Convolutional Neural Network (ADCNN). Further, DMVDA has been specially developed with absolute multi-variate speech attribute processing algorithm for effective value creation. In order to improve the benefits of this speech-processing algorithm, the DMVDA has acoustic data sampling procedures. The DL techniques introduced in this work helps to identify Parkinson symptoms by analyzing the heterogeneous dataset. The integration of these techniques produces nominal 3% increase in the performance than the existing techniques.

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Abbreviations

A(M) :

Parkinson acoustic model

S(p) :

Speech sound pressure

M(v) :

Medium wave particle velocity

ϑ :

Sound vector

P(f) :

Adaptive frequency

λ T :

Wavelength

f :

Frequency

Mm :

Multimodal acoustic data matrix

T :

Time interval

C :

Number of classes

D(S) :

Speech dataset

τ :

Transition function

Rh(T) :

Recurrent hidden layer

U T :

Input at time ‘T’

ψ :

Output function

W :

State transition matrix

Z :

Input matrix

J :

Output matrix

Ф :

Nonlinear acoustic element function

G T :

Gate recurrent unit

s :

Data sample

u :

Normal distribution value

I :

Success rate

m :

Failure rate

S :

Size of population

α :

Sampling error rate

e :

Convolution function

ML:

Machine learning

DL:

Deep learning

DNN:

Deep neural network

RNN:

Recurrent neural network

CNN:

Convolutional neural network

PCA:

Principle component analysis

SVM:

Support vector machine

NN:

Neural network

RF:

Random Forest

NB:

Naïve Bayes

EEG:

Electroencephalogram

DMVDA:

Deep Multi-Variate Vocal Data Analysis

ADNN:

Acoustic Deep Neural Network

ADRNN:

Acoustic Deep Recurrent Neural Network

ADCNN:

Acoustic Deep Convolutional Neural Network

LSTM:

Long Short-Term Memory

References

  1. Sethi KD (2002) Clinical aspects of Parkinson disease. Current Opinion in Neurology 15(4):457–460

    Article  Google Scholar 

  2. Poewe W, Seppi K, Tanner CM, Halliday GM, Brundin P, Volkmann J, Schrag A-E, Lang AE (2017) Parkinson disease. Nature Reviews Disease Primers 3(1):1–21

    Article  Google Scholar 

  3. Cahn-Weiner DA, Williams K, Grace J, Tremont G, Westervelt H, Stern RA (2003) Discrimination of dementia with Lewy bodies from Alzheimer disease and Parkinson disease using the clock drawing test. Cognitive and Behavioral Neurology 16(2):85–92

    Article  Google Scholar 

  4. Cavallo, F., Moschetti, A., Esposito, D., Maremmani, C., & Rovini, E. (2019). Upper limb motor pre-clinical assessment in Parkinson’s disease using machine learning. Parkinsonism & Related Disorders

  5. Yao, L., Brown, P., & Shoaran, M. (2018, October). Resting Tremor Detection in Parkinson’s Disease with Machine Learning and Kalman Filtering. In 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS) (pp. 1–4). IEEE.

  6. Almeida JS, Rebouças Filho PP, Carneiro T, Wei W, Damaševičius R, Maskeliūnas R, de Albuquerque VHC (2019) Detecting Parkinson’s disease with sustained phonation and speech signals using machine learning techniques. Pattern Recognition Letters 125:55–62

    Article  Google Scholar 

  7. Anand, A., Haque, M. A., Alex, J. S. R., & Venkatesan, N. (2018, December). Evaluation of Machine learning and Deep learning algorithms combined with dimentionality reduction techniques for classification of Parkinson’s Disease. In 2018 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) (pp. 342–347). IEEE.

  8. Alqahtani, E. J., Alshamrani, F. H., Syed, H. F., & Olatunji, S. O. (2018, April). Classification of Parkinson’s Disease Using NNge Classification Algorithm. In 2018 21st Saudi Computer Society National Computer Conference (NCC) (pp. 1–7). IEEE.

  9. Mašić, F., Đug, M., Nuhić, J., & Kevrić, J. (2017, May). Detection of Parkinson’s Disease by Voice Signal. In International symposium on innovative and interdisciplinary applications of advanced technologies (pp. 1066–1073). Springer, Cham.

  10. Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G (2019) Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Computing and Applications 32(3):839–854

    Article  Google Scholar 

  11. Wu H, Soraghan J, Lowit A, Di Caterina G (2018) A deep learning method for pathological voice detection using convolutional deep belief networks. Inter speech-2018

  12. Aich, S., Kim, H. C., Hui, K. L., Al-Absi, A. A., & Sain, M. (2019, February). A supervised machine learning approach using different feature selection techniques on voice datasets for prediction of Parkinson’s disease. In 2019 21st International Conference on Advanced Communication Technology (ICACT) (pp. 1116–1121). IEEE.

  13. Almalaq, A., Dai, X., Zhang, J., Hanrahan, S., Nedrud, J., & Hebb, A. (2015, November). Causality graph learning on cortical information flow in Parkinson’s disease patients during behaviour tests. In 2015 49th Asilomar Conference on Signals, Systems and Computers (pp. 925–929). IEEE.

  14. Karan B, Sahu SS, Mahto K (2019) Parkinson disease prediction using intrinsic mode function based features from speech signal. Biocybernetics and Biomedical Engineering 40(1):249–264

    Article  Google Scholar 

  15. Agarwal, A., Chandrayan, S., & Sahu, S. S. (2016, March). Prediction of Parkinson’s disease using speech signal with extreme learning machine. In 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) (pp. 3776–3779). IEEE.

  16. Gottapu RD, Dagli CH (2018) Analysis of Parkinson’s Disease Data. Procedia Computer Science 140:334–341

    Article  Google Scholar 

  17. Al-Fatlawi, A. H., Jabardi, M. H., & Ling, S. H. (2016, July). Efficient diagnosis system for Parkinson’s disease using deep belief network. In 2016 IEEE Congress on Evolutionary Computation (CEC) (pp. 1324–1330). IEEE.

  18. Shinde S, Prasad S, Saboo Y, Kaushick R, Saini J, Pal PK, Ingalhalikar M (2019) Predictive markers for Parkinson’s disease using deep neural nets on neuromelanin sensitive MRI. Neuroimage Clinical 22:101748

    Article  Google Scholar 

  19. Oh, S. L., Hagiwara, Y., Raghavendra, U., Yuvaraj, R., Arunkumar, N., Murugappan, M., & Acharya, U. R. (2018). A deep learning approach for Parkinson’s disease diagnosis from EEG signals, Neural Computing and Applications, pp. 1–7.

  20. Rajamanickam Yuvaraj U, Acharya R, Hagiwara Y (2018) A novel Parkinson’s disease diagnosis index using higher-order spectra features in EEG signals. Neural Computing and Applications 30(4):1225–1235

    Article  Google Scholar 

  21. Yıldırım, Ö., Baloglu, U. B., & Acharya, U. R (2018), A deep convolutional neural network model for automated identification of abnormal EEG signals, Neural Computing and Applications, pp 1–12.

  22. Xiao, L., Zhang, H., Chen, W., Wang, Y., & Jin, Y. (2018). Transformable Convolutional Neural Network for Text Classification. In IJCAI, (pp. 4496–4502).

  23. Alvi, M. (2016). A manual for selecting sampling techniques in research.

  24. Gürüler H (2017) A novel diagnosis system for Parkinson’s disease using complex-valued artificial neural network with k-means clustering feature weighting method. Neural Computing and Applications 28(7):1657–1666

    Article  Google Scholar 

  25. Liu L, Ouyang W, Wang X, Fieguth P, Chen J, Liu X, Pietikäinen M (2019) Deep learning for generic object detection: a survey. arXiv:1809.02165

  26. Little MA, McSharry PE, Hunter EJ, Ramig LO (2009) Suitability of dysphonia measurements for tele monitoring of Parkinson’s disease. IEEE Transactions on Biomedical Engineering 56(4):1015–1022

    Article  Google Scholar 

  27. Erdogdu Sakar B, Isenkul M, 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 Journal of Biomedical and Health Informatics 17(4):828–834

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science and Engineering, GGR College of Engineering, Vellore, India

    Gayathri Nagasubramanian

  2. Department of Information Technology, Thiagarajar College of Engineering, Madurai, India

    Muthuramalingam Sankayya

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  1. Gayathri Nagasubramanian
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  2. Muthuramalingam Sankayya
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Correspondence to Gayathri Nagasubramanian.

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Nagasubramanian, G., Sankayya, M. Multi-Variate vocal data analysis for Detection of Parkinson disease using Deep Learning. Neural Comput & Applic 33, 4849–4864 (2021). https://doi.org/10.1007/s00521-020-05233-7

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