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Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson’s disease

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Abstract

Speech diagnosis of Parkinson’s disease (PD) as a non-invasive and simple diagnosis method is particularly worth exploring. However, the number of samples of speech-based PD is relatively small, and there exist discrepancies in the distribution between subjects. In order to solve the two problems, a novel unsupervised two-step sparse transfer learning is proposed in this paper to tackle with PD speech diagnosis. In the first step, convolution sparse coding with the coordinate selection of samples and features is designed to learn speech structure from the source domain to replenish sample information of the target domain. In the second step, joint local structure distribution alignment is designed to maintain the neighbor relationship between the respective samples of the training set and test set, and reduce the distribution difference between the two domains at the same time. Two representative public PD speech datasets and one real-world PD speech dataset were exploited to verify the proposed method on PD speech diagnosis. Experimental results demonstrate that each step of the proposed method has a positive effect on the PD speech classification results, and it also delivers superior performance over the existing relative methods.

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References

  1. Mirarchi D, Vizza P, Tradigo G et al (2017) Signal analysis for voice evaluation in Parkinson’s disease. In: 2017 IEEE International conference on healthcare informatics (ICHI). IEEE, pp 530–535

  2. Vollstedt EJ, Kasten M, Klein C et al (2019) Using global team science to identify genetic Parkinson’s disease worldwide. Ann Neurol 86(2):153

    Article  Google Scholar 

  3. Tsanas A, Little MA, Mcsharry PE et al (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 

  4. Gümüşçü A, Karadağ K, Tenekecı ME et al (2017) Genetic algorithm based feature selection on diagnosis of Parkinson disease via vocal analysis. In: 2017 25th Signal processing and communications applications conference (SIU). IEEE, pp 1–4

  5. Emrani S, McGuirk A, Xiao W (2017) Prognosis and diagnosis of Parkinson's disease using multi-task learning. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, pp 1457–1466

  6. Tsanas A, Little MA, Mcsharry PE et al (2010) Accurate telemonitoring of Parkinson’s disease progression by noninvasive speech tests. IEEE Trans Biomed Eng 57(4):884–893

    Article  Google Scholar 

  7. Gillivan-Murphy P, Miller N, Carding P (2019) Voice tremor in Parkinson’s disease: an acoustic study. J Voice 33(4):526–535

    Article  Google Scholar 

  8. Wroge TJ, Özkanca Y, Demiroglu C et al (2018) Parkinson’s disease diagnosis using machine learning and voice. In: 2018 IEEE Signal processing in medicine and biology symposium (SPMB). IEEE, pp 1–7

  9. Magee M, Copland D, Vogel AP (2019) Motor speech and non-motor language endophenotypes of Parkinson’s disease. Expert Rev Neurother 19:1191–1200

    Article  Google Scholar 

  10. Orozco-Arroyave JR, Belalcazar-Bolanos EA, Arias-Londoño JD et al (2015) Characterization methods for the detection of multiple voice disorders: neurological, functional, and laryngeal diseases. IEEE J Biomed Health Inform 19(6):1820–1828

    Article  Google Scholar 

  11. Skodda S, Visser W, Schlegel U (2011) Vowel articulation in Parkinson’s disease. J Voice 25(4):467–472

    Article  Google Scholar 

  12. Orozco-Arroyave JR, Hönig F, Arias-Londoño JD et al (2016) Automatic detection of Parkinson’s disease in running speech spoken in three different languages. J Acoust Soc Am 139(1):481–500

    Article  Google Scholar 

  13. Kalf J, De Swart B, Bloem BR et al (2007) 3.414 Guidelines for speech–language therapy in Parkinson’s disease. Parkinsonism Relat Disord 13(08):S183–S184

    Article  Google Scholar 

  14. Zou N, Huang X (2018) Empirical Bayes transfer learning for uncertainty characterization in predicting Parkinson’s disease severity. IISE Trans Healthcare Syst Eng 8(3):209–219

    Article  Google Scholar 

  15. Sakar BE, Isenkul ME, Sakar CO et al (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 

  16. Naseer A, Rani M, Naz S et al (2020) Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput Appl 32(3):839–854

    Article  Google Scholar 

  17. Al-Fatlawi AH, Jabardi MH, Ling SH (2016) Efficient diagnosis system for Parkinson's disease using deep belief network. In: 2016 IEEE Congress on evolutionary computation (CEC). IEEE, pp 1324–1330

  18. Derya A, Akif D (2016) An expert diagnosis system for Parkinson disease based on genetic algorithm-wavelet kernel-extreme learning machine. Parkinson’s Dis 2016:1–9

    Google Scholar 

  19. Cai Z, Gu J, Chen H et al (2017) A new hybrid intelligent framework for predicting Parkinson’s disease. IEEE Access 5:17188–17200

    Article  Google Scholar 

  20. Ozkan H (2016) A comparison of classification methods for telediagnosis of Parkinson’s disease. Entropy 18(4):115

    Article  Google Scholar 

  21. Yang S, Zheng F, Luo X et al (2014) Effective dysphonia detection using feature dimension reduction and kernel density estimation for patients with Parkinson’s disease. PLoS ONE 9(2):e88825

    Article  Google Scholar 

  22. Shahbakhti M, Taherifar D, Sorouri A (2013) Linear and non-linear speech features for detection of Parkinson's disease. In: The 6th 2013 biomedical engineering international conference. IEEE, pp 1–3

  23. Shahbakhi M, Far DT, Tahami E (2014) Speech analysis for diagnosis of Parkinson’s disease using genetic algorithm and support vector machine. J Biomed Sci Eng 7(4):147–156

    Article  Google Scholar 

  24. Behroozi M, Sami A (2016) A multiple-classifier framework for Parkinson’s disease detection based on various vocal tests. Int J Telemed Appl 2016(11, supplement 5):1–9

    Google Scholar 

  25. Vásquez-Correa JC, Orozco-Arroyave JR, Arora R et al (2017) Multi-view representation learning via GCCA for multimodal analysis of Parkinson's disease. In: 2017 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2966–2970

  26. Mekyska J, Rektorova I, Smekal Z (2011) Selection of optimal parameters for automatic analysis of speech disorders in Parkinson's disease. In: 2011 34th International conference on telecommunications and signal processing (TSP). IEEE, pp 408–412

  27. Sakar CO, Kursun O (2010) Telediagnosis of Parkinson’s disease using measurements of dysphonia. J Med Syst 34(4):591–599

    Article  Google Scholar 

  28. Su M, Chuang KS (2015) Dynamic feature selection for detecting Parkinson's disease through voice signal. In: 2015 IEEE MTT-S 2015 international microwave workshop series on RF and wireless technologies for biomedical and healthcare applications (IMWS-BIO). IEEE, pp 148–149

  29. Caesarendra W, Ariyanto M, Setiawan JD et al (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

  30. Kaya E, Findik O, Babaoglu I et al (2011) Effect of discretization method on the diagnosis of Parkinson’s disease. Int J Innov Comput Inf Control 7:4669–4678

    Google Scholar 

  31. Benba A, Jilbab A, Hammouch A (2014) Hybridization of best acoustic cues for detecting persons with Parkinson's disease. In: 2014 Second world conference on complex systems (WCCS). IEEE, pp 622–625

  32. Galaz Z, Mekyska J, Mzourek Z et al (2016) Prosodic analysis of neutral, stress-modified and rhymed speech in patients with Parkinson’s disease. Comput Methods Prog Biomed 127:301–317

    Article  Google Scholar 

  33. Naranjo L, Pérez CJ, Campos-Roca Y et al (2016) Addressing voice recording replications for Parkinson’s disease detection. Expert Syst Appl 46:286–292

    Article  Google Scholar 

  34. Hirschauer TJ, Adeli H, Buford JA (2015) Computer-aided diagnosis of Parkinson’s disease using enhanced probabilistic neural network. J Med Syst 39(11):179

    Article  Google Scholar 

  35. Alqahtani EJ, Alshamrani FH, Syed HF et al (2018) Classification of Parkinson’s disease using NNge classification algorithm. In: 2018 21st Saudi computer society national computer conference (NCC). IEEE, pp 1–7

  36. Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359

    Article  Google Scholar 

  37. Kim DH, Wit H, Thurston M (2018) Artificial intelligence in the diagnosis of Parkinson’s disease from ioflupane-123 single-photon emission computed tomography dopamine transporter scans using transfer learning. Nucl Med Commun 39(10):887–893

    Article  Google Scholar 

  38. Das D, Lee CSG (2018) Sample-to-sample correspondence for unsupervised domain adaptation. Eng Appl Artif Intell 73(AUG.):80–91

    Article  Google Scholar 

  39. Sun B, Feng J, Saenko K (2016) Return of frustratingly easy domain adaptation. In: Thirtieth AAAI conference on artificial intelligence

  40. Sun B, Saenko K (2016) Deep coral: correlation alignment for deep domain adaptation. In: European conference on computer vision. Springer, Cham, pp 443–450

  41. Sakurai S, Uchiyama H, Shimada A et al (2018) Two-step transfer learning for semantic plant segmentation. In: 7th International conference on pattern recognition applications and methods

  42. An G, Yokota H, Motozawa N et al (2019) Deep learning classification models built with two-step transfer learning for age related macular degeneration diagnosis. In: 2019 41st Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE

  43. Zhang R, Guo Z, Sun Y et al (2020) COVID19X-rayNet: a two-step transfer learning model for the COVID-19 detecting problem based on a limited number of chest X-ray images. Interdiscip Sci Comput Life Sci 12:1–11

    Article  Google Scholar 

  44. Zhang H, Patel VM (2018) Convolutional sparse and low-rank coding-based image decomposition. IEEE Trans Image Process 27(5):2121–2133

    Article  MathSciNet  MATH  Google Scholar 

  45. Hu X, Heide F (2018) Convolutional sparse coding for RGB + NIR imaging. IEEE Trans Image Process 27(4):1611–1625

    Article  MathSciNet  MATH  Google Scholar 

  46. Wohlberg B (2016) Efficient algorithms for convolutional sparse representations. IEEE Trans Image Process 25(1):301–315

    Article  MathSciNet  MATH  Google Scholar 

  47. Hang Chang; Ju Han; Cheng Zhong (2018) Unsupervised transfer learning via multi-scale convolutional sparse coding for biomedical applications. IEEE Trans Pattern Anal Mach Intell 40(5):1182–1194

    Article  Google Scholar 

  48. Pan SJ, Tsang IW, Kwok JT et al (2011) Domain adaptation via transfer component analysis. IEEE Trans Neural Netw 22(2):199–210

    Article  Google Scholar 

  49. Ganin Y, Lempitsky V (2014) Unsupervised domain adaptation by backpropagation. arXiv preprint arXiv:1409.7495

  50. Bousmalis K, Trigeorgis G, Silberman N et al (2016) Domain separation networks. In: Advances in neural information processing systems, NIPS 2016, pp 343–351

  51. Kang G, Zheng L, Yan Y et al (2018) Deep adversarial attention alignment for unsupervised domain adaptation: the benefit of target expectation maximization. In: Proceedings of the European conference on computer vision (ECCV), pp 401–416

  52. Csurka G (2017) A comprehensive survey on domain adaptation for visual applications. In: Csurka G (ed) Domain adaptation in computer vision applications. Springer, Cham, pp 1–35

    Chapter  Google Scholar 

  53. Pinheiro PO (2018) Unsupervised domain adaptation with similarity learning. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 8004–8013

  54. Sener O, Song HO, Saxena A et al (2016) Learning transferrable representations for unsupervised domain adaptation. In: Advances in neural information processing systems, NIPS 2016, pp 2110–2118

  55. Haeusser P, Frerix T, Mordvintsev A et al (2017) Associative domain adaptation. In: Proceedings of the IEEE international conference on computer vision, pp 2765–2773

  56. Saito K, Ushiku Y, Harada T et al (2017) Adversarial dropout regularization. arXiv preprint arXiv:1711.01575

  57. Saito K, Watanabe K, Ushiku Y et al (2018) Maximum classifier discrepancy for unsupervised domain adaptation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 3723–3732

  58. Pei Z, Cao Z, Long M et al (2018) Multi-adversarial domain adaptation. In: Thirty-second AAAI conference on artificial intelligence

  59. Liu F, Zhang G, Lu J (2020) Heterogeneous domain adaptation: an unsupervised approach. IEEE Trans Neural Netw Learn Syst 31:5588–5602

    Article  MathSciNet  Google Scholar 

  60. Long M, Wang J, Ding G et al (2013) Transfer feature learning with joint distribution adaptation. In: Proceedings of the IEEE international conference on computer vision, pp 2200–2207

  61. Gong B, Shi Y, Sha F et al (2015) Geodesic flow kernel for unsupervised domain adaptation. In: 2012 IEEE Conference on computer vision and pattern recognition. IEEE

  62. Wang J, Feng W, Chen Y et al (2018) Visual domain adaptation with manifold embedded distribution alignment. In: 2018 ACM International conference on multimedia, pp 402–410

  63. Long M, Cao Y et al (2018) Transferable representation learning with deep adaptation networks. IEEE Trans Pattern Anal Mach Intell 41:3071–3085

    Article  Google Scholar 

  64. Long M, Zhu H, Wang J et al (2017) Deep transfer learning with joint adaptation networks. In: The 34th international conference on machine learning, Sydney, pp 2208–2217

  65. Ganin Y, Ustinova E, Ajakan H et al (2017) Domain-adversarial training of neural networks. J Mach Learn Res 17(1):2096–2030

    MathSciNet  MATH  Google Scholar 

  66. Long M, Cao Z, Wang J et al (2018) Conditional adversarial domain adaptation. In: 32nd Conference on neural information processing systems (NeurIPS 2018), Montreal, Canada pp 1640–1650

  67. Kononenko I (1994) Estimating attributes: analysis and extensions of RELIEF. In: European conference on machine learning. Springer, Berlin, pp 171–182

  68. Boyd S, Parikh N (2011) Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn 3(1):1–122

    Article  MATH  Google Scholar 

  69. Wang J, Chen Y, Hao S et al (2017) Balanced distribution adaptation for transfer learning. In: 2017 IEEE International conference on data mining (ICDM). IEEE, pp 1129–1134

  70. Pan SJ, Kwok JT, Yang Q (2008) Transfer learning via dimensionality reduction. In: AAAI, vol 8, pp 677–682

  71. He X, Niyogi P (2004) Locality preserving projections. In: Advances in neural information processing systems, NIPS 2004, pp 153–160

  72. Belkin M, Niyogi P (2002) Laplacian eigenmaps and spectral techniques for embedding and clustering. Im: NIPS'01: Proceedings of the 14th international conference on neural information processing systems: natural and synthetic, January 2001, pp 585–591

  73. Schölkopf B, Smola A, Müller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319

    Article  Google Scholar 

  74. Little MA, Mcshappy PE, Roberts SJ et al (2007) Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMed Eng OnLine 6:23–41

    Article  Google Scholar 

  75. Canturk I, Karabiber F (2016) A machine learning system for the diagnosis of Parkinson’s disease from speech signals and its application to multiple speech signal types. Arab J Sci Eng 41(12):5049–5059

    Article  Google Scholar 

  76. Eskıdere Ö, Karatutlu A, Ünal C (2015) Detection of Parkinson's disease from vocal features using random subspace classifier ensemble. In: 2015 Twelve international conference on electronics computer and computation (ICECCO). IEEE, pp 1–4

  77. Zhang H-H, Yang L, Liu Y, Wang P, Yin J, Li Y, Qiu M, Zhu X, Yan F (2016) Classification of Parkinson’s disease utilizing multi-edit nearest-neighbor and ensemble learning algorithms with speech samples. BioMed Eng OnLine 15(1):122–143

    Article  Google Scholar 

  78. 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 

  79. Li Y, Zhang C, Jia Y et al (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

  80. Vadovský M, Paralič J (2017) Parkinson's disease patients classification based on the speech signals. In: 2017 IEEE 15th International symposium on applied machine intelligence and informatics (SAMI). IEEE, pp 000321–000326

  81. Zhang YN (2017) Can a smartphone diagnose Parkinson disease? A deep neural network method and telediagnosis system implementation. Parkinson’s Dis 2017:1–11

    Google Scholar 

  82. Benba A, Jilbab A, Hammouch A (2016) Analysis of multiple types of voice recordings in cepstral domain using MFCC for discriminating between patients with Parkinsons disease and healthy people. Int J Speech Technol 19(3):449–456

    Article  Google Scholar 

  83. Kraipeerapun P, Amornsamankul S (2015) Using stacked generalization and complementary neural networks to predict Parkinson's disease. In: 2015 11th International conference on natural computation (ICNC). IEEE, pp 1290–1294

  84. Khan MM, Mendes A, Chalup SK (2018) Evolutionary wavelet neural network ensembles for breast cancer and Parkinson’s disease prediction. PLoS ONE 13(2):e0192192

    Article  Google Scholar 

  85. Ali L, Zhu C, Zhang Z et al (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 

  86. Shahbaba B, Neal R (2009) Nonlinear models using Dirichlet process mixtures. J Mach Learn Res 10(12):1829–1850

    MathSciNet  MATH  Google Scholar 

  87. Psorakis I, Damoulas T, Girolami MA (2010) Multiclass relevance vector machines: sparsity and accuracy. IEEE Trans Neural Netw 21(10):1588–1598

    Article  Google Scholar 

  88. Guo PF, Bhattacharya P, Kharma NN (2010) Advances in detecting Parkinson’s disease. In: Medical biometrics, second international conference, ICMB, Hong Kong, China, June. DBLP

  89. Das R (2010) A comparison of multiple classification methods for diagnosis of Parkinson disease. Expert Syst Appl 37(2):1568–1572

    Article  Google Scholar 

  90. Ozcift A, Gulten A (2011) Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms. Comput Methods Prog Biomed 104(3):443–451

    Article  Google Scholar 

  91. Luukka P (2011) Feature selection using fuzzy entropy measures with similarity classifier. Expert Syst Appl 38(4):4600–4607

    Article  Google Scholar 

  92. Li DC, Liu CW, Hu SC (2011) A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets. Artif Intell Med 52(1):45–52

    Article  Google Scholar 

  93. Spadoto AA, Guido RC, Carnevali FL et al (2011) Improving Parkinson's disease identification through evolutionary-based feature selection. In: 2011 Annual international conference of the IEEE engineering in medicine and biology society. IEEE, pp 7857–7860

  94. Polat K (2012) Classification of Parkinson’s disease using feature weighting method on the basis of fuzzy C-means clustering. Int J Syst Sci 43(4):597–609

    Article  MathSciNet  MATH  Google Scholar 

  95. Chen HL, Huang CC, Yu XG et al (2013) An efficient diagnosis system for detection of Parkinson’s disease using fuzzy k-nearest neighbor approach. Expert Syst Appl 40(1):263–271

    Article  Google Scholar 

  96. Åström F, Koker R (2011) A parallel neural network approach to prediction of Parkinson’s disease. Expert Syst Appl 38(10):12470–12474

    Article  Google Scholar 

  97. Daliri MR (2013) Chi-square distance kernel of the gaits for the diagnosis of Parkinson’s disease. Biomed Signal Process Control 8(1):66–70

    Article  Google Scholar 

  98. Zuo WL, Wang ZY, Liu T et al (2013) Effective detection of Parkinson’s disease using an adaptive fuzzy k-nearest neighbor approach. Biomed Signal Process Control 8(4):364–373

    Article  Google Scholar 

  99. Kadam VJ, Jadhav SM (2019) Feature ensemble learning based on sparse autoencoders for diagnosis of Parkinson’s disease. In: Kadam V, Jadhav SM (eds) Computing, communication and signal processing. Springer, Singapore, pp 567–581

  100. Ma H, Tan T, Zhou H et al (2016) Support vector machine-recursive feature elimination for the diagnosis of Parkinson disease based on speech analysis. In: 2016 Seventh international conference on intelligent control and information processing (ICICIP). IEEE, pp 34–40

  101. Dash S, Thulasiram R, Thulasiraman P (2017) An enhanced chaos-based firefly model for Parkinson's disease diagnosis and classification. In: 2017 International conference on information technology (ICIT). IEEE, pp 159–164

  102. 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 Comput Appl 28(7):1657–1666

    Article  Google Scholar 

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Acknowledgements

We are grateful for the support of the National Natural Science Foundation of China NSFC (No. 61771080); the Fundamental Research Funds for the Central Universities (2019CDQYTX019, 2019CDCGTX306), the Basic and Advanced Research Project in Chongqing (cstc2018jcyjAX0779, cstc2020jcyj-msxmX0523, cstc2020jcyj-msxmX0100); and the Chongqing Technology Innovation and Application Development Project (cstc2020jscx-fyzx0212).

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  1. School of Microelectronics and Communication Engineering, Chongqing University, Chongqing, 400030, China

    Yongming Li, Xinyue Zhang, Pin Wang, Xiaoheng Zhang & Yuchuan Liu

  2. Chongqing Radio and TV University, Chongqing, 400052, China

    Xiaoheng Zhang

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Li, Y., Zhang, X., Wang, P. et al. Insight into an unsupervised two-step sparse transfer learning algorithm for speech diagnosis of Parkinson’s disease. Neural Comput & Applic 33, 9733–9750 (2021). https://doi.org/10.1007/s00521-021-05741-0

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