Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

A Review on Early Diagnosis of Parkinson’s Disease Using Speech Signal Parameters Based on Machine Learning Technique

  • Conference paper
  • First Online:
Futuristic Communication and Network Technologies

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 966))

  • 289 Accesses

Abstract

Early diagnosis means an individual gets an indication about the disease on his or her own at very early stage of the disease. Today, many people across the globe are suffering from Parkinson’s disease (PD). Early detection of Parkinson’s disease can be a better choice to treat the disease much early. Vocal cord disorder, speech impairments/speech disorders are the early indicators of PD. The initial stage of PD affects the human speech production mechanism. The speech impairments are not apparent to common listeners. We should monitor carefully for the initial stage of PD by using proper expert systems. In this review, we mainly focused on speech signal analysis for the identification of PD with the help of machine learning techniques. The voice sample of affected people from PD can be used in an early detection algorithm using various classification models with different accuracy, sensitivity, specificity, etc. In our review, we found that mainly two types of techniques have been used in this problem (a) conventional feature-based techniques and (b) machine learning-based techniques. The detailed review using these types of algorithms is presented in this paper. In feature-based applications, mel frequency cepstral coefficient (MFCC) and linear predictive coding (LPC) are the mostly used features. Machine learning-based algorithms used intelligent architecture like artificial neural network (ANN), convolution neural network (CNN), hidden Markov model (HMM), XG boost, support vector machine (SVM), etc. It is found that machine learning-based algorithms are doing better in terms of highest accuracy but with some limitations.

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

Access this chapter

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

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 229.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 299.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 299.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. 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 7:2000410

    Article  Google Scholar 

  2. Akshay S, Vincent K (2019) Identification of Parkinson disease patients classification using feed forward technique based on speech signals. IJEAT 8(5)

    Google Scholar 

  3. Radha N, Rm SM, Sameera HS (2021) Parkinson’s disease detection using machine learning techniques. J Adv Res Dyn Control Syst 30(2):543

    Google Scholar 

  4. Shamrat FMJM, Asaduzzaman M, Rahman AKMS, Tusher RTH, Tasnim Z (2019) A comparative analysis of Parkinson dis- ease prediction using machine learning approaches. Int J Sci Technol Res 8(11):2576–2580. ISSN: 2277-8616

    Google Scholar 

  5. Kamran I, Naz S, Razzak I, Imran M (2021) Handwriting dynamics assessment using deep neural network for early identification of Parkinson’s disease. Future Gener Comput Syst 117:234–244

    Article  Google Scholar 

  6. Nissar I, Rizvi D, Masood S, Mir A (2019) Voice-based detection of Parkinson’s disease through ensemble machine learning approach: A performance study. EAI Endorsed Trans. Pervasive Health Technol 5(19):162806

    Article  Google Scholar 

  7. Berus L, Klancnik S, Brezocnik M, Ficko M (2018) Classifying Parkinson’s disease based on acoustic measures using artificial neural networks. Sensors (Basel) 19(1):16

    Article  Google Scholar 

  8. Abhishek MS, Chethan CR, Aditya CR, Divitha D, Nagaraju TR (2020) Diagnosis of Parkinson’s disorder through speech data using machine learning algorithms. 9(3):2278–3075

    Google Scholar 

  9. Karapinar Senturk Z (2020) Early diagnosis of Parkinson’s disease using machine learning algorithms. Med Hypotheses 138(109603):109603

    Google Scholar 

  10. Canter GJ (1963) Speech characteristics of patients with Parkinson’s disease: I. intensity, pitch, and duration. J Speech Hear Disord 28:221–229

    Article  Google Scholar 

  11. Rusz J, Cmejla R, Ruzickova H, Ruzicka E (2011) Quantitative acoustic measurements for characterization of speech and voice disorders in early untreated Parkin- son’s disease. J Acoust Soc Am 129(1):350–367

    Article  Google Scholar 

  12. Hartelius L, Svensson P (1994) Speech and swallowing symptoms associated with Parkinson’s disease and multiple sclerosis: a survey. Folia Phoniatr Logop 46(1):9–17

    Google Scholar 

  13. Metter EJ, Hanson WR (1986) Clinical and acoustical variability in hypokinetic dysarthria. J Commun Disord 19(5):347–366

    Google Scholar 

  14. Baker KK, Ramig LO, Luschei ES, Smith ME (1998) Thyroarytenoid muscle activity associated with hypophonia in Parkinson disease and aging. Neurology 51(6):1592–1598

    Article  Google Scholar 

  15. Roy N, Nissen SL, Dromey C, Sapir S (2009) Articulatory changes in muscle tension dysphonia: evidence of vowel space expansion following manual circumlaryngeal therapy. J Commun Disord 42(2):124–135

    Article  Google Scholar 

  16. Holmes RJ, Oates JM, Phyland DJ, Hughes AJ (2000) Voice characteristics in the progression of Parkinson’s disease. Int J Lang Commun Disord 35(3):407–418

    Article  Google Scholar 

  17. Shao J, MacCallum JK, Zhang Y, Sprecher A, Jiang JJ (2010) Acoustic analysis of the tremulous voice: assessing the utility of the correlation dimension and pertur- bation parameters. J. Commun. Disord. 43(1):35–44

    Article  Google Scholar 

  18. Spencer KA, Rogers MA (2005) Speech motor programming in hypokinetic and ataxic dysarthria. Brain Lang. 94(3):347–366

    Article  Google Scholar 

  19. Fletcher SG (1972) Time-by-count measurement of diadochokinetic syllable rate. J Speech Hear Res 15(4):763–770

    Article  Google Scholar 

  20. Skodda S, Rinsche H, Schlegel U (2009) Progression of dysprosody in Parkinson’s disease over time–a longitudinal study. Mov Disord 24(5):716–722

    Article  Google Scholar 

  21. 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)

    Google Scholar 

  22. Goberman AM, Elmer LW (2005) Acoustic analysis of clear versus conversational speech in individuals with Parkinson disease. J. Commun Disord 38(3):215–230

    Article  Google Scholar 

  23. Little M, McSharry P, Hunter E, Spielman J, Ramig L (2008) Suitability of dysphonia measurements for telemonitoring of Parkinson’s disease. Derm Helv

    Google Scholar 

  24. Rahn DA 3rd, Chou M, Jiang JJ, Zhang Y (2007) Phonatory impairment in Parkinson’s disease: evidence from nonlinear dynamic analysis and perturbation analy- sis. J Voice 21(1):64–71

    Article  Google Scholar 

  25. Sapir S, Ramig LO, Spielman JL, Fox C (2010) Formant centralization ratio: a proposal for a new acoustic measure of dysarthric speech. J Speech Lang Hear Res 53(1):114–125

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. Sapir S, Spielman JL, Ramig LO, Story BH, Fox C (2007) Effects of intensive voice treatment (the Lee silverman voice treatment [LSVT]) on vowel articulation in dysarthric individuals with idiopathic Parkinson disease: acoustic and perceptual findings. J Speech Lang Hear Res 50(4):899–912

    Article  Google Scholar 

  28. SaiJayram AKV, Ramasubramanian V, Sreenivas TV (2002) Robust parameters for automatic segmentation of speech. In: IEEE international conference on acoustics speech and signal processing

    Google Scholar 

  29. Murty KSR, Yegnanarayana B (2006) Combining evidence from residual phase and MFCC features for speaker recognition. IEEE Signal Process Lett 13(1):52–55

    Article  Google Scholar 

  30. Borrie SA, McAuliffe MJ, Liss JM (2012) Perceptual learning of dysarthric speech: a review of experimental studies. J Speech Lang Hear Res 55(1):290–305

    Article  Google Scholar 

  31. Ali AMA, Van der Spiegel J, Mueller P (2001) Acoustic-phonetic features for the automatic classification of fricatives. J Acoust Soc Am 109(5):2217–2235

    Google Scholar 

  32. Dusan S, Flanagan JL, Karve A, Balaraman M (2007) Speech compression by polynomial approximation. IEEE Trans Audio Speech Lang Process 15(2):387–395

    Article  Google Scholar 

  33. Vikas, Sharma RK (2014) Early detection of Parkinson’s disease through Voice. In: 2014 international conference on advances in engineering and technology (ICAET)

    Google Scholar 

  34. Adams WR (2017) High-accuracy detection of early Parkinson’s Disease using multiple characteristics of finger movement while typing. PLoS ONE 12(11):e0188226

    Article  Google Scholar 

  35. Hlavnička J, Čmejla R, Tykalová T, Šonka K, Růžička E, Rusz J (2017) Automated analysis of connected speech reveals early biomarkers of Parkinson’s disease in patients with rapid eye movement sleep behaviour disorder. Sci. Rep. 7(1):12

    Article  Google Scholar 

  36. Pahuja G, Nagabhushan TN, Prasad B (2019) Early detection of Parkinson’s disease by using SPECT imaging and biomarkers. J Intell Syst 29(1):1329–1344

    Google Scholar 

  37. Rehman RZU, Del Din S, Guan Y, Yarnall AJ, Shi JQ, Rochester L (2019) Selecting clinically relevant gait characteristics for classification of early parkinson’s disease: a comprehensive machine learning approach. Sci. Rep. 9(1):17269

    Article  Google Scholar 

  38. Caliskan A, Badem H, Basturk A, Yuksel ME (2017) Diagnosis of the Parkinson dis- ease by using deep neural network classifier. IU-J Electr Electron Eng 17:3311–3318

    Google Scholar 

  39. Sakar BE 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 

  40. Hazan H, Hilu D, Manevitz L, Ramig LO, Sapir S (2012) Early diagnosis of Parkinson’s disease via machine learning on speech data. In: 2012 IEEE 27th conven tion of electrical and electronics engineers in Israel

    Google Scholar 

  41. Frid A, Hazan H, Hilu D, Manevitz L, Ramig LO, Sapir S (2014) Computational diagnosis of Parkinson’s disease directly from natural speech using machine learning techniques. In: 2014 IEEE international conference on software science, technology and engineering

    Google Scholar 

  42. Cosi P, Hosoma JP, Valente A (2005) High performance telephone bandwidth speaker independent continuous digit recognition. In: IEEE workshop on automatic speech recognition and understanding, 2001. ASRU ’01

    Google Scholar 

  43. Frid A, Lavner Y (2010) Acoustic-phonetic analysis of fricatives for classification using SVM based algorithm. In: 2010 IEEE 26th convention of electrical and electronics engineers in Israel

    Google Scholar 

  44. Hasegawa-Johnson M, Gunderson J, Perlman A, Huang T (2006) Hmm-based and svm-based recognition of the speech of talkers with spastic dysarthria. In: 2006 IEEE international conference on acoustics speed and signal processing proceedings

    Google Scholar 

  45. Caballero Morales SO, Cox SJ (2009) Modelling errors in automatic speech recognition for dysarthric speakers. EURASIP J Adv Signal Process 2009(1)

    Google Scholar 

  46. Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE Inst Electr Electron Eng 77(2):257–286

    Google Scholar 

  47. 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 

  48. Sakar BE, Serbes G, Sakar CO (2017) Analyzing the effectiveness of vocal features in early telediagnosis of Parkinson’s disease. PLoS One 12(8):e0182428

    Article  Google Scholar 

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

    Article  Google Scholar 

  50. Benba A, Jilbab A, Hammouch A, Sandabad S (2015) Voiceprints analysis using MFCC and SVM for detecting patients with Parkinson’s disease. In: 2015 interna tional conference on electrical and information technologies (ICEIT)

    Google Scholar 

  51. Anila M, Laksmaiah K (2020) Education foundation. A review on Parkinson’s disease diagnosis using machine learning techniques. Int J Eng Res Technol (Ahmedabad) V9(06)

    Google Scholar 

  52. Wang W, Lee J, Harrou F, Sun Y (2020) Early detection of Parkinson’s disease using deep learning and machine learning. IEEE Access 8:147635–147646

    Article  Google Scholar 

  53. Perez KS, Ramig LO, Smith ME, Dromey C (1996) The Parkinson larynx: trem- or and videostroboscopic findings. J Voice 10(4):354–361

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Electronics Engineering, VIT, Vellore, India

    Rani Kumari & Prakash Ramachandran

Authors
  1. Rani Kumari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Prakash Ramachandran
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Rani Kumari .

Editor information

Editors and Affiliations

  1. School of Electronics Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India

    N. Subhashini

  2. Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman, Petaling Jaya, Malaysia

    Morris. A. G. Ezra

  3. Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology (NTUST), Taipei, Taiwan

    Shien-Kuei Liaw

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Kumari, R., Ramachandran, P. (2023). A Review on Early Diagnosis of Parkinson’s Disease Using Speech Signal Parameters Based on Machine Learning Technique. In: Subhashini, N., Ezra, M.A.G., Liaw, SK. (eds) Futuristic Communication and Network Technologies. Lecture Notes in Electrical Engineering, vol 966. Springer, Singapore. https://doi.org/10.1007/978-981-19-8338-2_18

Download citation

  • DOI: https://doi.org/10.1007/978-981-19-8338-2_18

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-8337-5

  • Online ISBN: 978-981-19-8338-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation