Skip to main content
SpringerLink
Log in

An IoT-based smart healthcare system to detect dysphonia

  • S.I. : Healthcare Analytics
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Smart healthcare systems for the internet of things (IoT) platform are cost-efficient and facilitate continuous remote monitoring of patients to avoid unnecessary hospital visits and long waiting times to see practitioners. Presenting a smart healthcare system for the detection of dysphonia can reduce the suffering and pain of patients by providing an initial evaluation of voice. This preliminary feedback of voice could minimize the burden on ENT specialists by referring only genuine cases to them as well as giving an early alarm of potential voice complications to patients. Any possible delay in the treatment and/or inaccurate diagnosis using the subjective nature of tools may lead to severe circumstances for an individual because some types of dysphonia are life-threatening. Therefore, an accurate and reliable smart healthcare system for IoT platform to detect dysphonia is proposed and implemented in this study. Higher-order directional derivatives are used to analyze the time–frequency spectrum of signals in the proposed system. The computed derivatives provide essential and vital information by analyzing the spectrum along different directions to capture the changes that appeared due to malfunctioning the vocal folds. The proposed system provides 99.1% accuracy, while the sensitivity and specificity are 99.4 and 98.1%, respectively. The experimental results showed that the proposed system could provide better classification accuracy than the traditional non-directional first-order derivatives. Hence, the system can be used as a reliable tool for detecting dysphonia and implemented in edge devices to avoid latency issues and protect privacy, unlike cloud processing.

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

Access this article

Log in via an institution

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
Fig. 7

Similar content being viewed by others

References

  1. Yang P, Stankevicius D, Marozas V, Deng Z, Liu E, Lukosevicius A, Dong F, Xu L, Min G (2018) Lifelogging data validation model for internet of things enabled personalized healthcare. IEEE Trans Syst Man Cybern: Syst 48(1):50–64. https://doi.org/10.1109/TSMC.2016.2586075

    Article  Google Scholar 

  2. Guelzim T, Obaidat MS, Sadoun B (2016) Chapter 1-Introduction and overview of key enabling technologies for smart cities and homes. In: Smart cities and homes. morgan kaufmann, Boston, pp 1–16. https://doi.org/10.1016/B978-0-12-803454-5.00001-8

  3. Raza M, Awais M, Singh N, Imran M, Hussain S (2020) Intelligent IoT framework for indoor healthcare monitoring of Parkinson’s disease patient. IEEE J Sel Areas Commun. https://doi.org/10.1109/JSAC.2020.3021571

    Article  Google Scholar 

  4. Dourado CMJM, Silva SPPD, Nóbrega RVMD, Filho PPR, Muhammad K, Albuquerque VHCD (2020) An open IoHT-based deep learning framework for online medical image recognition. IEEE J Sel Areas Commun. https://doi.org/10.1109/JSAC.2020.3020598

    Article  Google Scholar 

  5. Naseer A, Rani M, Naz S, Razzak MI, Imran M, Xu G (2020) Refining Parkinson’s neurological disorder identification through deep transfer learning. Neural Comput Appl 32(3):839–854. https://doi.org/10.1007/s00521-019-04069-0

    Article  Google Scholar 

  6. Ali F, El-Sappagh S, Islam SMR, Ali A, Attique M, Imran M, Kwak K-S (2021) An intelligent healthcare monitoring framework using wearable sensors and social networking data. Future Gener Comput Syst 114:23–43. https://doi.org/10.1016/j.future.2020.07.047

    Article  Google Scholar 

  7. Ali F, El-Sappagh S, Islam SMR, Kwak D, Ali A, Imran M, Kwak K-S (2020) A smart healthcare monitoring system for heart disease prediction based on ensemble deep learning and feature fusion. Inf Fus 63:208–222. https://doi.org/10.1016/j.inffus.2020.06.008

    Article  Google Scholar 

  8. Santos MAG, Munoz R, Olivares R, Filho PPR, Ser JD, Albuquerque VHCd (2020) Online heart monitoring systems on the internet of health things environments: A survey, a reference model and an outlook. Inf Fus 53:222–239. https://doi.org/10.1016/j.inffus.2019.06.004

    Article  Google Scholar 

  9. Ding W, Abdel-Basset M, Eldrandaly KA, Abdel-Fatah L, Albuquerque VHCd (2020) Smart supervision of cardiomyopathy based on fuzzy Harris Hawks optimizer and wearable sensing data optimization: a new model. IEEE Trans Cybern. https://doi.org/10.1109/TCYB.2020.3000440

    Article  Google Scholar 

  10. Muhammad K, Khan S, Ser JD, Albuquerque VHCd (2020) Deep learning for multigrade brain tumor classification in smart healthcare systems: a prospective survey. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.2995800

    Article  Google Scholar 

  11. Rehman A, Naz S, Razzak MI, Akram F, Imran M (2020) A deep learning-based framework for automatic brain tumors classification using transfer learning. Circuits Syst Signal Process 39(2):757–775. https://doi.org/10.1007/s00034-019-01246-3

    Article  Google Scholar 

  12. Razzak MI, Imran M, Xu G (2019) Efficient brain tumor segmentation with multiscale two-pathway-group conventional neural networks. IEEE J Biomed Health Inf 23(5):1911–1919. https://doi.org/10.1109/JBHI.2018.2874033

    Article  Google Scholar 

  13. Ali Z, Muhammad G, Alhamid MF (2017) An automatic health monitoring system for patients suffering from voice complications in smart cities. IEEE Access 5:3900–3908. https://doi.org/10.1109/ACCESS.2017.2680467

    Article  Google Scholar 

  14. Razzak MI, Imran M, Xu G (2020) Big data analytics for preventive medicine. Neural Comput Appl 32(9):4417–4451. https://doi.org/10.1007/s00521-019-04095-y

    Article  Google Scholar 

  15. Hossain MS, Muhammad G, Alamri A (2019) Smart healthcare monitoring: a voice pathology detection paradigm for smart cities. Multimed Syst 25(5):565–575. https://doi.org/10.1007/s00530-017-0561-x

    Article  Google Scholar 

  16. Arias-Londono JD, Gomez-Garcia JA, Godino JI (2019) Multimodal and multi-output deep learning architectures for the automatic assessment of voice quality using the GRB scale. IEEE J Sel Top Signal Process. https://doi.org/10.1109/JSTSP.2019.2956410

    Article  Google Scholar 

  17. The american heritage® stedman’s medical dictionary retrieved MAy 1, 2018 from Dictionary.com website http://dictionary.reference.com/browse/dysphonia.

  18. Mau T (2010) Diagnostic evaluation and management of hoarseness. The Med Clin North Am 94(5):945–960. https://doi.org/10.1016/j.mcna.2010.05.010

    Article  Google Scholar 

  19. Roy N, Merrill RM, Gray SD, Smith EM (2005) Voice Disorders in the General Population: Prevalence, Risk Factors and Occupational Impact. Laryngoscope 115(11):1988–1995. https://doi.org/10.1097/01.mlg.0000179174.32345.41

    Article  Google Scholar 

  20. Roy N, Merrill RM, Thibeault S, Parsa RA, Gray SD, Smith EM (2004) Prevalence of voice disorders in teachers and the general population. J Speech Lang Hear Res 47(2):281–293. https://doi.org/10.1044/1092-4388(2004/023)

    Article  Google Scholar 

  21. Quick Statistics: Voice, Speech and Language. National Institute on Deafness and Other Communication Disorders. http://www.nidcd.nih.gov/health/statistics/vsl/Pages/stats.aspx. Accessed May 01, 2018

  22. Yan Q, Yang R, Huang J Copy-move detection of audio recording with pitch similarity. In: 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 19-April 24, 2015. pp 1782–1786. https://doi.org/10.1109/ICASSP.2015.7178277

  23. Eddins DA, Anand S, Lang A, Shrivastav R (2020) Developing clinically relevant scales of breathy and rough voice quality. J Voice. https://doi.org/10.1016/j.jvoice.2019.12.021

    Article  Google Scholar 

  24. Nemr K, Simoes-Zenari M, Cordeiro GF, Tsuji D, Ogawa AI, Ubrig MT, Menezes MH (2012) GRBAS and Cape-V scales: high reliability and consensus when applied at different times. J Voice: Off J Voice Found 26(6):812.e817-822. https://doi.org/10.1016/j.jvoice.2012.03.005

    Article  Google Scholar 

  25. Thiruvaran T, Ambikairajah E, Epps J, Enzinger E A comparison of single-stage and two-stage modelling approaches for automatic forensic speaker recognition. In: 2013 IEEE 8th international conference on industrial and information systems, 17–20 Dec. 2013. pp 433–438. https://doi.org/10.1109/ICIInfS.2013.6732023

  26. Uloza V, Vegiene A, Saferis V (2015) Correlation between the quantitative video laryngostroboscopic measurements and parameters of multidimensional voice assessment. Biomed Signal Process Control 17:3–10. https://doi.org/10.1016/j.bspc.2014.10.006

    Article  Google Scholar 

  27. Poburka BJ (1999) A new stroboscopy rating form. J Voice 13(3):403–413. https://doi.org/10.1016/S0892-1997(99)80045-9

    Article  Google Scholar 

  28. Rosen CA (2005) Stroboscopy as a research instrument: development of a perceptual evaluation tool. Laryngoscope 115(3):423–428. https://doi.org/10.1097/01.mlg.0000157830.38627.85

    Article  Google Scholar 

  29. Deguchi S, Ishimaru Y, Washio S (2007) Preliminary evaluation of stroboscopy system using multiple light sources for observation of pathological vocal fold oscillatory pattern. Ann Otolog Rhinol Laryngol 116(9):687–694. https://doi.org/10.1177/000348940711600911

    Article  Google Scholar 

  30. Speyer R, Wieneke GH, Kersing W, Dejonckere PH (2005) Accuracy of measurements on digital videostroboscopic images of the vocal folds. Ann Otolog Rhinol Laryngol 114(6):443–450. https://doi.org/10.1177/000348940511400606

    Article  Google Scholar 

  31. Patel R, Dailey S, Bless D (2008) Comparison of high-speed digital imaging with stroboscopy for laryngeal imaging of glottal disorders. Ann Otolog Rhinol Laryngol 117(6):413–424. https://doi.org/10.1177/000348940811700603

    Article  Google Scholar 

  32. Bohr C, Kraeck A, Eysholdt U, Ziethe A, Döllinger M (2013) Quantitative analysis of organic vocal fold pathologies in females by high-speed endoscopy. Laryngoscope 123(7):1686–1693. https://doi.org/10.1002/lary.23783

    Article  Google Scholar 

  33. Manfredi C, Bocchi L, Cantarella G, Peretti G (2012) Videokymographic image processing: objective parameters and user-friendly interface. Biomed Signal Process Control 7(2):192–201. https://doi.org/10.1016/j.bspc.2011.02.007

    Article  Google Scholar 

  34. Krausert CR, Olszewski AE, Taylor LN, McMurray JS, Dailey SH, Jiang JJ (2011) Mucosal wave measurement and visualization techniques. J Voice 25(4):395–405. https://doi.org/10.1016/j.jvoice.2010.02.001

    Article  Google Scholar 

  35. Švec JG, Schutte HK (2012) Kymographic imaging of laryngeal vibrations. Curr Opin Otolaryngol Head Neck Surg 20(6):458–465. https://doi.org/10.1097/MOO.0b013e3283581feb

    Article  Google Scholar 

  36. Woo P (2014) Objective measures of laryngeal imaging: what have we learned since Dr. Paul Moore. J Voice 28(1):69–81. https://doi.org/10.1016/j.jvoice.2013.02.001

    Article  MathSciNet  Google Scholar 

  37. Al-nasheri A, Muhammad G, Alsulaiman M, Ali Z, Mesallam TA, Farahat M, Malki KH, Bencherif MA (2017) An investigation of multidimensional voice program parameters in three different databases for voice pathology detection and classification. J Voice 31(1):113.e119-113.e118. https://doi.org/10.1016/j.jvoice.2016.03.019

    Article  Google Scholar 

  38. Muhammad G, Mesallam TA, Malki KH, Farahat M, Alsulaiman M, Bukhari M (2011) Formant analysis in dysphonic patients and automatic Arabic digit speech recognition. Biomed Eng Online 10(41):1–12. https://doi.org/10.1186/1475-925X-10-41

    Article  Google Scholar 

  39. Kay Elemetric Corp (1993) Multi-dimensional voice program (MDVP) Ver 33. Lincoln Park, NJ

  40. Milenkovic P, Read C (1992) CSpeech version 4 user’s manual. Madison, WI

    Google Scholar 

  41. Boersma P, Weenink D (2001) Praat a system for doing phonetics by computer. Glot Int 5:341–345

    Google Scholar 

  42. Arjmandi MK, Pooyan M, Mikaili M, Vali M, Moqarehzadeh A (2011) Identification of voice disorders using long-time features and support vector machine with different feature reduction methods. J Voice Off J Voice Found 25(6):e275-289. https://doi.org/10.1016/j.jvoice.2010.08.003

    Article  Google Scholar 

  43. Massachusetts Eye & Ear Infirmary Voice & Speech LAB (1994) Disordered voice database model 4337 (Ver. 1.03) Kay Elemetrics Corp, NJ

  44. Peppard RC, Bless DM, Milenkovic P (1988) Comparison of young adult singers and nonsingers with vocal nodules. J Voice 2(3):250–260. https://doi.org/10.1016/S0892-1997(88)80083-3

    Article  Google Scholar 

  45. Lin E, Jiang J, Hanson DG (1998) Glottographic signal perturbation in biomechanically different types of dysphonia. Laryngoscope 108(1 Pt 1):18–25

    Article  Google Scholar 

  46. Rosen CA, Lombard LE, Murry T (2000) Acoustic, aerodynamic and videostroboscopic features of bilateral vocal fold lesions. Ann Otolog Rhinol Laryngol 109(9):823–828. https://doi.org/10.1177/000348940010900907

    Article  Google Scholar 

  47. Ali Z, Elamvazuthi I, Alsulaiman M, Muhammad G (2016a) Detection of voice pathology using fractal dimension in a multiresolution analysis of normal and disordered speech signals. J Med Syst 40(1):20. https://doi.org/10.1007/s10916-015-0392-2

    Article  Google Scholar 

  48. Ali Z, Talha M, Alsulaiman M (2017) A practical approach: design and implementation of a healthcare software for screening of dysphonic patients. IEEE Access 5:5844–5857. https://doi.org/10.1109/ACCESS.2017.2693282

    Article  Google Scholar 

  49. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987. https://doi.org/10.1109/TPAMI.2002.1017623

    Article  MATH  Google Scholar 

  50. Lopez PG, Montresor A, Epema D, Datta A, Higashino T, Iamnitchi A, Barcellos M, Felber P, Riviere E (2015) Edge-centric computing: vision and challenges. SIGCOMM Comput Commun Rev 45(5):37–42. https://doi.org/10.1145/2831347.2831354

    Article  Google Scholar 

  51. Ali Z, Elamvazuthi I, Alsulaiman M, Muhammad G (2016b) Detection of voice pathology using fractal dimension in a multiresolution analysis of normal and disordered speech signals. J Med Syst 40(1):1–10

    Article  Google Scholar 

  52. Ali Z, Imran M, Alsulaiman M, Zia T, Shoaib M (2018) A zero-watermarking algorithm for privacy protection in biomedical signals. Future Gener Comput Syst 82:290–303. https://doi.org/10.1016/j.future.2017.12.007

    Article  Google Scholar 

  53. Markaki M, Stylianou Y (2011) Voice pathology detection and discrimination based on modulation spectral features. IEEE Trans Audio Speech Lang Process 19(7):1938–1948. https://doi.org/10.1109/tasl.2010.2104141

    Article  Google Scholar 

  54. Muhammad G, Ali Z, Alsulaiman M, Almutib K (2014) Vocal fold disorder detection by applying LBP operator on dysphonic speech signal. In: Kijima H (ed) 2nd international conference on intelligent control. Modelling and systems engineering, Cambridge, pp 29–31

    Google Scholar 

  55. Marwan N, Carmen Romano M, Thiel M, Kurths J (2007) Recurrence plots for the analysis of complex systems. Phys Rep 438(5–6):237–329. https://doi.org/10.1016/j.physrep.2006.11.001

    Article  MathSciNet  Google Scholar 

  56. Titze I (1995) Workshop on acoustic voice analysis: summary statement. National center for voice and speech, Denver

    Google Scholar 

Download references

Acknowledgements

The authors extend their appreciation to the Deputyship for Research & Innovation, “Ministry of Education” in Saudi Arabia for funding this research work through the project number IFKSURG-1435-051. The authors thank the Deanship of Scientific Research and RSSU at King Saud University for their technical support.

Author information

Authors and Affiliations

  1. School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK

    Zulfiqar Ali

  2. College of Applied Computer Science, King Saud University, Riyadh, Saudi Arabia

    Muhammad Imran

  3. College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia

    Muhammad Shoaib

Authors
  1. Zulfiqar Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Muhammad Imran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Muhammad Shoaib
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Muhammad Imran.

Ethics declarations

Conflict of interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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

Ali, Z., Imran, M. & Shoaib, M. An IoT-based smart healthcare system to detect dysphonia. Neural Comput & Applic 34, 11255–11265 (2022). https://doi.org/10.1007/s00521-020-05558-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-020-05558-3

Keyword

Search

Navigation