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Multimedia Systems

, Volume 25, Issue 5, pp 565–575 | Cite as

Smart healthcare monitoring: a voice pathology detection paradigm for smart cities

  • M. Shamim HossainEmail author
  • Ghulam Muhammad
  • Atif Alamri
Special Issue Paper

Abstract

With the increasing demand for automated, remote, intelligent, and real-time healthcare services in smart cities, smart healthcare monitoring is necessary to provide improved and complete care to residents. In this monitoring, health-related media or signals collected from smart-devices/objects are transmitted and processed to cater to the need for quality care. However, it is challenging to create a framework or method to handle media-related healthcare data analytics or signals (e.g., voice/audio, video, or electroglottographic (EGG) signals) to meet the complex on-demand healthcare needs for successful smart city management. To this end, this paper proposes a cloud-oriented smart healthcare monitoring framework that interacts with surrounding smart devices, environments, and smart city stakeholders for affordable and accessible healthcare. As a smart city healthcare monitoring case study, a voice pathology detection (VPD) method is proposed. In the proposed method, two types of input, a voice signal and an EGG signal, are used. The input devices are connected to the Internet and the captured signals are transmitted to the cloud. The signals are then processed and classified as either normal or pathologic with a confidence score. These results are passed to registered doctors that make the final decision and take appropriate action. To process the signals, local features are extracted from the first-order derivative of the voice signal, and shape and cepstral features are extracted from the EGG signal. For classification, a Gaussian mixture model-based approach is used. Experimental results show that the proposed method can achieve VPD that is more than 93% accurate.

Keywords

Smart healthcare Smart city Voice pathology detection Healthcare media-cloud 

Notes

Acknowledgements

This work is financially supported by the King Saud University, Deanship of Scientific Research, Research Chair of Pervasive and Mobile Computing.

References

  1. 1.
    Solanas, et al.: Smart health: a context aware health paradigm within smart cities. IEEE Commun. Mag. 52(8), 74–81 (2014)CrossRefGoogle Scholar
  2. 2.
    Patsakis, R., Venanzio, P., Bellavista, A,. Solanas and Bouroche, M.: Personalized medical services using smart cities’ infrastructures. In: Proc. IEEE MeMeA’14, Lisboa, 2014, pp. 1–5Google Scholar
  3. 3.
    Olshansky, S.J., et al.: The future of smart health. Computer 49(11), 14–21 (2016)CrossRefGoogle Scholar
  4. 4.
    Park, C., et al.: M2 M-based smart health service for human UI/UX using motion recognition. Cluster Comput. 18, 221–232 (2015)CrossRefGoogle Scholar
  5. 5.
    Hossain, M.S.: Patient status monitoring for smart home healthcare. In: IEEE ICME 2016, Seattle, USA, July 11–15, 2016. doi: 10.1109/ICMEW.2016.7574719
  6. 6.
    Chen, M., Ma, Y., Song, J., Lai, C., Hu, B.: Smart clothing: connecting human with clouds and big data for sustainable health monitoring. Mob. Netw. Appl. 21(5), 825–845 (2016)CrossRefGoogle Scholar
  7. 7.
    Zanella, A., et al.: Internet of things for smart cities. IEEE Internet Things J. 1(1), 22–32 (2014)CrossRefGoogle Scholar
  8. 8.
    Hossain, M.S., Muhammad, G.: Cloud-assisted industrial internet of things (IIoT)-enabled framework for health monitoring. Computer Netw 101(2016), 192–202 (2016)CrossRefGoogle Scholar
  9. 9.
    Black, L.I., Vahratian, A., Hoffman, H.J.: Communication disorders and use of intervention services among children aged 3–17 years: United States, 2012. NCHS data brief, no 205. National Center for Health Statistics, Hyattsville (2015)Google Scholar
  10. 10.
    Bhattacharyya, N.: The prevalence of voice problems among adults in the United States. Laryngoscope. 124(10), 2359–2362 (2014)CrossRefGoogle Scholar
  11. 11.
    Muhammad, G., et al.: Enhanced living by assessing voice pathology using co-occurrence matrix. Sensors. 17(2), 267 (2017). doi: 10.3390/s17020267 CrossRefGoogle Scholar
  12. 12.
    McNeil, C.: Smart integrated biodiagnostic systems for healthcare. E-Health. Jan. 2010; http://www.smarthealthip.com/docs/MonthlyFocus_EC_201001_SmartHEALTH_final.pdf. Accessed on 31 Jan 2017
  13. 13.
    Cisco Smart Healthcare Facility Solutions http://www.cisco.com/c/en_in/solutions/industries/healthcare/smart-healthcare-facility.html; Accessed on 31 January 2017
  14. 14.
    Demirkan, H.: A smart healthcare systems framework. IT Prof. 15(5), 38–45 (2013)CrossRefGoogle Scholar
  15. 15.
    Sajjad, M., et al.: Leukocytes classification and segmentation in microscopic blood smear: a resource-aware healthcare service in smart cities. IEEE Access (2016). doi: 10.1109/ACCESS.2016.2636218 CrossRefGoogle Scholar
  16. 16.
    Liu, S., Li, W., Liu, K.: Pragmatic oriented data interoperability for smart healthcare information systems. In: Proc. 14th IEEE/ACM international symposium on cluster, cloud and grid computing, Chicago, IL, 2014, pp. 811–818Google Scholar
  17. 17.
    Banerjee, A., Gupta, S.K.S.: Analysis of smart mobile applications for healthcare under dynamic context changes. IEEE Trans. Mob. Comput. 14(5), 904–919 (2015)CrossRefGoogle Scholar
  18. 18.
    Samani, H., Zhu, R.: Robotic automated external defibrillator ambulance for emergency medical service in smart cities. IEEE Access. 4(2016), 268–283 (2016)CrossRefGoogle Scholar
  19. 19.
    Piniewski, B., et al.: Empowering healthcare patients with smart technology. Computer 43(7), 27–34 (2010)CrossRefGoogle Scholar
  20. 20.
    Hossain, M.S.: Cloud-supported cyber-physical framework for patients monitoring. IEEE Syst. J. 11(1), 118–127 (2017)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Catarinucci, L., et al.: An IoT-aware architecture for smart healthcare systems. IEEE Internet Things J 2(6), 515–526 (2015)CrossRefGoogle Scholar
  22. 22.
    Hassan, M.M., Albakr, H.S., Al-Dossari, H.: A cloud-assisted internet of things framework for pervasive healthcare in smart city environment. In: Proc. ACM EMASC ‘14, Orlando, Florida, USA, Nov. 3–27, 2014Google Scholar
  23. 23.
    Laplante, A., et al.: Caring: an undiscovered “Superility” of smart healthcare. IEEE Softw. 33(6), 16–19 (2016)CrossRefGoogle Scholar
  24. 24.
    Al-nasheri, A., et al.: An investigation of multi-dimensional voice program parameters in three different databases for voice pathology detection and classification. J. Voice 31(1), 113e.9–113e.18 (2017)CrossRefGoogle Scholar
  25. 25.
    Muhammad, G., et al.: Voice pathology detection using interlaced derivative pattern on glottal source excitation. Biomed. Signal Process. Control 31(2017), 156–164 (2017)CrossRefGoogle Scholar
  26. 26.
    Ali, Z., Elamvazuthi, I., Alsulaiman, M., Muhammad, G.: Automatic voice pathology detection with running speech by using estimation of auditory spectrum and cepstral coefficients based on the all-pole model. J. Voice 30(6), 757.e7–757.e19 (2016)CrossRefGoogle Scholar
  27. 27.
    Kitzing, P., Maier, A., Åhlander, V.L.: Automatic speech recognition (ASR) and its use as a tool for assessment or therapy of voice, speech, and language disorders. Logop. Phonatrics Vocol. 34(2009), 91–96 (2009)CrossRefGoogle Scholar
  28. 28.
    Muhammad, G.: Automatic speech recognition using interlaced derivative pattern for cloud based healthcare system. Cluster Comput. 18(2), 795–802 (2015)MathSciNetCrossRefGoogle Scholar
  29. 29.
    Lieberman, P.: Perturbations in vocal pitch. J. Acoust. Soc. Am. 33(5), 597–603 (1961)CrossRefGoogle Scholar
  30. 30.
    Yumoto, E., Gould, W.J., Baer, T.: Harmonics-to-noise ratio as an index of the degree of hoarseness. J. Acoust. Soc. Am. 71(1982), 1544–1550 (1982)CrossRefGoogle Scholar
  31. 31.
    Heman-Ackah, Y.D., et al.: Cepstral peak prominence: a more reliable measure of dysphonia. Ann. Otol. Rhinol. Laryngol. 112(2003), 324–333 (2003)CrossRefGoogle Scholar
  32. 32.
    Leonardo, A., et al.: Analysis and classification of voice pathologies using glottal signal parameters. J. Voice 30(5), 549–556 (2016)CrossRefGoogle Scholar
  33. 33.
    Alku, P.: Glottal wave analysis with pitch synchronous iterative adaptive inverse filtering. Speech Commun. 11(2–3), 109–118 (1992)CrossRefGoogle Scholar
  34. 34.
    Markaki, M., Stylianou, Y.: Voice pathology detection and discrimination based on modulation spectral features. IEEE Trans. Audio Speech Lang. Process. 19, 1938–1948 (2011)CrossRefGoogle Scholar
  35. 35.
    Arjmandi, M.K., Pooyan, M., Mikaili, M., Vali, M., Moqarehzadeh, A.: Identification of voice disorders using long-time features and support vector machine with different feature reduction methods. J. Voice 25, e275–e289 (2011)CrossRefGoogle Scholar
  36. 36.
    Amami, R., Smiti, A.: An incremental method combining density clustering and support vector machines for voice pathology detection. Comput. Electr. Eng. (2016). doi: 10.1016/j.compeleceng.2016.08.021 CrossRefGoogle Scholar
  37. 37.
    Kay Elemetrics Corp., Disordered Voice Database”, Version 1.03 (CD-ROM), October 1994, MEEI, Voice and Speech Lab, Boston, MAGoogle Scholar
  38. 38.
    Muhammad, G., Rahman, S.K.M., Alelaiwi, A., Alamri, A.: Smart health solution integrating IoT and cloud: a case study of voice pathology monitoring. IEEE Commun. Mag. 55(1), 69–73 (2017)CrossRefGoogle Scholar
  39. 39.
    Hossain, M.S., Muhammad, G.: Healthcare big data voice pathology assessment framework. IEEE Access 4(1), 7806–7815 (2016)CrossRefGoogle Scholar
  40. 40.
    Selamtzisa, A., Ternstrom, S.: Analysis of vibratory states in phonation using spectral features of the electroglottographic signal. J. Acous. Soc. Am. 136(5), 2773–2783 (2014)CrossRefGoogle Scholar
  41. 41.
    Hossain, M.S., et al.: Audio–visual emotion-aware cloud gaming framework. IEEE Trans. Circuits Syst. Video Technol. 25(12), 2105–2118 (2015)CrossRefGoogle Scholar
  42. 42.
    Barry, W.J., Putzer, M.: Saarbrucken Voice Database, Institute of Phonetics, University of Saarland. http://www.stimmdatenbank.coli.uni-saarland.de/; Accessed 31 Jan 2017
  43. 43.
    Kay Elemetrics, Multi-dimensional voice program (MDVP) (computer program), 2012Google Scholar

Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • M. Shamim Hossain
    • 1
    • 3
    Email author
  • Ghulam Muhammad
    • 2
  • Atif Alamri
    • 1
    • 3
  1. 1.Department of Software Engineering, College of Computer and Information Sciences (CCIS)King Saud UniversityRiyadhSaudi Arabia
  2. 2.Department of Computer Engineering, College of Computer and Information Sciences (CCIS)King Saud UniversityRiyadhSaudi Arabia
  3. 3.Research Chair of Pervasive and Mobile ComputingKing Saud UniversityRiyadhSaudi Arabia

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