An intelligent healthcare system for detection and classification to discriminate vocal fold disorders

Zulfiqar Ali, M. Shamim Hossain, Ghulam Muhammad, Arun Kumar Sangaiah

Research output: Contribution to journalArticlepeer-review

71 Citations (Scopus)
62 Downloads (Pure)


The growing population of senior citizens around the world will appear as a big challenge in the future and they will engage a significant portion of the healthcare facilities. Therefore, it is necessary to develop intelligent healthcare systems so that they can be deployed in smart homes and cities for remote diagnosis. To overcome the problem, an intelligent healthcare system is proposed in this study. The proposed intelligent system is based on the human auditory mechanism and capable of detection and classification of various types of the vocal fold disorders. In the proposed system, critical bandwidth phenomena by using the bandpass filters spaced over Bark scale is implemented to simulate the human auditory mechanism. Therefore, the system acts like an expert clinician who can evaluate the voice of a patient by auditory perception. The experimental results show that the proposed system can detect the pathology with an accuracy of 99.72%. Moreover, the classification accuracy for vocal fold polyp, keratosis, vocal fold paralysis, vocal fold nodules, and adductor spasmodic dysphonia is 97.54%, 99.08%, 96.75%, 98.65%, 95.83%, and 95.83%, respectively. In addition, an experiment for paralysis versus all other disorders is also conducted, and an accuracy of 99.13% is achieved. The results show that the proposed system is accurate and reliable in vocal fold disorder assessment and can be deployed successfully for remote diagnosis. Moreover, the performance of the proposed system is better as compared to existing disorder assessment systems.

Original languageEnglish
Pages (from-to)19-28
Number of pages10
JournalFuture Generation Computer Systems
Early online date5 Mar 2018
Publication statusPublished (in print/issue) - 31 Aug 2018


  • Auditory perception
  • Binary classification
  • Critical bands
  • Healthcare
  • Vocal fold disorders


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