Optimal Feature Selection and Comparison for Automatic Detection of Parkinson's Disease Using Speech Signal

Azadi, Hamid; Khalil Zade, Mohammad Ali; Akbarzade Toutounchi, Mohammad Reza; Kobravi, Hamid Reza; Rezaei Talab, Fariborz; Ziafati Bagherzade, Seyed Amir; Noei Sarcheshme, Alireza; Shahsavan Pour, Nina

doi:10.22041/ijbme.2017.55213.1174

Document Type : Full Research Paper

Authors

¹ M.Eng,Bioelectric Department, Biomedical Engineering Faculty, Ferdowsi University of biomedical, Mashhad, Iran - Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University, Mashhad, Iran

² Ph.D, Bioelectric Department, Biomedical Engineering Faculty, Azad University, Mashhad, Iran

³ Professor, Bioelectric Department, Biomedical Engineering Faculty, Ferdowsi University, Mashhad, Iran - Center of Excellence on Soft Computing and Intelligent Information Processing, Ferdowsi University, Mashhad, Iran

⁴ M.Eng, Bioelectric Department, Biomedical Engineering Faculty, Azad University, Mashhad, Iran

⁵ M.Eng,Bioelectric Department, Biomedical Engineering Faculty, Ferdowsi University of biomedical, Mashhad, Iran

⁶ M.Eng, Bioelectric Department, Biomedical EngineeringFaculty, Azad University, Mashhad, Iran

https://doi.org/10.22041/ijbme.2017.55213.1174

Abstract

In recent years, researchers have tried hardly to diagnose Parkinson's disease through finding its relation with the patient's speech signal. Also, many studies have been performed on determining the intensity of the disease and its relation with vocal impairment measures. In this paper, we aim to assess and compare the ability of extracting different feature sets from speech signal in order to Parkinson's disease diagnosis. Therefore, 132 features were used to measure vocal impairments from the voice signal of individuals vocalizing phoneme /a/. Then, we used RELIEF feature selection method and applied it to Support Vector Machine (SVM) classifier to choose the best feature of each class. A comparison was made between different feature sets, and finally discrimination percent 95.93 was reached to separate patients from the healthy ones using the combination of selected features. Results obtained from this research can be a very important step toward diagnosing Parkinson's disease non-invasively.

Keywords

Main Subjects

Speech processing

References

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Iranian Journal of Biomedical Engineering

Optimal Feature Selection and Comparison for Automatic Detection of Parkinson's Disease Using Speech Signal

References

References

Volume 10, Issue 1
May 2016
Pages 41-47

Optimal Feature Selection and Comparison for Automatic Detection of Parkinson's Disease Using Speech Signal

References

References

Volume 10, Issue 1May 2016Pages 41-47

Volume 10, Issue 1
May 2016
Pages 41-47