Parkinson Disease Detection Based on Voice and EMG Pattern Classification Method for Indonesian Case Study

Authors

  • Farika Putri Mechanical Engineering Department, Faculty of Engineering, Diponegoro University, Semarang, Indonesia
  • Wahyu Caesarendra Mechanical Engineering Department, Faculty of Engineering, Diponegoro University, Semarang, Indonesia http://orcid.org/0000-0002-9784-4204
  • Elta Diah Pamanasari Neurogical Department, Faculty of Medicine, Diponegoro University, Semarang, Indonesia
  • Mochammad Ariyanto Mechanical Engineering Department, Faculty of Engineering, Diponegoro University, Semarang, Indonesia
  • Joga D Setiawan Mechanical Engineering Department, Faculty of Engineering, Diponegoro University, Semarang, Indonesia

DOI:

https://doi.org/10.22219/jemmme.v3i2.6977

Keywords:

Parkinson’s disease (PD), Voice signal, Electromyography (EMG) signal, ANN

Abstract

Parkinson disease (PD) detection using pattern recognition method has been presented in literatures. This paper present multi-class PD detection utilizing voice and electromyography (EMG) features of Indonesian subjects. The multi-class classification consists of healthy control, possible stage, probable stage and definite stage. These stages are based on Hughes scale used in Indonesia for PD. Voice signals were recorded from 15 people with Parkinson (PWP) and 8 healthy control subjects. Voice and EMG data acquistion were conducted in dr Kariadi General Hospital Semarang, Central Java, Indonesia. Twenty two features are used for voice signal feature extraction and twelve features are emploed for EMG signal. Artificial Neural Network is used as classification method. The results of voice classification show that accuracy for testing step of 94.4%. For EMG classification, the accuracy of testing of 71%.

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References

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Published

2018-12-31

How to Cite

Putri, F., Caesarendra, W., Pamanasari, E. D., Ariyanto, M., & Setiawan, J. D. (2018). Parkinson Disease Detection Based on Voice and EMG Pattern Classification Method for Indonesian Case Study. Journal of Energy, Mechanical, Material, and Manufacturing Engineering, 3(2), 87–98. https://doi.org/10.22219/jemmme.v3i2.6977

Issue

Vol. 3 No. 2 (2018)

Section

Articles

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