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Elta Diah Pamanasari
Neurogical Department, Faculty of Medicine, Diponegoro University, Semarang, Indonesia
Mechanical Engineering

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Parkinson Disease Detection Based on Voice and EMG Pattern Classification Method for Indonesian Case Study Farika Putri; Wahyu Caesarendra; Elta Diah Pamanasari; Mochammad Ariyanto; Joga D Setiawan
JEMMME (Journal of Energy, Mechanical, Material, and Manufacturing Engineering) Vol. 3 No. 2 (2018)
Publisher : University of Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jemmme.v3i2.6977

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%.
Co-Authors Farika Putri Joga D Setiawan Mochammad Ariyanto Wahyu Caesarendra