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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 healt h y c ontrol , possible stage , probable stage and definite stage . These stage s are based on Hughes scale used in Indonesia for PD. Voice signals were recorded from 1 5 people with Parkinson (PWP) and 8 healt h y control subjects. Voice and EMG data acquistion were conducted in d r 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 result s of voice classification show that accuracy for testing step of 9 4.4 %. For EMG classification, the accuracy of testing of 71%.

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