Low Cost Parkinson’s Disease Early Detection and Classification Based on Voice and Electromyography Signal

Parkinson’s disease (PD) is one of the health problems concerning for elderly population. Manageable symptom is an important thing for Parkinson’s sufferer in order to be independent enough to do daily activities. As a solution to Parkinson’s early detection method, this research purpose is to develop a low cost diagnostic tool for PD which inexpensive yet accurate and easy to use by neurologist, enriching and giving new insight for neurologist about voice and electromyography (EMG) signal analysis result. It can be very useful for PD clinical evaluation and spreading awareness about PD as well as the important of early diagnose to citizen. Parkinson’s detection method in this research uses pattern recognition method, the first step is initiated with voice and EMG data acquisition. Second step is feature extraction using five features for voice and EMG signal. The last step is classification using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Artificial Neural network (ANN) methods. The pattern recognition of PD is divided in two sections, the first is for two class classification, and the second is four stage classification based on Hughes Scale which commonly used in Indonesia as PD diagnose guideline. Based on the results, voice method classification has higher accuracy than EMG classification because the feature for voice is a good feature which can well classified the voice data. Voice data sampling rate is higher than EMG data sampling rate which means voice data recording has more data each second than EMG data. Two class classification has higher accuracy than four class classification both in ANN and ANFIS. Based on the four class classification results in both of voice and EMG signals using ANN and ANFIS, the probable class has the lowest accuracy of all classes.