Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine

Abstract

Parkinson’s disease (PD) is the most common disease of motor system degeneration that occurs when the dopamine-producing cells are damaged in substantia nigra. To detect PD, various signals have been investigated, including EEG, gait and speech. Since approximately 90 percent of the people with PD suffer from speech disorders, speech analysis is considered as the most common technique for this aim. This paper proposes a new algorithm for diagnosing of Parkinson’s disease based on voice analysis. In the first step, genetic algorithm (GA) is undertaken for selecting optimized features from all extracted features. Afterwards a network based on support vector machine (SVM) is used for classification between healthy and people with Parkinson. The dataset of this research is composed of a range of biomedical voice signals from 31 people, 23 with Parkinson’s disease and 8 healthy people. The subjects were asked to pronounce letter “A” for 3 seconds. 22 linear and non-linear features were extracted from the signals that 14 features were based on F0 (fundamental frequency or pitch), jitter, shimmer and noise to harmonics ratio, which are main factors in voice signal. Because changing in these factors is noticeable for the people with PD, optimized features were selected among them. Of the various numbers of optimized features, the data classification was investigated. Results show that the classification accuracy percent of 94.50 per 4 optimized features, the accuracy percent of 93.66 per 7 optimized features and the accuracy percent of 94.22 per 9 optimized features, could be achieved. It can be observed that the best classification accuracy may be achieved using Fhi (Hz), Fho (Hz), jitter (RAP) and shimmer (APQ5).