High accuracy multilayer autoencoder trained classification method for diagnosis of Parkinson's disease using vocal signals

Dysarthria and dysphonia are two commonly occurring disorders of speech in Parkinson's disease patient, which are observed in approximately 90 % of the disease cases. It has been reported that these disorders give early sign of Parkinson's disease. Hence, effective development of diagnostic tools for detecting early biomarkers can help in controlling the symptoms of disease. In this paper, we have proposed a classification model using Multilayer Autoencoder for feature space reduction. Comparisons among 6 types of classification methods with various feature space sizes were conducted to find out the best performing classification algorithm and feature space size based on accuracy, specificity, and sensitivity. Mel Frequency Cepstral method was used to extract set of features from voice signals of both healthy group and unhealthy group. The model showed promising results in classification when combined with Multi-Layer Autoencoder and SVM classifier.