Multi-Variate vocal data analysis for Detection of Parkinson disease using Deep Learning

Machine learning (ML) and Deep learning (DL) methods are differently implemented with various decision-making abilities. Particularly, the usage of ML and DL techniques in disease detection is inevitable in the near future. This work uses the ability of acoustic-based DL techniques for detecting Parkinson disease symptoms. This disease can be identified by many DL techniques such as deep knowledge creation networks and recurrent networks. The proposed Deep Multi-Variate Vocal Data Analysis (DMVDA) System has been designed and implemented using Acoustic Deep Neural Network (ADNN), Acoustic Deep Recurrent Neural Network (ADRNN), and Acoustic Deep Convolutional Neural Network (ADCNN). Further, DMVDA has been specially developed with absolute multi-variate speech attribute processing algorithm for effective value creation. In order to improve the benefits of this speech-processing algorithm, the DMVDA has acoustic data sampling procedures. The DL techniques introduced in this work helps to identify Parkinson symptoms by analyzing the heterogeneous dataset. The integration of these techniques produces nominal 3% increase in the performance than the existing techniques.

Automated summary

The study utilized deep learning techniques for detecting Parkinson's disease symptoms, including the use of acoustic deep neural network, deep recurrent neural network, and deep convolutional neural network. By using specially developed algorithms, the efficiency of speech processing was successfully improved.