A Review on Early Diagnosis of Parkinson’s Disease Using Speech Signal Parameters Based on Machine Learning Technique

Abstract

Early diagnosis means an individual gets an indication about the disease on his or her own at very early stage of the disease. Today, many people across the globe are suffering from Parkinson’s disease (PD). Early detection of Parkinson’s disease can be a better choice to treat the disease much early. Vocal cord disorder, speech impairments/speech disorders are the early indicators of PD. The initial stage of PD affects the human speech production mechanism. The speech impairments are not apparent to common listeners. We should monitor carefully for the initial stage of PD by using proper expert systems. In this review, we mainly focused on speech signal analysis for the identification of PD with the help of machine learning techniques. The voice sample of affected people from PD can be used in an early detection algorithm using various classification models with different accuracy, sensitivity, specificity, etc. In our review, we found that mainly two types of techniques have been used in this problem (a) conventional feature-based techniques and (b) machine learning-based techniques. The detailed review using these types of algorithms is presented in this paper. In feature-based applications, mel frequency cepstral coefficient (MFCC) and linear predictive coding (LPC) are the mostly used features. Machine learning-based algorithms used intelligent architecture like artificial neural network (ANN), convolution neural network (CNN), hidden Markov model (HMM), XG boost, support vector machine (SVM), etc. It is found that machine learning-based algorithms are doing better in terms of highest accuracy but with some limitations.