Paper Info
Title
A Belief Rule Base Approach to Support Comparison of Digital Speech Signal Features for Parkinson's Disease Diagnosis
Abstract
Parkinson's disease is a neurological disorder. It affects the structures of the central and peripheral nervous system that control movement. One of the symptoms of Parkinson's disease is difficulty in speaking. Hence, analysis of speech signal of patients may provide valuable features for diagnosing. Previous works on diagnosis based on speech data have employed machine learning and deep learning techniques. However, these approaches do not address the various uncertainties in data. Belief rule based expert system (BRBES) is an approach that can reason under various forms of data uncertainty. Thus, the main objective of this research is to compare the potential of BRBES on various speech signal features of patients of parkinson's disease. The research took into account various types of standard speech signal features such MFCCs, TQWTs etc. A BRBES was trained on a dataset of 188 patients of parkinson's disease and 64 healthy candidates with 5fold cross validation. It was optimized using an exploitive version of the nature inspired optimization algorithm called BRB-based adaptive differential evolution (BRBaDE). The optimized model performed better than explorative BRBaDE, genetic algorithm and MATLAB's FMIN-CON optimization on most of these features. It was also found that for speech based diagnosis of Parkinson's disease under uncertainty, the features such as Glottis Quotient, Jitter variants, MFCCs, RPDE, DFA and PPE are relatively more suitable.
Year
DOI
Venue
2021
10.1007/978-3-030-86993-9_35
BRAIN INFORMATICS, BI 2021
Keywords
DocType
Volume
CNN, Speech emotion, RAVDESS, MFCC, Data augmentation
Conference
12960
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Shafkat Raihan100.34
Sharif Noor Zisad200.34
Raihan Ul Islam3123.72
Mohammad Shahadat Hossain43212.25
Karl Andersson58022.20