Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson’s Disease
Open accessAbstract
There has been considerable recent research into the connection between Parkinson's disease (PD) and speech impairment. Recently, a wide range of speech signal processing algorithms (dysphonia measures) aiming to predict PD symptom severity using speech signals have been introduced. In this paper, we test how accurately these novel algorithms can be used to discriminate PD subjects from healthy controls. In total, we compute 132 dysphonia measures from sustained vowels. Then, we select four parsimonious subsets of these dysphonia measures using four feature selection algorithms, and map these feature subsets to a binary classification response using two statistical classifiers: random forests and support vector machines. We use an existing database consisting of 263 samples from 43 subjects, and demonstrate that these new dysphonia measures can outperform state-of-the-art results, reaching almost 99% overall classification accuracy using only ten dysphonia features. We find that some of the recently proposed dysphonia measures complement existing algorithms in maximizing the ability of the classifiers to discriminate healthy controls from PD subjects. We see these results as an important step toward noninvasive diagnostic decision support in PD.
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- Authors
- Tsanas, A., Little, M. A., McSharry, P. E., Spielman, J., & Ramig, L. O.
- Affiliations
- University of Oxford (GB)
- University of Colorado Boulder (US)
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2012-05
- Volume
- 59
- Issue
- 5
- Pages
- 1264-1271
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2084627109
- Semantic Scholar
- 7fc960bb5616e71bfcfba59c39a443f291d20178
- Cited by
- 732
- References
- 47
- Citations by year
- 2026: 50 · 2025: 62 · 2024: 40 · 2023: 68 · 2022: 66 · 2021: 84 · 2020: 75 · 2019: 60 · 2018: 58 · 2017: 41 · 2016: 38 · 2015: 39 · 2014: 23 · 2013: 21 · 2012: 7
- FWCI
- 19.30
- Type
- journal article
- Language
- en
- Open access
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- Aston Publications Explorer (Aston University) (acceptedVersion)
- Funders
- Intel Corporation
- Wellcome Trust (WT090651MF, 090651)
- National Institutes of Health
- Engineering and Physical Sciences Research Council
- National Institute on Deafness and Other Communication Disorders (R01DC001150, R01-DC1150)
- Topics
- Health Sciences › Medicine › Physiology › Voice and Speech Disorders
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Keywords
- Parkinson's disease, speech processing, speech disorders, sustained vowels, feature selection, random forest, support vector machine, binary classification, non-invasive diagnosis, diagnostic decision support
- MeSH terms
- Aged, Aged, 80 and over, Decision Trees, Female, Humans, Male, Middle Aged, Parkinson Disease, Signal Processing, Computer-Assisted, Case-Control Studies, Nonlinear Dynamics, Dysphonia, Support Vector Machine
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