The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Nonlinear speech analysis algorithms mapped to a standard metric achieve clinically useful quantification of average Parkinson's disease symptom severity
Open accessAbstract
The standard reference clinical score quantifying average Parkinson's disease (PD) symptom severity is the Unified Parkinson's Disease Rating Scale (UPDRS). At present, UPDRS is determined by the subjective clinical evaluation of the patient's ability to adequately cope with a range of tasks. In this study, we extend recent findings that UPDRS can be objectively assessed to clinically useful accuracy using simple, self-administered speech tests, without requiring the patient's physical presence in the clinic. We apply a wide range of known speech signal processing algorithms to a large database (approx. 6000 recordings from 42 PD patients, recruited to a six-month, multi-centre trial) and propose a number of novel, nonlinear signal processing algorithms which reveal pathological characteristics in PD more accurately than existing approaches. Robust feature selection algorithms select the optimal subset of these algorithms, which is fed into non-parametric regression and classification algorithms, mapping the signal processing algorithm outputs to UPDRS. We demonstrate rapid, accurate replication of the UPDRS assessment with clinically useful accuracy (about 2 UPDRS points difference from the clinicians' estimates,p< 0.001). This study supports the viability of frequent, remote, cost-effective, objective, accurate UPDRS telemonitoring based on self-administered speech tests. This technology could facilitate large-scale clinical trials into novel PD treatments.
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Cites
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Novel Speech Signal Processing Algorithms for High-Accuracy Classification of Parkinson’s Disease
Tsanas, A., Little, M. A., McSharry, P. E., Spielman, J., & Ramig, L. O. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2183367 · Cited by 732
Self-citationDetecting and monitoring the symptoms of Parkinson's disease using smartphones: A pilot study
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Self-citationSmith, K. M., & Caplan, D. N. (2018) · Brain and Language · doi:10.1016/j.bandl.2018.08.002 · Cited by 144
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Metadata
- Authors
- Tsanas, A., Little, M. A., McSharry, P. E., & Ramig, L. O.
- Venue
- Journal of The Royal Society Interface
- Publisher
- The Royal Society
- Published
- 2010-11-17
- Volume
- 8
- Issue
- 59
- Pages
- 842-855
- ISSN
- 1742-5662 (ISSN-L)
- 1742-5689
- OpenAlex
- W2170517798
- Cited by
- 323
- References
- 6
- FWCI
- 5.14
- Type
- journal article
- Language
- en
- Open access
- bronze
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