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Detecting Patients with Parkin son’s disease using Mel Frequency Cepstral Coefficients a nd Support Vector Machines
Open accessAbstract
In order to develop the assessment of speech disorders for detecting patients with Parkinson's disease (PD), we have collected 34 sustained vowel / a /, from 34 subjects including 17 PD patients.We subsequently extracted from 1 to 20 coefficients of the Mel Frequency Cepstral Coefficients (MFCCs) from each individual.To extract the voiceprint from each individual, we compressed the frames by calculating their average value.For classification, we used the Leave-One-Subject-Out (LOSO) validation scheme and the Support Vector Machines (SVMs) with its different types of kernels, (i.e.; RBF, Linear and polynomial).The best classification accuracy achieved was 91.18% using the first 12 coefficients of the MFCCs by Linear kernels SVMs.
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“The following active test features were selected, based on previous literature and their relevancy to PD: (1) Sustained phonation: mel‐frequency cepstral coefficient 2 (MFCC2) 34, 35; (2) Rest tremor: skewness36, 37, 38, 39; (3) Postural tremor: total power40, 41; (4) Finger tapping: intratap variability18, 42; (5) Balance: mean velocity43, 44, 45, 46, 47; and (6) Gait: turn speed46, 48, 49, 50”
From Evaluation of smartphone‐based testing to generate exploratory outcome measures in a phase 1 Parkinson's disease clinical trial (2018) · cites this paper as [34]
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Citations
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Metadata
- Authors
- Laboratoire de recherche en génie électrique, E. N. S. D. L. T. (., Benba, A., Jilbab, A., Laboratoire de recherche en génie électrique, E. N. S. D. L. T. (., Hammouch, A., & Laboratoire de recherche en génie électrique, E. N. S. D. L. T. (.
- Affiliations
- Mohammed V University (MA)
- Corresponding author
- Benba, Achraf
- Venue
- International Journal on Electrical Engineering and Informatics
- Published
- 2015-06-30
- Volume
- 7
- Issue
- 2
- Pages
- 297-307
- ISSN
- 2085-6830 (ISSN-L)
- 2087-5886
- OpenAlex
- W4243783822
- Cited by
- 33
- References
- 11
- Citations by year
- 2025: 2 · 2024: 5 · 2023: 3 · 2022: 6 · 2021: 5 · 2020: 2 · 2019: 2 · 2018: 1 · 2017: 4 · 2016: 3
- FWCI
- 1.96
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- International Journal on Electrical Engineering and Informatics (publishedVersion, cc-by)
- Topics
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
- Keywords
- Parkinson's disease, Mel-frequency cepstral coefficients, support vector machine, voiceprint, sustained vowels, linear kernel, leave-one-subject-out cross-validation
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