The DOI matches its Crossref record; 11 of the 11 references it cites were found in a registry or an index.
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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Cites
Parkinson's disease: clinical features and diagnosis
Jankovic, J. (2008) · Journal of Neurology, Neurosurgery & Psychiatry · doi:10.1136/jnnp.2007.131045 · Cited by 5,720
Suitability of Dysphonia Measurements for Telemonitoring of Parkinson's DiseasePreprint
Little, M. A., McSharry, P., Hunter, E. J., Spielman, J. L., & Ramig, L. O. (2008) · Nature Precedings · doi:10.1109/tbme.2008.2005954 · Cited by 1,194
Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection
Little, M. A., McSharry, P., Roberts, S. J., Costello, D. A., & Moroz, I. M. (2007) · BioMedical Engineering OnLine · doi:10.1186/1475-925x-6-23 · Cited by 782
Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings
Şakar, B. E., Isenkul, M. E., Sakar, C. O., Sertbaş, A., Gürgen, F. S., Delil, Ş., Apaydın, H., & Kurşun, O. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2245674 · Cited by 703
Burden of illness in Parkinson's disease
Huse, D. M., Schulman, K. L., Orsini, L. S., Castelli‐Haley, J., Kennedy, S., & Lenhart, G. M. (2005) · Movement Disorders · doi:10.1002/mds.20609 · Cited by 335
A systematic review of depression and mental illness preceding Parkinson's disease
Ishihara, L., & Brayne, C. (2006) · Acta Neurologica Scandinavica · doi:10.1111/j.1600-0404.2006.00579.x · Cited by 312
Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder DetectionPreprint
Little, M. A., McSharry, P., Roberts, S., Costello, D. A. E., & Moroz, I. M. (2007) · Nature Precedings · doi:10.1038/npre.2007.326.1 · Cited by 195
Rahn, D. A., Chou, M. J., Jiang, J. J., & Zhang, Y. (2005) · Journal of Voice · doi:10.1016/j.jvoice.2005.08.011 · Cited by 152
Parkinsons disease Diagnosis using Mel frequency Cepstral Coefficients and Vector Quantization
Kapoor, T., & Sharma, R. K. (2011) · International Journal of Computer Applications · doi:10.5120/1821-2393 · Cited by 31
Uma Rani, K., & Holi, M. S. (2013) · doi:10.1109/pht.2013.6461288 · Cited by 24
Research works printed but not linked: 8
- S. B. O'Sullivan, T. J. Schmitz, "Parkinson disease," Physical Rehabilitation, 5th ed. Philadelphia, PA, USA: F. A. Davis Company, 2007, pp. 856-894.2007, pp. 856-894.
- Ch. S. Kumar, P. R. Mallikarjuna, "Design of an automatic speaker recognition system using MFCC, Vector Quantization and LBG algorithm," International Journal on Computer Science and Engineering, Vol. 3, no. 8, 2011.
- S. Young, G. Evermann, T. Hain, D. Kershaw, X. Liu, G. Moore, J. Odell, D. Ollason, D. Povey, V. Valtchev, P. Woodland, "The HTK Book (for HTK Version 3.4)," Copyright. 2001-2006, Cambridge University Engineering Department.
- J. Martinez, H. Perez, E. Escamilla, M. M. Suzuki, "Speaker recognition using mel frequency cepstral coefficients (MFCC) and Vactor Quantization (VQ) techniques," IEEE Electrical Communications and Computers, Cholula, Puebla, Feb 2012, pp. 248-251.
- Achraf BENBA, Abdelilah JILBAB and Ahmed HAMMOUCH. "Voice analysis for detecting persons with Parkinson's disease using MFCC and VQ." The 2014 International Conference on Circuits, Systems and Signal Processing, 2014.
- BENBA, ACHRAF, ABDELILAH JILBAB, and AHMED HAMMOUCH. "VOICE ANALYSIS FOR DETECTING PERSONS WITH PARKINSON'S DISEASE USING PLP AND VQ." Journal of Theoretical & Applied Information Technology70.3 (2014).
- Achraf BENBA, Abdelilah JILBAB and Ahmed HAMMOUCH " Voiceprint analysis using Perceptual Linear Prediction and Support Vector Machines for detecting persons with Parkinson's disease", the 3rd International Conference on Health Science and Biomedical Systems, Florence, Italy, November 22-24 2014.
- Achraf BENBA, Abdelilah JILBAB and Ahmed HAMMOUCH. "Hybridization of best acoustic cues for detecting persons with Parkinson's disease," 2 nd World conference on complex system,Agadir, Morocco, November 10-12 2014.
Cited by
Sorted by citations
Lipsmeier, F., Taylor, K. I., Kilchenmann, T., Wolf, D., Scotland, A., Schjodt‐Eriksen, J., Cheng, W., Fernandez‐Garcia, I., Siebourg‐Polster, J., Jin, L., et al. (2018) · Movement Disorders · doi:10.1002/mds.27376 · Cited by 266
Er, M. B., Işık, E., & Işık, İ. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.103006 · Cited by 114
Benba, A., Jilbab, A., & Hammouch, A. (2016) · International Journal of Speech Technology · doi:10.1007/s10772-016-9338-4 · Cited by 79
Self-citationAutomatic Image Annotation Based on Deep Learning Models: A Systematic Review and Future Challenges
Adnan, M. M., Rahim, M. S. M., Rehman, A. U., Mehmood, Z., Saba, T., & Naqvi, R. A. (2021) · IEEE Access · doi:10.1109/access.2021.3068897 · Cited by 67
Building a Machine-Learning Framework to Remotely Assess Parkinson's Disease Using Smartphones
Chén, O. Y., Lipsmeier, F., Phan, H., Prince, J., Taylor, K. I., Gossens, C., Lindemann, M., & Vos, M. D. (2020) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2020.2988942 · Cited by 59
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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