The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings
Abstract
There has been an increased interest in speech pattern analysis applications of Parkinsonism for building predictive telediagnosis and telemonitoring models. For this purpose, we have collected a wide variety of voice samples, including sustained vowels, words, and sentences compiled from a set of speaking exercises for people with Parkinson's disease. There are two main issues in learning from such a dataset that consists of multiple speech recordings per subject: 1) How predictive these various types, e.g., sustained vowels versus words, of voice samples are in Parkinson's disease (PD) diagnosis? 2) How well the central tendency and dispersion metrics serve as representatives of all sample recordings of a subject? In this paper, investigating our Parkinson dataset using well-known machine learning tools, as reported in the literature, sustained vowels are found to carry more PD-discriminative information. We have also found that rather than using each voice recording of each subject as an independent data sample, representing the samples of a subject with central tendency and dispersion metrics improves generalization of the predictive model.
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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 · cited 1×
“PARKINSON’s disease (PD) is a neurodegenerative disorder of central nervous system that causes partial or full loss in motor reflexes, speech, behavior, mental processing, and other vital functions [1].”
Epidemiology of Parkinson's disease
de Lau, L. M., & Breteler, M. M. (2006) · The Lancet Neurology · doi:10.1016/s1474-4422(06)70471-9 · Cited by 4,110
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Loh, H. W., Ooi, C. P., Palmer, E., Barua, P. D., Dogan, S., Tuncer, T., Baygin, M., & Acharya, U. R. (2021) · Electronics · doi:10.3390/electronics10141740 · Cited by 144
Detecting the impact of subject characteristics on machine learning-based diagnostic applications
Chaibub Neto, E., Pratap, A., Perumal, T. M., Tummalacherla, M., Snyder, P., Bot, B. M., Trister, A. D., Friend, S. H., Mangravite, L., & Omberg, L. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0178-x · Cited by 83
Hecker, P., Steckhan, N., Eyben, F., Schuller, B. W., & Arnrich, B. (2022) · Frontiers in Digital Health · doi:10.3389/fdgth.2022.842301 · Cited by 78
Avuçlu, E., & Elen, A. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02260-3 · Cited by 74
Zhang, J., Wu, J., Qiu, Y., Song, A., Li, W., Li, X., & Liu, Y. (2023) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106517 · Cited by 69
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. (. (2015) · International Journal on Electrical Engineering and Informatics · doi:10.15676/ijeei.2015.7.2.10 · Cited by 33
Herath, H. M. D. P. M., Weraniyagoda, W. A. S. A., Rajapaksha, R. T. M., Wijesekara, P. A. D. S. N., Sudheera, K. L. K., & Chong, P. H. J. (2022) · Sensors · doi:10.3390/s22186966 · Cited by 23
Zhang, M., Tang, E., Ding, H., & Zhang, Y. (2024) · Journal of Speech, Language, and Hearing Research · doi:10.1044/2024_jslhr-24-00157 · Cited by 22
Sharing Practices for Datasets Related to Accessibility and Aging
Kamikubo, R., Dwivedi, U., & Kacorri, H. (2021) · Proceedings of the 23rd International ACM SIGACCESS Conference on Computers and Accessibility · doi:10.1145/3441852.3471208 · Cited by 13
Quantifying Device Type and Handedness Biases in a Remote Parkinson's Disease AI‐Powered Assessment
Tumpa, Z. N., Zawad, M. R. S., Sollis, L., Parab, S., Chen, I. Y., & Washington, P. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01934-2 · Cited by 4
Metadata
- Authors
- Sakar, B. E., Isenkul, M. E., Sakar, C. O., Sertbas, A., Gurgen, F., Delil, S., Apaydin, H., & Kursun, O.
- Venue
- IEEE Journal of Biomedical and Health Informatics
- Published
- 2013-07
- Volume
- 17
- Issue
- 4
- Pages
- 828-834
- ISSN
- 2168-2194 (ISSN-L)
- 2168-2208
- OpenAlex
- W2037760741
- Cited by
- 703
- References
- 2
- FWCI
- 8.98
- Type
- journal article
- Language
- en
- Open access
- closed
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