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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Citations
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
Cited by
Parkinson disease prediction using intrinsic mode function based features from speech signal
Karan, B., Sahu, S. S., & Mahto, K. (2020) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2019.05.005 · Cited by 165
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
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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- Sakar, B. E., Isenkul, M. E., Sakar, C. O., Sertbas, A., Gurgen, F., Delil, S., Apaydin, H., & Kursun, O. (2013). Collection and Analysis of a Parkinson Speech Dataset With Multiple Types of Sound Recordings. IEEE Journal of Biomedical and Health Informatics. https://doi.org/10.1109/jbhi.2013.2245674
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