The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Oropharyngeal dysphagia identification using wavelets and optimum path forest
Open accessAbstract
The swallowing disturbers are defined as oropharyngeal dysphagia when present specifies signals and symptoms that are characterized for alterations in any phases of swallowing. Early diagnosis is crucial for the prognosis of patients with dysphagia and the potential to diagnose dysphagia in a noninvasive manner by assessing the sounds of swallowing is a highly attractive option for the dysphagia clinician. This study proposes a new framework for oropharyngeal dysphagia identification, having two main contributions: a new set of features extract from swallowing signal by discrete wavelet transform and the dysphagia classification by a novel pattern classifier called OPF. We also employed the well known SVM algorithm in the dysphagia identification task, for comparison purposes. We performed the experiments in two sub-signals: the first was the moment of the maximal peak (MP) of the signal and the second is the swallowing apnea period (SAP). The OPF final accuracy obtained were 85.2% and 80.2% for the analyzed signals MP and SAP, respectively, outperforming the SVM results.
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“Optimum path forest (OPF) is considered one of the graph-based classifiers quite similar to support vector machine (SVM) techniques but has a faster execution time and is easier to deploy [13].”
From Embedded Intelligence on FPGA: Survey, Applications and Challenges (2021) · cites this paper as [13]
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Research works printed but not linked: 5
- papa, 2007, design of robust pattern classifiers based on pptimum-path forests, Mathematical Morphology and its Applications to Signal and Image Processing (ISMM), 337
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Embedded Intelligence on FPGA: Survey, Applications and Challenges
Seng, K. P., Lee, P. J., & Ang, L. M. (2021) · Electronics · doi:10.3390/electronics10080895 · Cited by 105
“Optimum path forest (OPF) is considered one of the graph-based classifiers quite similar to support vector machine (SVM) techniques but has a faster execution time and is easier to deploy [13].”
BackgroundDiniz, W. F. S., Fremont, V., Fantoni, I., & Nóbrega, E. G. O. (2017) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2017.06.013 · Cited by 11
Metadata
- Authors
- Spadotto, A. A., Pereira, J. C., Guido, R. C., Papa, J. P., Falcao, A. X., Gatto, A. R., Cola, P. C., & Schelp, A. O.
- Affiliations
- Universidade de São Paulo (BR)
- Universidade Estadual Paulista (Unesp) (BR)
- Venue
- 2008 3rd International Symposium on Communications, Control and Signal Processing
- Publisher
- IEEE
- Published
- 2008-03
- Pages
- 735-740
- OpenAlex
- W2168717215
- Cited by
- 12
- References
- 30
- Citations by year
- 2025: 1 · 2022: 1 · 2021: 2 · 2017: 1 · 2015: 1 · 2012: 1
- FWCI
- 9.91
- Type
- conference paper
- Language
- en
- Open access
- green
- hdl.handle.net
- Licence
- other-oa
- Locations
- International Symposium on Communications, Control and Signal Processing (publishedVersion)
- Acervo Digital da Universidade Estadual Paulista (Universidade Estadual Paulista) (submittedVersion)
- UNESP Institutional Repository (São Paulo State University) (submittedVersion, other-oa)
- Topics
- Health Sciences › Health Professions › Speech and Hearing › Dysphagia Assessment and Management
- Health Sciences › Health Professions › Speech and Hearing › Voice and Speech Disorders
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- oropharyngeal dysphagia, swallowing sounds, discrete wavelet transform, optimum-path forest, support vector machine comparison, non-invasive diagnosis
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