Classification of Parkinson's disease patients based on spectrogram using local binary pattern descriptors
| datacite.rights | http://purl.org/coar/access_right/c_abf2 | spa |
| dc.contributor.author | Gelvez-Almeida, E | |
| dc.contributor.author | Váasquez-Coronel, A | |
| dc.contributor.author | Guatelli, R | |
| dc.contributor.author | Aubin, V | |
| dc.contributor.author | Mora, M | |
| dc.date.accessioned | 2023-08-18T14:29:01Z | |
| dc.date.available | 2023-08-18T14:29:01Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | Extreme learning machine is an algorithm that has shown a good performance facing classi cation and regression problems. It has gained great acceptance by the scienti c community due to the simplicity of the model and its sola great generalization capacity. This work proposes the use of extreme learning machine neural networks to carry out the classi cation between Parkinson's disease patients and healthy individuals. The descriptor used corresponds to the feature vector generated applying the local binary Pattern algorithm to the grayscale spectrograms. The spectrograms are obtained from the audio signal samples from the considered repository. Experiments are conducted with single hidden layer and multilayer extreme learning machine networks comparing the results of each structure. Results show that hierarchical extreme learning machine with three hidden layers has a better general performance over multilayer extreme learning machine networks and a single hidden layer extreme learning machine. The rate of success obtained is within the ranges presented in the literature. However, the hierarchical network training time is considerably faster compared to multilayer networks of three or two hidden layers. | spa |
| dc.format.mimetype | spa | |
| dc.identifier.citation | E Gelvez-Almeida et al 2022 J. Phys.: Conf. Ser. 2153 012014 | spa |
| dc.identifier.doi | https://doi.org/10.1088/1742-6596/2153/1/012014 | |
| dc.identifier.issn | 17426596 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12442/13159 | |
| dc.language.iso | eng | spa |
| dc.publisher | IOP Publishing | spa |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | eng |
| dc.rights.accessrights | info:eu-repo/semantics/openAccess | spa |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |
| dc.subject | Parkinson's disease patients | spa |
| dc.subject | Local binary pattern descriptors | spa |
| dc.subject | Extreme learning machine | spa |
| dc.title | Classification of Parkinson's disease patients based on spectrogram using local binary pattern descriptors | spa |
| dc.type.driver | info:eu-repo/semantics/article | spa |
| dc.type.spa | Artículo científico | spa |
| dcterms.references | Logemann J A, Fisher H B, Boshes B, Blonsky E R 1978 Frequency and cooccurrence of vocal tract dysfunctions in the speech of a large sample of Parkinson patients Journal of Speech and hearing Disorders 43(1) 47 | eng |
| dcterms.references | Arora S, Baghai-Ravary L, Tsanas A 2019 Developing a large scale population screening tool for the assessment of Parkinson's disease using telephone-quality voice The Journal of the Acoustical Society of America 145(5) 2871 | eng |
| dcterms.references | 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 17(4) 828 | eng |
| dcterms.references | Wodzinski M, Skalski A, Hemmerling D, Orozco-Arroyave J R, N oth E 2019 Deep learning approach to Parkinson's disease detection using voice recordings and convolutional neural network dedicated to image classi cation 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (Berlin: IEEE) p 717 | eng |
| dcterms.references | Orozco-Arroyave J R, Arias-Londo~no J D, Vargas-Bonilla J F, Gonzalez-Rativa M C, N oth E 2014 New Spanish speech corpus database for the analysis of people su ering from Parkinson's disease Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14) (Reykjavik: European Language Resources Association) p 342 | eng |
| dcterms.references | Zahid L, Maqsood M, Durrani M Y, Bakhtyar M, Baber J, Jamal H, Mehmood I, Song O Y 2020 A spectrogram-based deep feature assisted computer-aided diagnostic system for Parkinson's disease IEEE Access 8 35482 | eng |
| dcterms.references | Trinh N, Darragh O 2019 Pathological speech classi cation using a convolutional neural network Irish Machine Vision and Image Processing (IMVIP 2019) (Dublin: Technological University Dublin) p 72 | eng |
| dcterms.references | Huang G B, Zhu Q Y, Siew C K 2004 Extreme learning machine: a new learning scheme of feedforward neural networks International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541) (Budapest: IEEE) p 985 | eng |
| dcterms.references | Giuliano M, Perez S N, Maldonado M, Bondar P, Linari D, Adamec D A, Debas M I, Morales C M, Le on L, Yaco A Y, Birelli J B, Mart nez R M, Lacaze M L, Gurlekian J A 2021 Construction of a Parkinson's voice database International Conference on Industrial Engineering and Operations Management (Sao Paulo: IEOM Society International) p 940 | eng |
| dcterms.references | Greenberg S, Kingsbury B E 1997 The modulation spectrogram: in pursuit of an invariant representation of speech International Conference on Acoustics, Speech, and Signal Processing (Munich: IEEE) p 1647 | eng |
| dcterms.references | Kingsbury B E, Morgan N, Greenberg S 1998 Robust speech recognition using the modulation spectrogram Speech Communication 24 117 | eng |
| dcterms.references | Pietik ainen M 2005 Image analysis with local binary patterns Scandinavian Conference on Image Analysis (SCIA 2005) (Berlin: Springer) p 115 | eng |
| dcterms.references | Huang G B, Zhu Q Y, Siew C K 2006 Extreme learning machine: theory and applications Neurocomputing 70(1-3) 489 | eng |
| dcterms.references | Ding S, Zhao H, Zhang Y, Xu X, Nie R 2015 Extreme learning machine: algorithm, theory and applications Arti cial Intelligence Review 44(1) 103 | eng |
| dcterms.references | Barata J C A, Hussein M S 2012 The Moore-Penrose pseudoinverse: a tutorial review of the theory Brazilian Journal of Physics 42(1-2) 146 | eng |
| dcterms.references | Chamara L, Zhou H, Huang G B, Vong C M 2013 Representational learning with extreme learning machine for big data IEEE Intelligent Systems 28(6) 31 | eng |
| dcterms.references | Tang J, Deng C, Huang G B 2015 Extreme learning machine for multilayer perceptron IEEE Transactions on Neural Networks and Learning Systems 27(4) 809 | eng |
| dcterms.references | Beck A, Teboulle M 2009 A fast iterative shrinkage-thresholding algorithm for linear inverse problems SIAM Journal on Imaging Sciences 2(1) 183 | eng |
| oaire.version | info:eu-repo/semantics/publishedVersion | spa |