Robust automated Parkinson disease detection based on voice signals with transfer learning

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dc.authorid0000-0003-3672-1865en_US
dc.authorid0000-0002-2635-4953en_US
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-7201-6963en_US
dc.contributor.authorKaraman, Onur
dc.contributor.authorÇakın, Hakan
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorPolat, Kemal
dc.date.accessioned2023-06-16T08:13:23Z
dc.date.available2023-06-16T08:13:23Z
dc.date.issued2021en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis publication was supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia.en_US
dc.description.abstractParkinson's disease (PD) is a progressive-neurodegenerative disorder that affects more than 6 million people around the world. However, conventional techniques for PD detection are often hand-crafted, in which special expertise is needed. In this study, considering the importance of rapid diagnosis of the disease, it was aimed to develop deep convolutional neural networks (CNN) for automated PD identification based on biomarkers-derived voice signals. The developed CNN methods consisted of two main stages, including data pre-processing and fine-tunning-based transfer learning steps. To train and evaluate the performance of the developed model, datasets were collected from the mPower Voice database. SqueezeNet1_1, ResNet101, and DenseNet161 architectures were retrained and evaluated to determine which architecture can classify frequency-time information most accurately. The performance results revealed that the proposed model could successfully identify the PD with an accuracy of 89.75%, sensitivity of 91.50%, and precision of 88.40% for DenseNet-161 architecture identified as the most suitable fine-tuning architecture. The results revealed that the proposed model based on transfer learning with a fine-tuning approach provides an acceptable detection of PD with an accuracy of 89.75%. The outcomes of the study confirmed that by integrating the developed model into smart electronic devices, it will be able to develop alternative pre-diagnosis methods and will assist the physicians for PD detection during the in-clinic assessment. The success of the proposed model would imply an enhancement in the life quality of patients and a cost reduction for the national health system.en_US
dc.identifier.citationKaraman, O., Çakın, H., Alhudhaif, A., & Polat, K. (2021). Robust automated Parkinson disease detection based on voice signals with transfer learning. Expert Systems with Applications, 178, 115013.en_US
dc.identifier.doi10.1016/j.eswa.2021.115013
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.scopus2-s2.0-85104408099en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttp://dx.doi.org/10.1016/j.eswa.2021.115013
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11130
dc.identifier.volume178en_US
dc.identifier.wosWOS:000705088200015en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherPERGAMON-ELSEVIER SCIENCE LTDen_US
dc.relation.ispartofExpert Systems with Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectParkinson's Disease (PD)en_US
dc.subjectAcoustic Sensingen_US
dc.subjectConvolutional Neural Network (CNN)en_US
dc.subjectTransfer Learningen_US
dc.subjectVoice Signalen_US
dc.subjectDiagnosisen_US
dc.titleRobust automated Parkinson disease detection based on voice signals with transfer learningen_US
dc.typeArticleen_US

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