Deep Learning-Based Parkinson's Disease Classification Using Vocal Feature Sets

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dc.contributor.authorGündüz, Hakan
dc.date.accessioned2020-05-01T09:11:17Z
dc.date.available2020-05-01T09:11:17Z
dc.date.issued2019
dc.departmentDÜ, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.descriptionGunduz, Hakan/0000-0003-2152-5490en_US
dc.descriptionWOS: 000484228300001en_US
dc.description.abstractParkinson's Disease (PD) is a progressive neurodegenerative disease with multiple motor and non-motor characteristics. PD patients commonly face vocal impairments during the early stages of the disease. So, diagnosis systems based on vocal disorders are at the forefront on recent PD detection studies. Our study proposes two frameworks based on Convolutional Neural Networks to classify Parkinson's Disease (PD) using sets of vocal (speech) features. Although, both frameworks are employed for the combination of various feature sets, they have difference in terms of combining feature sets. While the first framework combines different feature sets before given to 9-layered CNN as inputs, whereas the second framework passes feature sets to the parallel input layers which are directly connected to convolution layers. Thus, deep features from each parallel branch are extracted simultaneously before combining in the merge layer. Proposed models are trained with dataset taken from UCI Machine Learning repository and their performances are validated with Leave-One-Person-Out Cross Validation (LOPO CV). Due to imbalanced class distribution in our data, F-Measure and Matthews Correlation Coefficient metrics are used for the assessment along with accuracy. Experimental results show that the second framework seems to be very promising, since it is able to learn deep features from each feature set via parallel convolution layers. Extracted deep features are not only successful at distinguishing PD patients from healthy individuals but also effective in boosting up the discriminative power of the classifiers.en_US
dc.identifier.doi10.1109/ACCESS.2019.2936564en_US
dc.identifier.endpage115551en_US
dc.identifier.issn2169-3536
dc.identifier.startpage115540en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2019.2936564
dc.identifier.urihttps://hdl.handle.net/20.500.12684/5467
dc.identifier.volume7en_US
dc.identifier.wosWOS:000484228300001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isoenen_US
dc.publisherIeee-Inst Electrical Electronics Engineers Incen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectConvolutional neural networksen_US
dc.subjectdeep learningen_US
dc.subjecthealth informaticsen_US
dc.subjectParkinson's disease classificationen_US
dc.subjectvocal featuresen_US
dc.titleDeep Learning-Based Parkinson's Disease Classification Using Vocal Feature Setsen_US
dc.typeArticleen_US

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