Feature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson's Disease Diagnosis

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dc.authoridDemir, Fatih/0000-0003-3210-3664
dc.authoridSiddique, Kamran/0000-0003-2286-1728
dc.authoridAlswaitti, Mohammed/0000-0003-0580-6954
dc.authorwosidDemir, Fatih/M-3012-2017
dc.authorwosidSiddique, Kamran/AAI-9472-2020
dc.contributor.authorDemir, Fatih
dc.contributor.authorSengur, Abdulkadir
dc.contributor.authorAri, Ali
dc.contributor.authorSiddique, Kamran
dc.contributor.authorAlswaitti, Mohammed
dc.date.accessioned2024-08-04T20:50:51Z
dc.date.available2024-08-04T20:50:51Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this paper, a novel approach was developed for Parkinson's disease (PD) diagnosis based on speech disorders. When the literature about the speech disorders-based PD diagnosis was reviewed, it was seen that the most of approaches were concentrated on the feature selection as the datasets contained a huge number of features. In contrast, in the proposed approach, instead of eliminating some of the features by using any feature selection method, all features were initially used for forming a mapping procedure where the input feature vectors were converted to the input images. Then, a deep Long Short Term Memory (LSTM) network was employed for PD detection where the obtained images were used. The deep LSTM network carried out both feature extraction and classification processes and its training was carried out in an end-to-end fashion. The activations in the convolutional layer were converted to sequence data through the sequence-folding and sequence-unfolding layers. The activations in the LSTM output with learning parameters were conveyed to the Softmax layer for the classification process. A publically available PD dataset was used in the experimental works and classification accuracy, sensitivity, specificity, precision, and F-score metrics were used for performance evaluation. The obtained accuracy, sensitivity, specificity, precision and F-score values were 94.27%, 0.960, 0.960, 0.910 and 0.930, respectively. The obtained results were also compared with some of the published results and it had seen that most of the achievements of the proposed method are better than the compared methods.en_US
dc.description.sponsorshipXiamen University Malaysia under the XMUM Research Fund (XMUMRF) [XMUMRF/2019-C4/IECE/0012]en_US
dc.description.sponsorshipThis work was supported by Xiamen University Malaysia (XMUM) under the XMUM Research Fund (XMUMRF) received by Mohammed Alswaitti (Grant No: XMUMRF/2019-C4/IECE/0012).en_US
dc.identifier.doi10.1109/ACCESS.2021.3124765
dc.identifier.endpage149464en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85118638643en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage149456en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2021.3124765
dc.identifier.urihttps://hdl.handle.net/11616/100297
dc.identifier.volume9en_US
dc.identifier.wosWOS:000716678400001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_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.subjectFeature extractionen_US
dc.subjectSupport vector machinesen_US
dc.subjectLong short term memoryen_US
dc.subjectConvolutionen_US
dc.subjectRadio frequencyen_US
dc.subjectData modelsen_US
dc.subjectWavelet transformsen_US
dc.subjectConvolutional structureen_US
dc.subjectdeep LSTM networken_US
dc.subjectfeature mappingen_US
dc.subjectPD diagnosisen_US
dc.subjectspeech disordersen_US
dc.titleFeature Mapping and Deep Long Short Term Memory Network-Based Efficient Approach for Parkinson's Disease Diagnosisen_US
dc.typeArticleen_US

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