Parkinson's detection based on combined CNN and LSTM using enhanced speech signals with Variational mode decomposition

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dc.authoridisik, ibrahim/0000-0003-1355-9420
dc.authoridIsik, Esme/0000-0002-6179-5746;
dc.authorwosidisik, ibrahim/AAG-5915-2019
dc.authorwosidIsik, Esme/AAG-5927-2019
dc.authorwosidER, Mehmet Bilal/ABA-3943-2020
dc.contributor.authorEr, Mehmet Bilal
dc.contributor.authorIsik, Esme
dc.contributor.authorIsik, Ibrahim
dc.date.accessioned2024-08-04T20:50:26Z
dc.date.available2024-08-04T20:50:26Z
dc.date.issued2021
dc.departmentİnönü Üniversitesien_US
dc.description.abstractParkinson's disease (PD) can cause many non-motor and motor symptoms such as speech and smell. One of the difficulties that Parkinson's patients can experience is a change in speech or speaking difficulties. Therefore, the right diagnosis in the early period is important in reducing the possible effects of speech disorders caused by the disease. Speech signal of Parkinson patients shows major differences compared to normal people. In this study, a new approach based on pre-trained deep networks and Long short-term memory (LSTM) by using melspectrograms obtained from denoised speech signals with Variational Mode Decomposition (VMD) for detecting PD from speech sounds is proposed. The proposed model consists of four steps. In the first step, the noise is removed by applying VMD to the signals. In the second step, mel-spectrograms are extracted from the enhanced sound signals with VMD. In the third step, pre-trained deep networks are preferred to extract deep features from the mel-spectrograms. For this purpose, ResNet-18, ResNet-50 and ResNet-101 models are used as pre-trained deep network architecture. In the last step, the classification process is occurred by giving these features as input to the LSTM model, which is designed to define sequential information from the extracted features. Experiments are performed with the PC-GITA dataset, which consists of two classes and is widely used in the literature. The results obtained from the proposed method are compared with the latest methods in the literature, it is seen that it has a better performance in terms of classification performance.en_US
dc.identifier.doi10.1016/j.bspc.2021.103006
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85111101781en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2021.103006
dc.identifier.urihttps://hdl.handle.net/11616/100064
dc.identifier.volume70en_US
dc.identifier.wosWOS:000697546700008en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectParkinson's diseaseen_US
dc.subjectLong short-term memoryen_US
dc.subjectVariational Mode Decompositionen_US
dc.subjectConvolutional Neural Networken_US
dc.titleParkinson's detection based on combined CNN and LSTM using enhanced speech signals with Variational mode decompositionen_US
dc.typeArticleen_US

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