Empirical Wavelet Transform Based Features for Classification of Parkinson’s Disease Severity.
Parkinson’s disease (PD) is a type of progressive neurodegenerative disorder that has affected a large part of the population till now. Several symptoms of PD include tremor, rigidity, slowness of movements and vocal impairments. In order to develop an effective diagnostic system, a number of algori...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 2 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127989779&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127989779 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Feb2018 vid: 42 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 127989779 127989779 127989779 10.1007/s10916-017-0877-2 127989779 ppf: 0 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Empirical Wavelet Transform Based Features for Classification of Parkinson’s Disease Severity. aug: au: Oung, Qi Wei Muthusamy, Hariharan Basah, Shafriza Nisha Lee, Hoileong Vijean, Vikneswaran affil: School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600, Arau, Perlis, Malaysia sug: subj: Parkinson Disease Classification Severity of Illness Wearable Sensors Human Motion Analysis Systems Female Male Middle Age Aged Speech Disorders Biological Markers Signal Processing, Computer Assisted P-Value Analysis of Variance Data Analysis Software Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Parkinson’s disease (PD) is a type of progressive neurodegenerative disorder that has affected a large part of the population till now. Several symptoms of PD include tremor, rigidity, slowness of movements and vocal impairments. In order to develop an effective diagnostic system, a number of algorithms were proposed mainly to distinguish healthy individuals from the ones with PD. However, most of the previous works were conducted based on a binary classification, with the early PD stage and the advanced ones being treated equally. Therefore, in this work, we propose a multiclass classification with three classes of PD severity level (mild, moderate, severe) and healthy control. The focus is to detect and classify PD using signals from wearable motion and audio sensors based on both empirical wavelet transform (EWT) and empirical wavelet packet transform (EWPT) respectively. The EWT/EWPT was applied to decompose both speech and motion data signals up to five levels. Next, several features are extracted after obtaining the instantaneous amplitudes and frequencies from the coefficients of the decomposed signals by applying the Hilbert transform. The performance of the algorithm was analysed using three classifiers – K-nearest neighbour (KNN), probabilistic neural network (PNN) and extreme learning machine (ELM). Experimental results demonstrated that our proposed approach had the ability to differentiate PD from non-PD subjects, including their severity level – with classification accuracies of more than 90% using EWT/EWPT-ELM based on signals from motion and audio sensors respectively. Additionally, classification accuracy of more than 95% was achieved when EWT/EWPT-ELM is applied to signals from integration of both signal’s information. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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