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Empirical Wavelet Transform Based Features for Classification of Parkinson’s Disease Severity

  • Image & Signal Processing
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Abstract

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.

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Acknowledgments

All the authors who like to thank the neurologists from Penang General Hospital and all the individuals who have supported in this research.

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Authors and Affiliations

  1. School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600, Arau, Perlis, Malaysia

    Qi Wei Oung, Shafriza Nisha Basah, Hoileong Lee & Vikneswaran Vijean

  2. Department of Biomedical Engineering, School of Bio-Engineering, SRM Institute of Science and Technology, SRM Nagar, Kancheepuram District, Kattankulathur, Tamil Nadu, 603 203, India

    Hariharan Muthusamy

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  1. Qi Wei Oung
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The protocol of this study had been approved by Medical Research and Committee of National Medical Research Register (NMRR) Malaysia referring to the protocol number: NMRR-13-1412-18,661.

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Informed consent was obtained from all individual participants included in the study.

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Oung, Q.W., Muthusamy, H., Basah, S.N. et al. Empirical Wavelet Transform Based Features for Classification of Parkinson’s Disease Severity. J Med Syst 42, 29 (2018). https://doi.org/10.1007/s10916-017-0877-2

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