Discrimination of physiological tremor from pathological tremor using accelerometer and surface EMG signals-Reference-Cited by-同舟云学术

Discrimination of physiological tremor from pathological tremor using accelerometer and surface EMG signals

Published:2020-09-18 Issue:5 Volume:28 Page:461-476
ISSN:0928-7329
Container-title:Technology and Health Care
language:
Short-container-title:THC

Author:

Hossen A.¹,Deuschl G.²,Groppa S.³,Heute U.⁴,Muthuraman M.³

Affiliation:

1. Department of Electrical and Computer Engineering, Sultan Qaboos University, Al-Khoud, 123 Muscat, Oman

2. Department of Neurology, University of Kiel, D-24105 Kiel, Germany

3. Department of Neurology, Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing, University Medical Center of Johannes Gutenberg-University Mainz, 55131-Mainz, Germany

4. Institute for Circuit and System Theory, Faculty of Engineering, University of Kiel, D-24143 Kiel, Germany

Abstract

BACKGROUND AND OBJECTIVE: Although careful clinical examination and medical history are the most important steps towards a diagnostic separation between different tremors, the electro-physiological analysis of the tremor using accelerometry and electromyography (EMG) of the affected limbs are promising tools. METHODS: A soft-decision wavelet-based decomposition technique is applied with 8 decomposition stages to estimate the power spectral density of accelerometer and surface EMG signals (sEMG) sampled at 800 Hz. A discrimination factor between physiological tremor (PH) and pathological tremor, namely, essential tremor (ET) and the tremor caused by Parkinson’s disease (PD), is obtained by summing the power entropy in band 6 (B6: 7.8125–9.375 Hz) and band 11 (B11: 15.625–17.1875 Hz). RESULTS: A discrimination accuracy of 93.87% is obtained between the PH group and the ET & PD group using a voting between three results obtained from the accelerometer signal and two sEMG signals. CONCLUSION: Biomedical signal processing techniques based on high resolution wavelet spectral analysis of accelerometer and sEMG signals are implemented to efficiently perform classification between physiological tremor and pathological tremor.

Publisher

IOS Press

Subject

Health Informatics,Biomedical Engineering,Information Systems,Biomaterials,Bioengineering,Biophysics

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