Affiliations: [a] Department of Electrical and Computer Engineering, Sultan Qaboos University, Al-Khoud, 123 Muscat, Oman | [b] Department of Neurology, University of Kiel, D-24105 Kiel, Germany | [c] Department of Neurology, Movement Disorders and Neurostimulation, Biomedical Statistics and Multimodal Signal Processing, University Medical Center of Johannes Gutenberg-University Mainz, 55131-Mainz, Germany | [d] Institute for Circuit and System Theory, Faculty of Engineering, University of Kiel, D-24143 Kiel, Germany
Correspondence:
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Corresponding author: A. Hossen, Department of Electrical and Computer Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khoud, 123 Muscat, Oman. E-mail: abhossen@squ.edu.om.
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.
