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.
| Original language | English |
|---|---|
| Pages (from-to) | 461-476 |
| Number of pages | 16 |
| Journal | Technology and Health Care |
| Volume | 28 |
| Issue number | 5 |
| DOIs | |
| Publication status | Published - 2020 |
| Externally published | Yes |
Keywords
- accelerometer signals
- discrimination
- EMG
- essential tremor
- Parkinsonian tremor
- Physiological tremor
- power-spectral density
- soft-decision technique
- wavelet-decomposition
ASJC Scopus subject areas
- Biophysics
- Bioengineering
- Information Systems
- Biomaterials
- Biomedical Engineering
- Health Informatics