Department of Electrical and Computer Engineering, Sultan Qaboos University, Al-Khoud, 123 Muscat, Oman.
Department of Neurology, University of Kiel, D-24105 Kiel, Germany.
Technol Health Care. 2020;28(5):461-476. doi: 10.3233/THC-191947.
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.
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).
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.
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.
尽管仔细的临床检查和病史是对不同震颤进行诊断性区分的最重要步骤，但使用加速度计和受影响肢体的肌电图（EMG）对震颤进行电生理分析是很有前途的工具。
应用软决策小波分解技术，对以 800 Hz 采样的加速度计和表面肌电图（sEMG）信号进行 8 级分解，以估计其功率谱密度。通过在频带 6（B6：7.8125-9.375 Hz）和频带 11（B11：15.625-17.1875 Hz）中求和功率熵，得出一种区分生理性震颤（PH）与病理性震颤（即特发性震颤（ET）和帕金森病引起的震颤）的判别因子。
使用加速度计信号和两个 sEMG 信号的三个结果之间的投票，在 PH 组和 ET&PD 组之间获得了 93.87%的判别准确率。
基于加速度计和 sEMG 信号的高分辨率小波谱分析的生物医学信号处理技术被用于有效地对生理性震颤和病理性震颤进行分类。
