Department of Electrical and Computer Engineering, University of Illinois at Chicago (UIC), IL, USA.
J Neural Eng. 2013 Jun;10(3):036019. doi: 10.1088/1741-2560/10/3/036019. Epub 2013 May 8.
We present a proof of concept for a novel method of predicting the onset of pathological tremor using non-invasively measured surface electromyogram (sEMG) and acceleration from tremor-affected extremities of patients with Parkinson's disease (PD) and essential tremor (ET).
The tremor prediction algorithm uses a set of spectral (Fourier and wavelet) and nonlinear time series (entropy and recurrence rate) parameters extracted from the non-invasively recorded sEMG and acceleration signals.
The resulting algorithm is shown to successfully predict tremor onset for all 91 trials recorded in 4 PD patients and for all 91 trials recorded in 4 ET patients. The predictor achieves a 100% sensitivity for all trials considered, along with an overall accuracy of 85.7% for all ET trials and 80.2% for all PD trials. By using a Pearson's chi-square test, the prediction results are shown to significantly differ from a random prediction outcome.
The tremor prediction algorithm can be potentially used for designing the next generation of non-invasive closed-loop predictive ON-OFF controllers for deep brain stimulation (DBS), used for suppressing pathological tremor in such patients. Such a system is based on alternating ON and OFF DBS periods, an incoming tremor being predicted during the time intervals when DBS is OFF, so as to turn DBS back ON. The prediction should be a few seconds before tremor re-appears so that the patient is tremor-free for the entire DBS ON-OFF cycle and the tremor-free DBS OFF interval should be maximized in order to minimize the current injected in the brain and battery usage.
我们提出了一种使用非侵入性测量的帕金森病（PD）和特发性震颤（ET）患者震颤受影响肢体的表面肌电图（sEMG）和加速度来预测病理性震颤发作的新概念方法。
该震颤预测算法使用从非侵入性记录的 sEMG 和加速度信号中提取的一组频谱（傅里叶和小波）和非线性时间序列（熵和复发率）参数。
结果表明，该算法成功预测了 4 名 PD 患者和 4 名 ET 患者记录的 91 次试验中的所有震颤发作。该预测器对所有考虑的试验具有 100％的敏感性，同时对所有 ET 试验的总体准确性为 85.7％，对所有 PD 试验的总体准确性为 80.2％。通过使用 Pearson 卡方检验，预测结果与随机预测结果显著不同。
该震颤预测算法可用于设计新一代用于深部脑刺激（DBS）的非侵入性闭环预测开-关控制器，用于抑制此类患者的病理性震颤。该系统基于交替的开和关 DBS 周期，在 DBS 关闭期间预测即将到来的震颤，以便将 DBS 重新打开。预测应在震颤再次出现之前几秒钟进行，以便患者在整个 DBS 开-关周期内无震颤，并且应最大限度地延长无震颤的 DBS 关闭间隔，以最大程度地减少大脑中的电流注入和电池使用。
