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基于矩形模型的扩展卡尔曼平滑器检测和跟踪尖峰序列中的震颤。

Detecting and tracking tremor in spike trains using the rectangular model based extended Kalman smoother.

机构信息

Biomedical Signal Processing Laboratory, Electrical & Computer Engineering, Portland State University, Portland, OR, USA.

出版信息

J Neurosci Methods. 2010 Apr 30;188(1):97-104. doi: 10.1016/j.jneumeth.2010.01.025. Epub 2010 Feb 10.

Abstract

Tremor is one of the most disabling symptoms in patients with movement disorders such as Parkinson's disease (PD) and essential tremor (ET). Spike trains extracted from microelectrode recordings are used to study the relationship of tremor exhibited by neuronal signals to physical tremor as measured with electromyograms (EMG), gyroscopes, or accelerometers. We describe a new method for continuously tracking the instantaneous tremor frequency and amplitude in spike trains based on a new state-space model and the extended Kalman smoother. This method can be used to detect periods of statistically significant tremor in recordings with intermittent tremor.

摘要

震颤是运动障碍患者(如帕金森病 (PD) 和特发性震颤 (ET))最致残的症状之一。从微电极记录中提取的尖峰序列用于研究神经元信号表现出的震颤与肌电图 (EMG)、陀螺仪或加速度计测量的物理震颤之间的关系。我们描述了一种基于新状态空间模型和扩展卡尔曼滤波器的新方法,用于连续跟踪尖峰序列中的瞬时震颤频率和幅度。该方法可用于检测具有间歇性震颤的记录中具有统计学意义的震颤期。

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