Choi Young-Seok, Koenig Matthew A, Jia Xiaofeng, Thakor Nitish V
Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21205 USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4715-8. doi: 10.1109/IEMBS.2009.5334199.
It is known that the multiunit activity (MUA) reflects the status of population of neurons in the vicinity of an electrode. We provide a quantitative measure of the time-varying multiunit neuronal spiking activity using an entropy based approach. To verify the status embedded in the neuronal activity of a population of neurons, we incorporate the discrete wavelet transform (DWT) to isolate the inherent spiking activity of MUA from the noise and background cortical activity or field potentials. Owing to the decorrelating property of DWT, the spiking activity would be preserved while reducing the non-spiking component such as the background noise. By evaluating the entropy of the wavelet coefficients of the denoised MUA, a multiresolution entropy of the MUA signal is developed. The proposed entropy measure was tested in the analysis of both simulated noisy MUA and actual MUA recorded from cortex in rodent model which undergoes hypoxic-ischemic brain injury. Simulation and Experimental results demonstrate that the dynamics of a population can be quantified by using the proposed multiresolution entropy.
众所周知，多单位活动（MUA）反映了电极附近神经元群体的状态。我们使用基于熵的方法对时变多单位神经元放电活动进行了定量测量。为了验证神经元群体活动中所蕴含的状态，我们采用离散小波变换（DWT）从噪声和背景皮层活动或场电位中分离出MUA固有的放电活动。由于DWT的去相关特性，在减少诸如背景噪声等非放电成分的同时，放电活动将得以保留。通过评估去噪后MUA的小波系数的熵，开发了MUA信号的多分辨率熵。所提出的熵测量方法在对模拟的有噪声MUA以及从经历缺氧缺血性脑损伤的啮齿动物模型皮层记录的实际MUA的分析中进行了测试。模拟和实验结果表明，所提出的多分辨率熵能够对神经元群体的动态变化进行量化。
