Entropy Measures in Neural Signals
Entropy measures have been widely used in analyzing neural signals from micro- to macroscales for the normal or abnormal brain assessment. As it is unrealistic to systematic analysis in all the information entropy-based indices in different application ar
- PDF / 1,498,456 Bytes
- 42 Pages / 439.36 x 666.15 pts Page_size
- 23 Downloads / 182 Views
Entropy Measures in Neural Signals Zhenhu Liang, Xuejing Duan, and Xiaoli Li
8.1 Introduction The concept of entropy was first proposed as a thermodynamic principle by Clausius in 1865. It described the distribution probability of molecules of gaseous or fluid systems. In 1949, Claude E. Shannon introduced entropy to information theory to describe the distribution of the signal component (Shannon and Weaver 1949). Since then, entropy had been investigated to analyze neural signals. So far, various entropy measures have been proposed and used to quantify neural signals ranging from spike trains (Zhaohui and Xiaoli 2013), local field potentials (LFP) (Hu and Liang 2013) to electroencephalogram (EEG) (Zandi et al. 2013). Especially, for the noninvasive and high temporal resolution, EEG is widely used in clinical neurological disease diagnosis and brain-state monitoring, such as epilepsy (Zandi et al. 2009), Alzheimer’s disease (Cao et al. 2015), depth of anesthesia (DoA) measures (Liang et al. 2015), cognition analysis (Song and Zhang 2016), and brain– computer interface (Zhang et al. 2015). In this chapter, we focus on the entropy measures in DoA monitoring.
Z. Liang • X. Duan Institute of Electric Engineering, Yanshan University, Qinhuangdao 066004, China Key Laboratory of Industrial Computer Control Engineering of Hebei Province, Yanshan University, Qinhuangdao 066004, China e-mail: [email protected] X. Li () State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing 100875, China e-mail: [email protected] © Springer Science+Business Media Singapore 2016 X. Li (ed.), Signal Processing in Neuroscience, DOI 10.1007/978-981-10-1822-0_8
125
126
Z. Liang et al.
In the operating room, it is important to guarantee successful surgery and ensure patient safety and comfort. How to intelligently monitor the anesthetic drug effect on the brain is an important clinical concern for the anesthesiologists (Monk et al. 2005). The central nervous system (CNS) is the target of anesthetic drugs and the EEG. EEG has been widely used as a surrogate parameter to quantify the anesthetic drug effect (Rampil 1998; Jameson and Sloan 2006; Bruhn et al. 2006). However, only limited information can be gleaned from the EEG signal purely by waveform observation. With the development of signal processing methods, our understanding of EEG has greatly improved. Various EEG signal processing methods have been applied to analyze, identify or detect mental disorder, and investigate consciousness mechanisms (Rampil 1998; Okogbaa et al. 1994; Burton and zilberg 2002; Natarajan et al. 2004; Abásolo et al. 2006). As yet, numerous entropy algorithms have been proposed and used to quantify depth of anesthesia. The number of entropy-related articles retrieved from PubMed comes to 244, covering the Shannon entropy (ShEn) (Yoon et al. 2011; Bruhn et al. 2001
Data Loading...
