用小波熵及非稳定周期轨道测量神经放电峰峰间期复杂性(PDF)
《西安交通大学学报》自然版[ISSN:0253-987X/CN:61-1069/T]
- 期数:
- 2008年第04期
- 页码:
- 487-491
- 栏目:
- 出版日期:
- 2008-04-10
文章信息/Info
- Title:
- Using Wavelet Entropy and Unstable Periodic Orbits to Analyse the Complexity of Interspike Interval
- 文章编号:
- 0253-987X（2008）04-0487-05
- Author(s):
- LIU Fangfang; JIAN Zhong; WANG Julei2; XUE Feng1; GAO Guodong1
- 1.Biomedical Engineering Research Institute，Xi′an Jiaotong University，Xi′an 710049，China; 2.Tangdu Hospital, The Fourth Military Medical University, Xi′an 710032, China
- 分类号:
- R318
- DOI:
- 0253-987X（2008）04-0487-05
- 文献标识码:
- A
- 摘要:
- 为了研究帕金森病理大鼠苍白球神经放电序列的复杂性，首先对RoseHindmarsh理论神经元模型分叉数据进行了动态测量，并结合非稳定周期轨道对模型分叉数据进行周期检测，最后应用小波熵对正常状态下的大鼠和帕金森病理状态下的大鼠的内侧苍白球细胞放电峰峰间期进行了复杂性检测．结果发现，在神经元模型峰峰间期时间序列中，小波熵能较好地区分混沌信号和周期信号(混沌信号的小波熵为0.04~0.21，周期信号的小波熵为0.007~0)，也能较好地区分周期一节律和周期二节律的信号(周期二节律数据的小波熵为0.007，周期一节律数据的小波熵接近于0).在小白鼠内侧苍白球细胞放电峰峰峰间期时间序列中，病理组小波熵明显高于正常组小波熵(正常组信号的小波熵为0.13~0.26，病理组信号的小波熵为0.38~0.87)．非稳定周期轨道分析方法从周期轨道方向得出了和小波熵一致的结论．结果证明， 小波熵可以定量反映神经元放电序列复杂性变化，是一种有效的复杂性测度方法．
- Abstract:
- To investigate the degree of complexity of interspike interval (ISI)， RoseHindmarsh model was evaluated with wavelet entropy and detected for period orbits with unstable periodic orbits (UPOs). Then the ISI data of neuronal discharge in different parts of globus pallidus from mouse with Parkinson disease were analyzed with the wavelet entropy. It is observed that in RoseHindmarsh model wavelet entropy is different between irregular firing pattern and period pattern (wavelet entropy of irregular pattern is between 0.004 and 0.21, wavelet entropy of period pattern is between 0.007 and zero)， and different between pattern one and pattern two (wavelet entropy of pattern two is 0.007, wavelet entropy of pattern one is nearly zero). When wavelet entropy is applied to the ISI data of neuronal discharge from mouse with Parkinson symptom， a significant increase can be found compared with healthy mouse (wavelet entropy of healthy mouse is between 0.13 and 0.26， wavelet entropy of mouse with Parkinson symptom is between 0.38 and 0.87). The conclusion of UPOs is consistent with wavelet entropy well.
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