国际生物医学工程杂志
國際生物醫學工程雜誌
국제생물의학공정잡지
INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING
2014年
2期
122-125
,共4页
小波熵%认知%癫痫%脑电图
小波熵%認知%癲癇%腦電圖
소파적%인지%전간%뇌전도
Wavelet entropy%Cognitive%Epilepsy%EEG
小波熵是一个衡量非线性信号多尺度动力学行为有序、无序程度的量化指标,其可提供信号非线性动力学过程复杂程度的信息.近年来,小波熵在脑电信号中的研究日益受到关注,国内外学者用小波熵研究脑电信号、诱发电位、事件相关电位等的复杂程度,进一步揭示了大脑电活动的动力学机制.其主要应用于大脑感知、认知活动的研究,癫痫脑电信号的动态观测,睡眠、网络成瘾、头外伤后脑神经的康复等几个方面.小波熵不仅可以显示受到刺激后脑电信号频率上同步化的动态演变过程,而且可以有效区分癫痫发作前状态和癫痫发作状态,从而加深了对脑动力学机制的理解,成为认知功能研究的一种新的方法,显示了在脑电信号分析中良好的应用前景.
小波熵是一箇衡量非線性信號多呎度動力學行為有序、無序程度的量化指標,其可提供信號非線性動力學過程複雜程度的信息.近年來,小波熵在腦電信號中的研究日益受到關註,國內外學者用小波熵研究腦電信號、誘髮電位、事件相關電位等的複雜程度,進一步揭示瞭大腦電活動的動力學機製.其主要應用于大腦感知、認知活動的研究,癲癇腦電信號的動態觀測,睡眠、網絡成癮、頭外傷後腦神經的康複等幾箇方麵.小波熵不僅可以顯示受到刺激後腦電信號頻率上同步化的動態縯變過程,而且可以有效區分癲癇髮作前狀態和癲癇髮作狀態,從而加深瞭對腦動力學機製的理解,成為認知功能研究的一種新的方法,顯示瞭在腦電信號分析中良好的應用前景.
소파적시일개형량비선성신호다척도동역학행위유서、무서정도적양화지표,기가제공신호비선성동역학과정복잡정도적신식.근년래,소파적재뇌전신호중적연구일익수도관주,국내외학자용소파적연구뇌전신호、유발전위、사건상관전위등적복잡정도,진일보게시료대뇌전활동적동역학궤제.기주요응용우대뇌감지、인지활동적연구,전간뇌전신호적동태관측,수면、망락성은、두외상후뇌신경적강복등궤개방면.소파적불부가이현시수도자격후뇌전신호빈솔상동보화적동태연변과정,이차가이유효구분전간발작전상태화전간발작상태,종이가심료대뇌동역학궤제적리해,성위인지공능연구적일충신적방법,현시료재뇌전신호분석중량호적응용전경.
Wavelet entropy,as a powerful quantitative parameter to measure the ordering/disordering level of multi-scale dynamical behavior for nonlinear signals,provides information of complex degree in nonlinear dynamical process.Recently,the wavelet entropy is attracting more and more attention in electroencephalogram (EEG) signal analysis,which is employed by domestic and overseas scholars to investigate the complex degree of EEG,evoked potential and event-related potential,and to profoundly reveal the dynamic mechanism of physiological electrical activity in the brain.It is mainly used in the research of perception,cognitive activity,dynamic observation of epileptic EEG signals,sleeping,internet addiction and rehabilitation of brain after injury.Not only can the wavelet entropy represent the dynamic evolution process of the frequency synchronization for stimulated EEG signals,but also distinguish the states before and after epileptic seizure,as well as to deepen the understanding of brain dynamics mechanism.The wavelet entropy is becoming a new tool for investigating cognition and exhibits a good application prospect in EEG signal analysis.