脑电复杂度和脑电能量在脑信号处理中的应用(英文)
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- 论文作者：曹建庭
- 英文论文作者：Jianting Cao
- 年：2016
- 作者机构：日本埼玉工业大学;
- 会议召开时间：2016-08-04
- 会议录名称：第三届全国神经动力学学术会议论文摘要集
- 语种：英文
- 分类号：R338;TN911.7
- 学会代码：AGLU
- 会议名称：第三届全国神经动力学学术会议
- 会议地点：中国甘肃敦煌
- 主办单位：中国力学学会动力学与控制专业委员会神经动力学专业组
- 学会名称：中国力学学会
- 页数：1
- 文件大小：612k
- 原文格式：D
- 会议级别：全国
摘要
We present a calculating entropy and an accumulating energy electroencephalography(EEG) analyses method for the purpose of supporting the clinical brain death determination. The brain death is defined as the cessation and irreversibility of all brain and brain stem functions. Based on this definition, the basic clinical criterion has been established in most countries. However, in the standard process of brain death diagnosis, it often involves certain risks and takes a long time. Therefore, in order to reduce the risk and to save the precious time for the medical care in clinical practice, an EEG-based preliminary examination system was developed during the standard clinical procedure. The purpose of the preliminary EEG examination is to explore advanced signal processing tools to discover whether any brain wave activity occurs in the patient's brain. The EEG preliminary examination system includes a portable EEG data acquisition tool and the EEG-oriented signal processing tools. The signal processing tools include several routines of noise reduction, source separation, feature extraction, as well as statistical tests.This talk will focus on an approximate entropy measure and an energy measure method to evaluate the differences between deep comatose patients and quasi brain death. The energy of brain activities is evaluated by using empirical mode decomposition(EMD). This method is used to decompose the data into several oscillatory components called intrinsic mode function(IMF). The IMF components are usually expressed as the standard Hilbert transforms, from which the instantaneous frequencies can be calculated. The local energy and the instantaneous frequency derived from the IMF components through the Hilbert transform can be given a full energy frequency time distribution of the data.In our studies, we firstly decompose a single-channel recorded EEG data into a number of components with different frequencies. From there, we can select the components that are related to the brain activities. Since the energy or the power of spontaneous activities in a live brain is usually higher than that of non-activity components, therefore, we can analyze and evaluate the energy or the power spectrum differences between comatose patients and quasi brain deaths. Moreover, in order to extract brain activity features from multi-channel EEG simultaneously, we use recently developed multivariate empirical mode decomposition(MEMD) method.
We present a calculating entropy and an accumulating energy electroencephalography(EEG) analyses method for the purpose of supporting the clinical brain death determination. The brain death is defined as the cessation and irreversibility of all brain and brain stem functions. Based on this definition, the basic clinical criterion has been established in most countries. However, in the standard process of brain death diagnosis, it often involves certain risks and takes a long time. Therefore, in order to reduce the risk and to save the precious time for the medical care in clinical practice, an EEG-based preliminary examination system was developed during the standard clinical procedure. The purpose of the preliminary EEG examination is to explore advanced signal processing tools to discover whether any brain wave activity occurs in the patient's brain. The EEG preliminary examination system includes a portable EEG data acquisition tool and the EEG-oriented signal processing tools. The signal processing tools include several routines of noise reduction, source separation, feature extraction, as well as statistical tests.This talk will focus on an approximate entropy measure and an energy measure method to evaluate the differences between deep comatose patients and quasi brain death. The energy of brain activities is evaluated by using empirical mode decomposition(EMD). This method is used to decompose the data into several oscillatory components called intrinsic mode function(IMF). The IMF components are usually expressed as the standard Hilbert transforms, from which the instantaneous frequencies can be calculated. The local energy and the instantaneous frequency derived from the IMF components through the Hilbert transform can be given a full energy frequency time distribution of the data.In our studies, we firstly decompose a single-channel recorded EEG data into a number of components with different frequencies. From there, we can select the components that are related to the brain activities. Since the energy or the power of spontaneous activities in a live brain is usually higher than that of non-activity components, therefore, we can analyze and evaluate the energy or the power spectrum differences between comatose patients and quasi brain deaths. Moreover, in order to extract brain activity features from multi-channel EEG simultaneously, we use recently developed multivariate empirical mode decomposition(MEMD) method.
We present a calculating entropy and an accumulating energy electroencephalography(EEG) analyses method for the purpose of supporting the clinical brain death determination. The brain death is defined as the cessation and irreversibility of all brain and brain stem functions. Based on this definition, the basic clinical criterion has been established in most countries. However, in the standard process of brain death diagnosis, it often involves certain risks and takes a long time. Therefore, in order to reduce the risk and to save the precious time for the medical care in clinical practice, an EEG-based preliminary examination system was developed during the standard clinical procedure. The purpose of the preliminary EEG examination is to explore advanced signal processing tools to discover whether any brain wave activity occurs in the patient's brain. The EEG preliminary examination system includes a portable EEG data acquisition tool and the EEG-oriented signal processing tools. The signal processing tools include several routines of noise reduction, source separation, feature extraction, as well as statistical tests.This talk will focus on an approximate entropy measure and an energy measure method to evaluate the differences between deep comatose patients and quasi brain death. The energy of brain activities is evaluated by using empirical mode decomposition(EMD). This method is used to decompose the data into several oscillatory components called intrinsic mode function(IMF). The IMF components are usually expressed as the standard Hilbert transforms, from which the instantaneous frequencies can be calculated. The local energy and the instantaneous frequency derived from the IMF components through the Hilbert transform can be given a full energy frequency time distribution of the data.In our studies, we firstly decompose a single-channel recorded EEG data into a number of components with different frequencies. From there, we can select the components that are related to the brain activities. Since the energy or the power of spontaneous activities in a live brain is usually higher than that of non-activity components, therefore, we can analyze and evaluate the energy or the power spectrum differences between comatose patients and quasi brain deaths. Moreover, in order to extract brain activity features from multi-channel EEG simultaneously, we use recently developed multivariate empirical mode decomposition(MEMD) method.
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