基于稀疏表示模型的EEG信号棘波自动检测技术与应用系统研究
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摘要
脑电图(Electroencephalography, EEG)检查是临床无创获取脑电信号最为便捷而成熟的方式,它是临床脑疾病诊断、神经生理学、脑科学等研究的重要途径。完成的脑电诱发电信号的产生和诱发电位的采集,及其癫痫特征波的自动检测与分类在临床检测、脑电监护、癫痫等脑疾病的控制与治疗上均有很重要的意义。
本论文主要针对癫痫脑电信号特别是诱发癫痫脑电信号的采集和处理展开研究,取得的创新成果主要包括：
(1)以EEG信号的神经电活动偶极子模型与头球模型为生理基础,以EEG信号的癫痫病态特征波具有的多形态结构特征为先验基础,对EEG信号的产生过程进行了数学建模,建立了EEG信号多形态生成模型。同时,基于EEG信号的可稀疏性表示假设,提出了面向特征抽取的EEG信号结构自适应稀疏分解模型(SSDM),为本文的癫痫特征自动检测理论与算法奠定了模型基础。
(2)设计了一种新的EEG信号自适应稀疏表示过完备原子库。基于原子库设计要尽可能匹配逼近信号的内在结构成分这个基本准则,利用高斯函数及一阶、二阶导函数作为生成函数,研究设计了能够匹配异常EEG信号的多种多形态瞬时结构波形的过完备稀疏表示原子库。利用匹配追踪算法进行稀疏分解,结果显示EEG信号形态匹配过完备原子库具有更强的稀疏表示性能,并揭示了经典原子库下的稀疏表示对EEG信号特征检测的局限性。在此基础上,讨论了基于稀疏表示的EEG信号压缩采样问题。
(3)提出了一种新的基于形态结构匹配的EEG信号棘波自动检测算法。算法的核心是,根据临床诊断中癫痫特征波的人工检测标准,以及本文提出的形态匹配过完备原子库,给出了癫痫特征波形态结构特征的定量化描述,包括癫痫特征波的持续时间、幅度、锐度以及波峰两侧或一侧是否出现短暂的反向抑制过程等特征,并进一步建立了癫痫特征波的定量化检测标准。算法首先通过波形分解与自适应预测滤波技术去除多导数据中非癫痫特征波形,仅保留有癫痫嫌疑的波段作进一步处理,以提高后续处理的效率；然后利用EEG信号在形态匹配过完备原子库下的稀疏表示时频参数,自适应提取各种瞬态波形的形态结构特征,通过与建立的定量化检测标准进行匹配与比对,从而实现EEG信号的癫痫特征波自动检测与分析。实验结果显示,本文的EEG稀疏表示原子库不仅能够克服Gabor字典不能识别周期化棘波序列的缺点,而且能够有效去除背景节律与伪迹的影响,提高了处理效率和癫痫波检测的正确率。
(4)根据癫痫等神经系统疾病的神经电生理信号处理和疾病筛查的需要,研制了便携式的、可方便实现稀疏表示分析算法的神经诱发电信号处理系统。在PDA等移动计算设备的SD接口技术和Zigbee短距离无线通讯技术基础上,设计出短距离无线遥测SD卡和相应的测试终端,并编写出运行于Windows CE等移动操作系统平台上的设备驱动程序和数据采集、处理程序,完成诱发电信号的产生和诱发电位的采集。系统硬件设计过程简单,软件设计平台完善。实践表明基于PDA等移动计算平台和Zigbee短距离无线通讯技术的诱发电位测试系统的卓越的诱发信号发生和实时采集、处理诱发电位信号的能力,满足大多数诱发电位研究的需要,特别是满足微侵扰式的诱发电位研究的需要。
EEG (Electroencephalography, EEG) examination is by now the most convenient and mature manner for clinic to take the brain electrical signal by noninvasive access. It also provides an efficient approach to make clinical diagnosis of brain diseases and research on neurophysiology and brain science. In fact, the generation and collection of nerve evoked electrical signals, and also automatic detection and classification of epilepsy features have played a very important role in clinical testing, EEG ward, control and treatment of epilepsy and other brain diseases.
The main focus in this thesis is the automatic detection of epileptic spikes in EEG signals based on sparse representation. The innovation results achieved in the thesis are summarized as follows.
Firstly, a multi-component mathematical model is established for the producing process of EEG signals, taking the neuroelectricity activity dipole model and header model as the physiological foundation and the epileptic characteristics in abnormal EEG signals as the prior foundation. Then, a structure adaptive sparse decomposition model (SSDM) is proposed for EEG signals, based on the hypothesis that EEG signals can be of sparse representation. In fact, the two models have been the foundation of our research in this thesis.
The second constribution in this thesis is the designed sparse dictionary for EEG signals. To represent EEG signals as sparsely as possible, the atoms in the designed dictionary should match the inherent structures in the EEG signals as closely as possible. Since EEG signals are locally observed much like Gaussian probability density, Gaussian wavelet, and Mexico-hat wavelet, the generation functions of the dictionary are intuitively chosen as the Gaussian and its first-order and second-order derivative functions, producing a sparse dictionary capable of matching kinds of characteristic epilepsy waves in abnormal EEG signals. Experiment results demonstrate that the designed sparse dictionary behaves more efficiently than the usual Gabor dictionary in sparse representation of the EEG signals. Moreover, compressive sampling of EEG signals is also discussed based on sparse representation.
The core of this thesis is the newly proposed algorithm of automatic detection of epileptic spikes in EEG signals through our quantitative dectection criteria which have been established based on the detection criteria in clinic diagnosis and our designed sparse dictionary. In the first stage of the algorithm, an adaptive autoregressive prediction filter is used as a pre-detector to detect all the possible epileptiform transients. This pre-detection is not only able to reduce the complexity of the algorithm but also improve the overall detection performance of the procedure. In the second stage, the time-frequency parametrization of EEG signals is provided using our designed sparse dictionary and the matching pursuit method, capable of describing quantificationally the epilepsy characteristic waves in the abnormal EEG signals. Through comparing with the pre-established detection criteria, the time-frequency parametrization can be used to automatically dectect the epileptic spikes in the abnormal EEG signals. Numerous experiment results show that the proposed algorithm is not only capable of detecting the periodic spike sequences in EEG signals, but also effectively elliminating the influences of background rhythm and artifacts. Compared with previous detection methods based on the Gabor dictionary, our algorithm behaves more efficiently and accurately.
Motivated by the need of clinical testing of epilepsy and other neurological diseases, a portable application system is designed for sampling and processing the EEG signals, which can easily implement the relevant processing algorithms based on sparse analysis. The design of hardware system has been based on technologies of Zigbee short-range wireless communi-cations and SD card interface for mobile computing devices such as PDA. The design of the application system includes a couple of aspects:(a) the design of SD card for short-range wireless telemetry and corresponding test terminals; (b) the programs for device drivers and data acquisition and processing running on the Windows CE operating system. The overall design process of hardware system is simple, and that of software system is perfect. Practical applications demonstrate that our designed system has met the most research on evoked potentials, in particular to those micro-intrusive types.
本论文主要针对癫痫脑电信号特别是诱发癫痫脑电信号的采集和处理展开研究,取得的创新成果主要包括：
(1)以EEG信号的神经电活动偶极子模型与头球模型为生理基础,以EEG信号的癫痫病态特征波具有的多形态结构特征为先验基础,对EEG信号的产生过程进行了数学建模,建立了EEG信号多形态生成模型。同时,基于EEG信号的可稀疏性表示假设,提出了面向特征抽取的EEG信号结构自适应稀疏分解模型(SSDM),为本文的癫痫特征自动检测理论与算法奠定了模型基础。
(2)设计了一种新的EEG信号自适应稀疏表示过完备原子库。基于原子库设计要尽可能匹配逼近信号的内在结构成分这个基本准则,利用高斯函数及一阶、二阶导函数作为生成函数,研究设计了能够匹配异常EEG信号的多种多形态瞬时结构波形的过完备稀疏表示原子库。利用匹配追踪算法进行稀疏分解,结果显示EEG信号形态匹配过完备原子库具有更强的稀疏表示性能,并揭示了经典原子库下的稀疏表示对EEG信号特征检测的局限性。在此基础上,讨论了基于稀疏表示的EEG信号压缩采样问题。
(3)提出了一种新的基于形态结构匹配的EEG信号棘波自动检测算法。算法的核心是,根据临床诊断中癫痫特征波的人工检测标准,以及本文提出的形态匹配过完备原子库,给出了癫痫特征波形态结构特征的定量化描述,包括癫痫特征波的持续时间、幅度、锐度以及波峰两侧或一侧是否出现短暂的反向抑制过程等特征,并进一步建立了癫痫特征波的定量化检测标准。算法首先通过波形分解与自适应预测滤波技术去除多导数据中非癫痫特征波形,仅保留有癫痫嫌疑的波段作进一步处理,以提高后续处理的效率；然后利用EEG信号在形态匹配过完备原子库下的稀疏表示时频参数,自适应提取各种瞬态波形的形态结构特征,通过与建立的定量化检测标准进行匹配与比对,从而实现EEG信号的癫痫特征波自动检测与分析。实验结果显示,本文的EEG稀疏表示原子库不仅能够克服Gabor字典不能识别周期化棘波序列的缺点,而且能够有效去除背景节律与伪迹的影响,提高了处理效率和癫痫波检测的正确率。
(4)根据癫痫等神经系统疾病的神经电生理信号处理和疾病筛查的需要,研制了便携式的、可方便实现稀疏表示分析算法的神经诱发电信号处理系统。在PDA等移动计算设备的SD接口技术和Zigbee短距离无线通讯技术基础上,设计出短距离无线遥测SD卡和相应的测试终端,并编写出运行于Windows CE等移动操作系统平台上的设备驱动程序和数据采集、处理程序,完成诱发电信号的产生和诱发电位的采集。系统硬件设计过程简单,软件设计平台完善。实践表明基于PDA等移动计算平台和Zigbee短距离无线通讯技术的诱发电位测试系统的卓越的诱发信号发生和实时采集、处理诱发电位信号的能力,满足大多数诱发电位研究的需要,特别是满足微侵扰式的诱发电位研究的需要。
EEG (Electroencephalography, EEG) examination is by now the most convenient and mature manner for clinic to take the brain electrical signal by noninvasive access. It also provides an efficient approach to make clinical diagnosis of brain diseases and research on neurophysiology and brain science. In fact, the generation and collection of nerve evoked electrical signals, and also automatic detection and classification of epilepsy features have played a very important role in clinical testing, EEG ward, control and treatment of epilepsy and other brain diseases.
The main focus in this thesis is the automatic detection of epileptic spikes in EEG signals based on sparse representation. The innovation results achieved in the thesis are summarized as follows.
Firstly, a multi-component mathematical model is established for the producing process of EEG signals, taking the neuroelectricity activity dipole model and header model as the physiological foundation and the epileptic characteristics in abnormal EEG signals as the prior foundation. Then, a structure adaptive sparse decomposition model (SSDM) is proposed for EEG signals, based on the hypothesis that EEG signals can be of sparse representation. In fact, the two models have been the foundation of our research in this thesis.
The second constribution in this thesis is the designed sparse dictionary for EEG signals. To represent EEG signals as sparsely as possible, the atoms in the designed dictionary should match the inherent structures in the EEG signals as closely as possible. Since EEG signals are locally observed much like Gaussian probability density, Gaussian wavelet, and Mexico-hat wavelet, the generation functions of the dictionary are intuitively chosen as the Gaussian and its first-order and second-order derivative functions, producing a sparse dictionary capable of matching kinds of characteristic epilepsy waves in abnormal EEG signals. Experiment results demonstrate that the designed sparse dictionary behaves more efficiently than the usual Gabor dictionary in sparse representation of the EEG signals. Moreover, compressive sampling of EEG signals is also discussed based on sparse representation.
The core of this thesis is the newly proposed algorithm of automatic detection of epileptic spikes in EEG signals through our quantitative dectection criteria which have been established based on the detection criteria in clinic diagnosis and our designed sparse dictionary. In the first stage of the algorithm, an adaptive autoregressive prediction filter is used as a pre-detector to detect all the possible epileptiform transients. This pre-detection is not only able to reduce the complexity of the algorithm but also improve the overall detection performance of the procedure. In the second stage, the time-frequency parametrization of EEG signals is provided using our designed sparse dictionary and the matching pursuit method, capable of describing quantificationally the epilepsy characteristic waves in the abnormal EEG signals. Through comparing with the pre-established detection criteria, the time-frequency parametrization can be used to automatically dectect the epileptic spikes in the abnormal EEG signals. Numerous experiment results show that the proposed algorithm is not only capable of detecting the periodic spike sequences in EEG signals, but also effectively elliminating the influences of background rhythm and artifacts. Compared with previous detection methods based on the Gabor dictionary, our algorithm behaves more efficiently and accurately.
Motivated by the need of clinical testing of epilepsy and other neurological diseases, a portable application system is designed for sampling and processing the EEG signals, which can easily implement the relevant processing algorithms based on sparse analysis. The design of hardware system has been based on technologies of Zigbee short-range wireless communi-cations and SD card interface for mobile computing devices such as PDA. The design of the application system includes a couple of aspects:(a) the design of SD card for short-range wireless telemetry and corresponding test terminals; (b) the programs for device drivers and data acquisition and processing running on the Windows CE operating system. The overall design process of hardware system is simple, and that of software system is perfect. Practical applications demonstrate that our designed system has met the most research on evoked potentials, in particular to those micro-intrusive types.
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