阿尔茨海默病脑电信号多尺度时空定量特征研究
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摘要
随着人口的老龄化,阿尔茨海默病(Alzheimer's Disease, AD)的发病率日益增多,关于AD诊断的生物学标志物的研究倍受关注。近年来,对痴呆的基因、病理、影像等方面都取得了一定的进展。脑电信号是伴随人生始终的神经生理曲线,随时记录着人脑在感知、认知、思维的执行过程中的神经系统电活动行为,蕴藏着丰富的信息,对认知功能研究来说,应该是一种最佳的手段。然而目前临床对于脑电信号的分析仍依靠医生的肉眼识别和经验性判断,在AD的诊断和病情评估中还没有发挥出应有的作用。采用新的数学分析方法对脑电信号进行定量分析,提取有价值的客观定量信息和参数,对于AD的诊断、病情评估及电生理机制的研究具有重要意义。
目的：
本研究拟采用连续子变换(Contiuous wavelet transform, CWT)对轻、中、重度AD患者的自发状态下的脑电信号进行多尺度分析,与正常对照进行比较,提取AD脑电信号的时频特征,子波功率谱分布特征,以子波熵(wavelet entropy,WE)来衡量脑电信号的复杂程度,采用条件采样和相位平均技术(Conditional sampling and phase-average technique)提取分尺度的相位平均波形特征及不同导联脑电信号的同步相位差和波幅差。比较轻、中、重度AD患者与正常对照之间脑电信号时频特征、子波功率谱、子波熵、第9尺度相位平均波形的波长、不同导联的相位差和波幅差的差异,提取有助于AD诊断和病情评估的电生理指标。
方法：
1.对轻、中、重度AD患者行详细病史采集,神经系统及全身查体,行简易智能精神状态量表(MMSE),画钟试验(CDT), Hachinski缺血量表(HIS),临床痴呆评定量表(CDR),日常生活能力量表(ADL)测评,所有患者均行头核磁共振检查,对中颞叶萎缩程度进行分级。对正常对照行病史采集、查体,行MMSE、MoCA、ADL量表测评。
2.采集轻、中、重度AD患者和正常对照,安静清醒闭目状态下的数字脑电信号,采样频率200Hz,选择无伪差的脑电信号,以20秒数据存为一个数据模块,用于脑电信号定量分析。
3.脑电信号的定量分析分5个部分
(1)采用连续子变换的方法对研究对象的脑电信号进行多尺度子波分析,共分析30个尺度,绘制子波系数等值线图,显示脑电信号的时频特征。
(2)根据子波系数计算脑电信号的分尺度功率,绘制不同导联分尺度功率随频率分布图,观察脑电信号的子波功率谱分布特征。
(3)根据脑电信号分尺度功率的百分比随尺度分布的特征,提取描述脑电信号复杂程度的定量指标——子波熵。
(4)以子波系数为检测对象,用条件采样方法检测第9尺度(对应频率中心10Hz)脑电信号,并对脑电信号的同类事件进行相位对齐叠加平均,获得该尺度脑电信号的相位平均波形。
(5)采用同步互相关分析方法,对不同导联同步采集的脑电信号相位平均波形进行时间相位分析,获得不同导联相位平均波形的相位差和波幅差。
4.统计学分析采用SPSS13.0统计软件包。计量资料以均数±标准差(X±S)表示。计数资料采用χ2检验。三组及以上计量资料比较采用单因素方差分析,Levene方差齐性检验,两两比较采用LSD；计量资料单因素之间相关性分析采用Pearson相关分析。显著性检验标准为α=0.05。
结果：
(1)正常对照尺度丰富,节律性活动明显,在10Hz,1Hz,0.1Hz附近三个频带上形成稳定的节律性活动,且不同尺度间脑电信号密切关联,而AD患者自发脑电信号的时频特征表现为,尺度单一,节律性活动不稳定,节律性活动在1Hz附近明显,失去正常脑电信号的多尺度间相互关联的时频结构特征,AD患者脑电信号的多尺度时频特征随病情的加重逐渐演变。
(2)正常对照自发脑电信号的子波功率谱随频率分布较宽,在0.1Hz、1Hz、10Hz附近存在3个低而宽的功率峰,而AD患者自发脑电信号的子波功率谱分布特征为频率分布较窄,1Hz附近的功率峰增高,而0.1Hz、10Hz附近的功率峰降低,且随病情的加重逐渐演变。
(3)轻、中、重度AD患者自发脑电信号不同导联的子波熵均低于正常对照(P<0.01),且与AD患者MMSE评分呈正相关(P<0.01),说明认知障碍的程度越重,脑电信号的子波熵越低,脑电信号的多尺度复杂程度越低。
(4)轻、中、重度AD患者自发脑电信号不同导联第9尺度相位平均波形的波长均大于正常对照(P<0.01),且与AD患者MMSE评分呈负相关(P<0.01)。说明认知功能障碍的程度越重,第9尺度相位平均波形的波长越长,在这一尺度范围内脑电信号的频率越慢。
(5)正常对照从枕部导联到额部导联存在明显的相位差和波幅差,枕部导联与额部导联的相位差大约π/2相位,而AD患者自发脑电信号不同导联第9尺度相位平均波形的相位差和波幅差较正常对照减少,重度AD患者脑电信号不同导联第9尺度相位平均波形的相位差和波幅差几乎为零,这一结果与AD患者脑电信号的a节律的泛化,a节律的前移相一致。
结论：
基于子波变换、子波功率谱、子波熵、条件采样和相位平均、同步互相关分析等技术建立的脑电信号多尺度分析方法适合于脑电信号的定量客观分析和AD的临床辅助诊断。
通过对AD患者自发脑电信号的多尺度定量分析,结果表明AD患者脑电信号的时频特征、子波功率谱分布特征、子波熵、第9尺度相位平均波形的波长、不同导联第9尺度相位平均波形的相位差和波幅差等定量特征和参数可以作为辅助AD临床诊断和病情评估的神经电生理指标。
With the aging of the population, the incidence of Alzheimer's disease (AD) is keeping increasing, researches on biomarkers of AD have drawn more and more attention. In recent years, researches on gene, pathology, and imaging of AD have made some progress. EEG, as a kind of neurophysiological curve, accompanies with whole person's life. It records brain electrical activities of perception, cognition, thinking and execution instantaneously, and contains rich information. For research of cognitive function, EEG is one of the best tools. However, the analysis of EEG in clinic still relies on the naked eye recognition and empirical judgment of the doctor. EEG, as a tool for the diagnosis of AD. has not yet played its due role. Using new mathematical methods for the quantitative analysis of the EEG signal, extracting valuable objective quantitative information and parameters has important value for the diagnosis and assessment of AD. At the same time it may provide new ideas for the study of the electrophysiological mechanisms in AD.
Objective:
In this study, continuous wavelet transform (CWT) is introduced for multi-scale analysis of spontaneous EEG in mild, moderate and severe AD patients, comparing with normal controls. Wavelet power spectrum is used to characterize the EEG time-frequency multi-scale features in AD patients. Wavelet entropy, as a quantitative parameter, is introduced to measure the complexity of EEG. Conditional sampling and phase-average technique obtain the phase-average wavelength, synchronous phase difference and amplitude difference from leads at different brain area. Time-frequency characteristics, such as wavelet power spectrum, wavelet entropy, average wavelength of scale9, the lead phase difference and amplitude differences, were compared among light, moderate, severe AD patients and normal controls. The aim of this study is to find useful quantitative electrophysiological parameters of spontaneous EEG for the diagnosis and assessment of AD.
Methods:
1. Detailed history, nervous system and systemic examination was underwent for light, moderate and severe AD patients. Assessment of MMSE, CDT, HIS, CDR, ADL were made for all AD patients.temporal lobe atrophy(MTA) of MRI were classified by Visual assessment of MTA. Detailed history, nervous system and systemic examination were underwent, Assessment of MMSE, MoCA and ADL were evaluated for normal controls.
2. Raw digital EEG signals were cllected at quiet, sober eye-close state for light, moderate, severe AD patients and normal controls. Sampling frequency was200Hz, Artifact-free digital EEG data was selected and storaged as20seconds segment, for quantitative analysis.
3. Quantitative analysis of the EEG is divided into five parts
(1) Multi-scale quantitative analysis was made for EEG data recored at different brain eara by CWT. Time-frequency characteristics of30scales were analyzed, the wavelet coefficient contour map of EEG.were drawn for AD patients and normal controls.
(2) Sub-scale wavelet power was calculated according to wavelet coefficients for the EEG data of all the leads, maps of sub-scale power spectrum distribution with frequency were drawn, to observe the characteristics of wavelet power distribution of EEG in AD patients and normal controls.
(3) Wavelet entropy, as a quantitative parameters to measure the complexity of the EEG was extracted according to the percentage of the sub-scale power distribution with frequency of EEG in AD patients and normal controls.(4) EEG signal of scale9(corresponding to the frequency center of10Hz) was tested by wavelet coefficient, and detected similar incidents using by conditional sampling was superimposed and averaged in the phase alignment. EEG phase average waveform of scale9was obtained.
(5) The phase-average waveform of the different lead simultaneously acquisied by synchronous cross-correlation method, the differents of phase and amplitude of phase averaged waveforms between the leads at different brain area were obtained.
4. SPSS13.0statistical package was used for statistical analysis. Measurement data were expressed as (X±S). Count data were analyzed by χ2test.measurement data≥3groups were compared using single factor analysis of variance, and homogeneity of variance, pairwise comparisons of LSD. Single factor correlation analysis between measurement data using Pearson correlation analysis. Significant test for a=0.05.
Results:
(1) Rich scale, stable rhythmic activities near the three frequency bands of10Hz,1Hz,0.1Hz, and closed association between neighber scales was the time-frequency characteristics of EEG of normal controls. However time-frequency feather of AD patients was characterized by single scale, instability rhythmic activities, obvious rhythmic activity near1Hz, and loss of the normal EEG of multi-scale interconnected time-frequency structural. The time-frequency characteristics of EEG in AD patients evolved with the worsen of the disease.
(2) There were three low and wide power peaks near the frequecy of0.1Hz,1Hz,10Hz on wavelet power spectrum distribution of spontaneous EEG in normal controls. While the wavelet power spectrum distribution in patients with AD was characteristic for a narrow power peak near1Hz, with the aggravation of the disease evolved, the power peak near1Hz increased, and power peak near0.1Hz,10Hz decreased.
(3) Wavelet entropy of spontaneous EEG recorded from all leads in Mild, moderate and severe AD patients were lower than normal controls (P<0.01). Wavelet entropy and the MMSE score was positively correlated (P<0.01). This shows that the more severe cognitive impairment, the lower wavelet entropy, the lower complexity of the EEG.
(4) Wavelength of phase average waveform of scale9recorded from all leads is greater than the normal controls (P<0.01), Wavelength of phase average waveform of scale9and MMSE score was negtivly correlated (P<0.01). This shows that the more severe cognitive impairment, the longer the wavelength of phase average waveform of scale9, the slower EEG frequency within the range of this scale.
(5) There exist significant phase and amplitude difference of phase average waveform at scale9for spontaneous EEG between occipital leads and forehead leads in normal controls with phase difference between occipital leads and forehead leads is about π/2phase. Amplitude of phase average waveform of occipital leads at scale9is higher than that of forehead lead. Phase difference and amplitude difference of phase average waveform at scale9between occipital leads and forehead leads in AD patients decrease when compared with normal controls while phase difference and amplitude difference in sever AD patients are almost negligible. The result is consistent with alpha rhythm diffused and forward transfer in AD patients on visual EEG.
Conclusion:
The new EEG multi-scale analysis methods including wavelet transform, wavelet power spectrum, wavelet entropy, conditional sampling and phase average, synchronous cross-correlation analysis technology is suitable for EEG analysis. It can privide new quantitative parameters for diagnosis and assesment of AD.
Spontaneous EEG signals of AD patints and normal controls were quantitative analyzed by new methods. The results showed time-frequency characteristics, wavelet power spectrum distribution, wavelet entropy, wavelength of phase average waveform of scale9, Phase difference and amplitude difference of phase average waveform at scale9between occipital leads and forehead leads, as quantitative characteristics and parameters can be used for clinical diagnosis and assessment of AD.
目的：
本研究拟采用连续子变换(Contiuous wavelet transform, CWT)对轻、中、重度AD患者的自发状态下的脑电信号进行多尺度分析,与正常对照进行比较,提取AD脑电信号的时频特征,子波功率谱分布特征,以子波熵(wavelet entropy,WE)来衡量脑电信号的复杂程度,采用条件采样和相位平均技术(Conditional sampling and phase-average technique)提取分尺度的相位平均波形特征及不同导联脑电信号的同步相位差和波幅差。比较轻、中、重度AD患者与正常对照之间脑电信号时频特征、子波功率谱、子波熵、第9尺度相位平均波形的波长、不同导联的相位差和波幅差的差异,提取有助于AD诊断和病情评估的电生理指标。
方法：
1.对轻、中、重度AD患者行详细病史采集,神经系统及全身查体,行简易智能精神状态量表(MMSE),画钟试验(CDT), Hachinski缺血量表(HIS),临床痴呆评定量表(CDR),日常生活能力量表(ADL)测评,所有患者均行头核磁共振检查,对中颞叶萎缩程度进行分级。对正常对照行病史采集、查体,行MMSE、MoCA、ADL量表测评。
2.采集轻、中、重度AD患者和正常对照,安静清醒闭目状态下的数字脑电信号,采样频率200Hz,选择无伪差的脑电信号,以20秒数据存为一个数据模块,用于脑电信号定量分析。
3.脑电信号的定量分析分5个部分
(1)采用连续子变换的方法对研究对象的脑电信号进行多尺度子波分析,共分析30个尺度,绘制子波系数等值线图,显示脑电信号的时频特征。
(2)根据子波系数计算脑电信号的分尺度功率,绘制不同导联分尺度功率随频率分布图,观察脑电信号的子波功率谱分布特征。
(3)根据脑电信号分尺度功率的百分比随尺度分布的特征,提取描述脑电信号复杂程度的定量指标——子波熵。
(4)以子波系数为检测对象,用条件采样方法检测第9尺度(对应频率中心10Hz)脑电信号,并对脑电信号的同类事件进行相位对齐叠加平均,获得该尺度脑电信号的相位平均波形。
(5)采用同步互相关分析方法,对不同导联同步采集的脑电信号相位平均波形进行时间相位分析,获得不同导联相位平均波形的相位差和波幅差。
4.统计学分析采用SPSS13.0统计软件包。计量资料以均数±标准差(X±S)表示。计数资料采用χ2检验。三组及以上计量资料比较采用单因素方差分析,Levene方差齐性检验,两两比较采用LSD；计量资料单因素之间相关性分析采用Pearson相关分析。显著性检验标准为α=0.05。
结果：
(1)正常对照尺度丰富,节律性活动明显,在10Hz,1Hz,0.1Hz附近三个频带上形成稳定的节律性活动,且不同尺度间脑电信号密切关联,而AD患者自发脑电信号的时频特征表现为,尺度单一,节律性活动不稳定,节律性活动在1Hz附近明显,失去正常脑电信号的多尺度间相互关联的时频结构特征,AD患者脑电信号的多尺度时频特征随病情的加重逐渐演变。
(2)正常对照自发脑电信号的子波功率谱随频率分布较宽,在0.1Hz、1Hz、10Hz附近存在3个低而宽的功率峰,而AD患者自发脑电信号的子波功率谱分布特征为频率分布较窄,1Hz附近的功率峰增高,而0.1Hz、10Hz附近的功率峰降低,且随病情的加重逐渐演变。
(3)轻、中、重度AD患者自发脑电信号不同导联的子波熵均低于正常对照(P<0.01),且与AD患者MMSE评分呈正相关(P<0.01),说明认知障碍的程度越重,脑电信号的子波熵越低,脑电信号的多尺度复杂程度越低。
(4)轻、中、重度AD患者自发脑电信号不同导联第9尺度相位平均波形的波长均大于正常对照(P<0.01),且与AD患者MMSE评分呈负相关(P<0.01)。说明认知功能障碍的程度越重,第9尺度相位平均波形的波长越长,在这一尺度范围内脑电信号的频率越慢。
(5)正常对照从枕部导联到额部导联存在明显的相位差和波幅差,枕部导联与额部导联的相位差大约π/2相位,而AD患者自发脑电信号不同导联第9尺度相位平均波形的相位差和波幅差较正常对照减少,重度AD患者脑电信号不同导联第9尺度相位平均波形的相位差和波幅差几乎为零,这一结果与AD患者脑电信号的a节律的泛化,a节律的前移相一致。
结论：
基于子波变换、子波功率谱、子波熵、条件采样和相位平均、同步互相关分析等技术建立的脑电信号多尺度分析方法适合于脑电信号的定量客观分析和AD的临床辅助诊断。
通过对AD患者自发脑电信号的多尺度定量分析,结果表明AD患者脑电信号的时频特征、子波功率谱分布特征、子波熵、第9尺度相位平均波形的波长、不同导联第9尺度相位平均波形的相位差和波幅差等定量特征和参数可以作为辅助AD临床诊断和病情评估的神经电生理指标。
With the aging of the population, the incidence of Alzheimer's disease (AD) is keeping increasing, researches on biomarkers of AD have drawn more and more attention. In recent years, researches on gene, pathology, and imaging of AD have made some progress. EEG, as a kind of neurophysiological curve, accompanies with whole person's life. It records brain electrical activities of perception, cognition, thinking and execution instantaneously, and contains rich information. For research of cognitive function, EEG is one of the best tools. However, the analysis of EEG in clinic still relies on the naked eye recognition and empirical judgment of the doctor. EEG, as a tool for the diagnosis of AD. has not yet played its due role. Using new mathematical methods for the quantitative analysis of the EEG signal, extracting valuable objective quantitative information and parameters has important value for the diagnosis and assessment of AD. At the same time it may provide new ideas for the study of the electrophysiological mechanisms in AD.
Objective:
In this study, continuous wavelet transform (CWT) is introduced for multi-scale analysis of spontaneous EEG in mild, moderate and severe AD patients, comparing with normal controls. Wavelet power spectrum is used to characterize the EEG time-frequency multi-scale features in AD patients. Wavelet entropy, as a quantitative parameter, is introduced to measure the complexity of EEG. Conditional sampling and phase-average technique obtain the phase-average wavelength, synchronous phase difference and amplitude difference from leads at different brain area. Time-frequency characteristics, such as wavelet power spectrum, wavelet entropy, average wavelength of scale9, the lead phase difference and amplitude differences, were compared among light, moderate, severe AD patients and normal controls. The aim of this study is to find useful quantitative electrophysiological parameters of spontaneous EEG for the diagnosis and assessment of AD.
Methods:
1. Detailed history, nervous system and systemic examination was underwent for light, moderate and severe AD patients. Assessment of MMSE, CDT, HIS, CDR, ADL were made for all AD patients.temporal lobe atrophy(MTA) of MRI were classified by Visual assessment of MTA. Detailed history, nervous system and systemic examination were underwent, Assessment of MMSE, MoCA and ADL were evaluated for normal controls.
2. Raw digital EEG signals were cllected at quiet, sober eye-close state for light, moderate, severe AD patients and normal controls. Sampling frequency was200Hz, Artifact-free digital EEG data was selected and storaged as20seconds segment, for quantitative analysis.
3. Quantitative analysis of the EEG is divided into five parts
(1) Multi-scale quantitative analysis was made for EEG data recored at different brain eara by CWT. Time-frequency characteristics of30scales were analyzed, the wavelet coefficient contour map of EEG.were drawn for AD patients and normal controls.
(2) Sub-scale wavelet power was calculated according to wavelet coefficients for the EEG data of all the leads, maps of sub-scale power spectrum distribution with frequency were drawn, to observe the characteristics of wavelet power distribution of EEG in AD patients and normal controls.
(3) Wavelet entropy, as a quantitative parameters to measure the complexity of the EEG was extracted according to the percentage of the sub-scale power distribution with frequency of EEG in AD patients and normal controls.(4) EEG signal of scale9(corresponding to the frequency center of10Hz) was tested by wavelet coefficient, and detected similar incidents using by conditional sampling was superimposed and averaged in the phase alignment. EEG phase average waveform of scale9was obtained.
(5) The phase-average waveform of the different lead simultaneously acquisied by synchronous cross-correlation method, the differents of phase and amplitude of phase averaged waveforms between the leads at different brain area were obtained.
4. SPSS13.0statistical package was used for statistical analysis. Measurement data were expressed as (X±S). Count data were analyzed by χ2test.measurement data≥3groups were compared using single factor analysis of variance, and homogeneity of variance, pairwise comparisons of LSD. Single factor correlation analysis between measurement data using Pearson correlation analysis. Significant test for a=0.05.
Results:
(1) Rich scale, stable rhythmic activities near the three frequency bands of10Hz,1Hz,0.1Hz, and closed association between neighber scales was the time-frequency characteristics of EEG of normal controls. However time-frequency feather of AD patients was characterized by single scale, instability rhythmic activities, obvious rhythmic activity near1Hz, and loss of the normal EEG of multi-scale interconnected time-frequency structural. The time-frequency characteristics of EEG in AD patients evolved with the worsen of the disease.
(2) There were three low and wide power peaks near the frequecy of0.1Hz,1Hz,10Hz on wavelet power spectrum distribution of spontaneous EEG in normal controls. While the wavelet power spectrum distribution in patients with AD was characteristic for a narrow power peak near1Hz, with the aggravation of the disease evolved, the power peak near1Hz increased, and power peak near0.1Hz,10Hz decreased.
(3) Wavelet entropy of spontaneous EEG recorded from all leads in Mild, moderate and severe AD patients were lower than normal controls (P<0.01). Wavelet entropy and the MMSE score was positively correlated (P<0.01). This shows that the more severe cognitive impairment, the lower wavelet entropy, the lower complexity of the EEG.
(4) Wavelength of phase average waveform of scale9recorded from all leads is greater than the normal controls (P<0.01), Wavelength of phase average waveform of scale9and MMSE score was negtivly correlated (P<0.01). This shows that the more severe cognitive impairment, the longer the wavelength of phase average waveform of scale9, the slower EEG frequency within the range of this scale.
(5) There exist significant phase and amplitude difference of phase average waveform at scale9for spontaneous EEG between occipital leads and forehead leads in normal controls with phase difference between occipital leads and forehead leads is about π/2phase. Amplitude of phase average waveform of occipital leads at scale9is higher than that of forehead lead. Phase difference and amplitude difference of phase average waveform at scale9between occipital leads and forehead leads in AD patients decrease when compared with normal controls while phase difference and amplitude difference in sever AD patients are almost negligible. The result is consistent with alpha rhythm diffused and forward transfer in AD patients on visual EEG.
Conclusion:
The new EEG multi-scale analysis methods including wavelet transform, wavelet power spectrum, wavelet entropy, conditional sampling and phase average, synchronous cross-correlation analysis technology is suitable for EEG analysis. It can privide new quantitative parameters for diagnosis and assesment of AD.
Spontaneous EEG signals of AD patints and normal controls were quantitative analyzed by new methods. The results showed time-frequency characteristics, wavelet power spectrum distribution, wavelet entropy, wavelength of phase average waveform of scale9, Phase difference and amplitude difference of phase average waveform at scale9between occipital leads and forehead leads, as quantitative characteristics and parameters can be used for clinical diagnosis and assessment of AD.
引文
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