一种高分辨率的时频分析方法用于睡眠脑电图梭形波识别 …

提出了一种新的睡眠梭形图（ｓｌｅｅｐ ｓｐｉｎｄｌｅ）识别方法－离散Ｇａｂｏｒ谱分解,在研究睡眠脑电波特征的基础上利用这一高分辨率的时频分析方法对睡眠脑电进行了分析处理。结果显示,离散Ｇａｂｏｒ谱方法可有效地从睡眠脑电波中识别出梭形波,为睡眠自动分阶的实现提供了特征。该方法识别梭形波的准确率已接受神经内科专家目测的水平,识别准确率达到９３％以上。睡眠梭形波的自动识别为研究睡眠的神经内科专家解除了阅     

一种高分辨率的时频分析方法用于睡眠脑电图梭形波识别的研究

提出了一种新的睡眠梭形波(sleep spindle)识别方法——离散Gabor谱分解,在研究睡眠脑电波特征的基础上利用这一高分辨率的时频分析方法对睡眠脑电进行了分析处理。结果显示,离散Gabor谱方法可有效地从睡眠脑电波中识别出梭形波,为睡眠自动分阶的实现提供了特征。该方法识别梭形波的准确率已接近神经内科专家目测的水平,识别准确率达到93%以上。睡眠梭形波的自动识别为研究睡眠的神经内科专家解除了阅读睡眠脑电图谱的繁冗工作,为进一步研究睡眠生理提供了有用的信息。     

利用小波级数检测睡眠EEG中的K-复合波

本文提出了一种利用小波级数检测睡眠脑电中Ｋ－复合波的方法。这种方法使用Ｄａｕｂｅｃｈｉｅｓ正交小波基,把ＥＥＧ信号分解成４个尺度上的小波级数,利用最大尺度的信号逼近检测Ｋ－复合波中的大慢波脉冲,然后,利用信号细节检测紧跟大慢波脉冲之后的梭形波。     

容易被误诊为“样放电”的几种正常睡眠脑电波形

随着视频脑电图和动态脑电图的普及,睡眠脑电图逐渐成为脑电图检查的重要部分。临床拟诊为癫的患者,凡清醒常规脑电图未能检出样放电者进行睡眠脑电图描记,可明显提高样放电的检出率[1]。临床脑电图工作者必须熟悉和正确识别各种不同的睡眠脑电图波形。无论成人或儿童,睡眠和清醒脑电图均明显不同,不同年龄和不同睡眠阶段也有不同的脑电图表现。睡眠时,由于人体内环境和大脑皮层神经元兴奋性的变化,会出现许多特征性波形,这些特征性睡眠波形是确定不同睡眠阶段的最主要标准。有些特征性睡眠脑电波形,其形态、频率和出现方式等方面酷似“样放电”,尤其在儿童期多见[2]。如果对这些特征性波形不能正确判断,则容易误诊,造成假阳性结果,为临床医生提供错误的信息。现将正常睡眠脑电波形中容易被误诊为“样放电”的几种特征性波形介绍如下。1入睡前高度同步化入睡前高度同步化(hypnagogic hypersynchro-nous)又称思睡期节律性θ活动,入睡前高度同步化节律性θ波暴发,思睡期节律性慢活动,入睡期暴发性慢波[3-5]。这种波形是在思睡期向浅睡期过渡时,主要在入睡前深度思睡时出现的,是与低幅混合频率背景活动明显不同的暴发性慢活动(图1)。波形特...     

低频光诱发脑电在浅睡期的样本熵复杂度分析

睡眠障碍患者通常表现为从浅睡期进入深睡期存在困难,分析浅睡期脑电波的变化对研究睡眠效率和睡眠质量至关重要。通过分析低频光刺激下睡眠过程中脑电波的复杂度值变化,研究人在浅睡期脑电波对光刺激的响应,进而探讨外部光刺激对睡眠过程中脑电波的影响。使用美国neuroscan型脑电图仪,采集10例志愿者的光刺激睡眠和正常睡眠的脑电数据。首先,利用时频分析,对睡眠过程中的脑电信号进行分期,获得浅睡期脑电信号;然后,使用小波包分解,获得该期脑电波的各频段分量（δ波、θ波、α波和纺锤波）;接着,采用样本熵算法,分别计算浅睡期脑电信号的复杂度以及各频段脑电波的复杂度;最后,对志愿者在光刺激（5 Hz）和正常睡眠下浅睡期脑电复杂度进行比较,研究光刺激对脑电复杂度的响应情况。结果显示：在低频光刺激下,浅睡期脑电波复杂度的均值为0514 15,明显低于正常睡眠复杂度的均值0589 23,在中央区和顶区有显著性差异（P<005）。研究表明,5 Hz光刺激可诱发浅睡期θ波的同步响应,增强脑电波的节律性,有助于更好地进入深度睡眠。     

基于BP网络的睡眠分阶方法及睡眠质量评估研究

我们利用不同睡眠期脑电复杂性特征与睡眠深度的关系及多道睡眠图功率谱特征,基于3层BP网络进行了睡眠自动分阶的研究,并提出了能部分反映睡眠质量的睡眠时间、浅睡时间、深睡时间、REM时间、觉睡比、醒转次数等参数。通过6例全睡眠监护实验说明,该方法可为睡眠质量的评价提供途径。     

卵巢有梭形细胞成分内膜样癌伴肝样癌1例报道及文献复习

目的：研究卵巢有梭形细胞成分宫内膜样癌伴肝样癌的临床病理特征以及肝样癌的组织发生。方法：收集和分析病人的临床病理资料。石蜡切片免疫组化ＡＢＣ法染色。结果：①１例６２岁老妇，患有原发性高度恶性卵巢癌，并伴有ＡＦＰ明显升高。ＡＦＰ常随病情变化而变化；②病理特征主要为有梭形细胞成分宫内膜样癌和明显肝样癌成分。免疫组化染色显示肝样癌及其癌细胞内外玻璃小体ＡＦＰ阳性；内膜样癌中腺上皮Ｋｅｒａｔｉｎ、ＥＭＡ、ＥＲ阳性；而梭形细胞则Ｋｅｒａｔｉｎ、ＥＭＡ和Ｖｉｍ阳性。而Ｄｅｓｍｉｎ、Ｍｙｏｇｌｏｂｉｎ、ＧＡＦＰ、ＨＣＧ所有瘤细胞均为阴性；③由于ＡＦＰ具有免疫抑制作用，患者预后不良。结论：以上结果表明，有梭形细胞成分卵巢宫内膜样癌伴肝样癌为卵巢一种特殊临床病理实体，反复复发后已完全向肝样癌转化     

三种蚤酯酶同功酶的比较研究

本研究使用垂直板型聚丙烯酰胺凝胶电泳的方法，比较分析了印鼠客蚤Ｘｅｎｏｐｓｙｌａｃｈｅｏｐｉｓ（Ｒｏｔｈｓｃｈｉｌｄ，１９０３），不等单蚤Ｍｏｎｏｐｓｙｌｌｕｓａｎｉｓｕｓ（Ｒｏｔｈｓｃｈｉｌｄ，１９０７）和棕形额蚤指名亚种Ｆｒｏｎｔｏ－ｐｓｙｌａｓｐａｄｉｘｓｐａｄｉｘ（ＪｏｒｄａｎｅｔＲｏｔｈｓｃｈｉｌｄ，１９２１）不同发育时期的酯酶同功酶谱。结果表明这三种蚤的Ｅｓｔ同功酶谱有明显且稳定的区别。印鼠客蚤的Ｅｓｔ同功酶显带１５条，含主带６条；不等单蚤的显带２１条，含主带２条；棕形额蚤指名亚种的显带１８条，含主带２条。这三种蚤的Ｅｓｔ同功酶不但在酶带的数目、深浅和主次酶带上有差异，而且每种蚤还有各自特异的标志酶带。本研究结果还显示，雌雄蚤的Ｅｓｔ同功酶谱无差异；同一蚤种不同发育时期的Ｅｓｔ同功酶谱绝大多数是相同的，但是可以通过酶带数目、深浅和特有酶带来鉴别同一蚤种的不同虫态和同一虫态的不同发育时期。同时，作者还探讨了相近迁移率酶带与亲缘关系以及蚤总科与角叶蚤总科Ｅｓｔ同功酶酶谱可能存在的差异。     

低频光刺激在浅睡期的脑电响应

目的人的睡眠是有节律的,浅睡眠和深睡眠反复交替进行,分析浅睡期脑电波的变化对研究睡眠效率和睡眠质量至关重要。本文通过分析低频光刺激下睡眠过程中脑电波的变化,研究人在浅睡期脑电波对光刺激的响应,进而探讨外部光刺激对睡眠过程中脑电波的影响。方法利用美国Neuroscan型脑电图仪采集10例志愿者的光刺激睡眠和正常睡眠的脑电数据。首先,利用时频分析对睡眠过程中的脑电信号进行分期,获得浅睡期脑电信号,然后对分期后的脑电信号做傅里叶变换,获得各频段脑电波,并求其能量。最后比较并分析了志愿者在光刺激(5 Hz)和正常睡眠下浅睡期的脑电信号能量。结果在低频光刺激下,浅睡期脑电波的波能量明显高于正常睡眠,尤其在中央区和顶区增加明显。结论在光刺激下大脑皮质以更平稳的方式进入抑制状态,有助于更好地进入深睡期。     

局灶性缺血脑损伤的无创诊断技术研究

脑缺血是许多脑血管疾病的共同特征,急性短暂脑缺血也是许多脑血管疾病的先兆。本文研究证明,体感诱发电位（ＳＥＰ）的时频分布对缺血脑损伤十分敏感,与缺血性脑血管疾病有密切关系。本文建立了ＳＤ大鼠局灶性缺血脑损伤实验模型,在用一种高分辨率的时频分析方法－离散Ｇａｂｏｒ谱（ＤＧＳ）分析ＳＥＰ的基础上,对局灶性缺血脑损伤进行了研究,发现在局灶性缺血早期,损伤区域与非损伤区域ＳＥＰ的时频特征即具有明显的不同,     

Optimization of sigma amplitude threshold in sleep spindle detection

Sleep spindles are transient EEG waveforms of non-rapid eye movement sleep. There is considerable intersubject variability in spindle amplitudes. The problem in automatic spindle detection has been that, despite this fact, a fixed amplitude threshold has been used. Selection of the spindle detection threshold value is critical with respect to the sensitivity of spindle detection. In this study a method was developed to estimate the optimal recording-specific threshold value for each all-night recording without any visual scorings. The performance of the proposed method was validated using four test recordings each having a very different number of visually scored spindles. The optimal threshold values for the test recordings could be estimated well. The presented method seems very promising in providing information about sleep spindle amplitudes of individual all-night recordings.     

多分辨率小波信号分解用于大鼠睡眠纺锤波的分析

本研究首先设计了慢波睡眠期脑电信号的合成仿真信号 ,对小波基函数进行了选择 ,结果证明Coiflet 5阶小波变换对大鼠慢波睡眠期EEG信号具有较好的分解结果。据此 ,应用多分辨率小波分析法设计了提取睡眠纺锤波的算法 ,并利用该算法对安定用药后和睡眠剥夺后大鼠慢波睡眠期纺锤波的持续时间和能量变化进行了分析 ,结果表明 :安定具有延长慢波睡眠期纺锤波持续时间的作用 ,而睡眠剥夺可以增加慢波睡眠期纺锤波的能量。这些结果说明 ,小波分析算法可以提供功率谱分析无法表现的时频信息。     

Apnea patients show a frontopolar inter-hemispheric spindle frequency difference

Sleep apnea syndrome is known to disturb sleep. The purpose of the present work was to study spindle frequency in apnea patients. All-night sleep EEG recordings of 15 apnea patients and 15 control subjects with median ages of 47 and 46 years, respectively, were studied. A previously presented and validated multi-channel spindle analysis method was applied for automatic detection and frequency analysis of bilateral frontopolar and central spindles. Bilateral frontopolar spindles of apnea patients were found to show lower frequencies on the left hemisphere than on the right. Such an inter-hemispheric spindle frequency difference in apnea patients is a novel finding. It could be that the hypoxias and hypercapnias caused by apneic episodes result in local disruption in the regulation of sleep in the frontal lobes.     

Automatic analysis of electro-encephalogram sleep spindle frequency throughout the night

A fully automatic method to analyse electro-encephalogram (EEG) sleep spindle frequency evolution during the night was developed and tested. Twenty allnight recordings were studied from ten healthy control subjects and ten sleep apnoea patients. A total of 22 868 spindles were detected. The overall mean spindle frequency was significantly higher in the control subjects than in the apnoea patients (12.5Hz against 11.7Hz, respectively; p<0.004). The proposed method further identified the sleep depth cycles, and the mean spindle frequency was automatically determined inside each sleep depth cycle. In control subjects, the mean spindle frequency increased from 12.0Hz in the first sleep depth cycle to 12.6Hz in the fifth cycle. No such increase was observed in the sleep apnoea patients. This difference in the spindle frequency evolution was statistically significant (p<0.004). The advantage of the method is that no EEG amplitude thresholds are needed.     

Development and comparison of four sleep spindle detection methods

OBJECTIVE: The objective of the present work was to develop and compare methods for automatic detection of bilateral sleep spindles. METHODS AND MATERIALS: All-night sleep electroencephalographic (EEG) recordings of 12 healthy subjects with a median age of 40 years were studied. The data contained 6043 visually scored bilateral spindles occurring in frontopolar or central brain location. In the present work a new sigma index for spindle detection was developed, based on the fast Fourier transform (FFT) spectrum, aiming at approximating our previous fuzzy spindle detector. The sigma index was complemented with spindle amplitude analysis, based on finite impulse response (FIR) filtering, to form of a combination detector of bilateral spindles. In this combination detector, the spindle amplitude distribution of each recording was estimated and used to tune two different amplitude thresholds. This combination detector was compared to bilaterally extracted sigma indexes and fuzzy detections, which aim to be independent of absolute spindle amplitudes. As a fourth method a fixed spindle amplitude detector was included. RESULTS: The combination detector provided the best overall performance; in S2 sleep a 70% true positive rate was reached with a specificity of 98.6%, and a false-positive rate of 32%. The bilateral sigma indexes provided the second best results, followed by fuzzy detector, while the fixed amplitude detector provided the poorest results so that in S2 sleep a 70% true positive rate was reached with a specificity of 97.7% and false-positive rate of 46%. The spindle amplitude distributions automatically determined for each recording by the combination detector were compared to amplitudes of visually scored spindles and they proved to correspond well. Inter-hemispheric amplitude variation of visually scored bilateral spindles is also presented. CONCLUSION: Flexibility is beneficial in the detection of bilateral spindles. The present work advances automated spindle detection and increases the knowledge of bilateral sleep spindle characteristics.     

Spindles are highly heritable as identified by different spindle detectors

Study ObjectivesSleep spindles, a defining feature of stage N2 sleep, are maximal at central electrodes and are found in the frequency range of the electroencephalogram (EEG) (sigma 11–16 Hz) that is known to be heritable. However, relatively little is known about the heritability of spindles. Two recent studies investigating the heritability of spindles reported moderate heritability, but with conflicting results depending on scalp location and spindle type. The present study aimed to definitively assess the heritability of sleep spindle characteristics.MethodsWe utilized the polysomnography data of 58 monozygotic and 40 dizygotic same-sex twin pairs to identify heritable characteristics of spindles at C3/C4 in stage N2 sleep including density, duration, peak-to-peak amplitude, and oscillation frequency. We implemented and tested a variety of spindle detection algorithms and used two complementary methods of estimating trait heritability.ResultsWe found robust evidence to support strong heritability of spindles regardless of detector method (h² > 0.8). However not all spindle characteristics were equally heritable, and each spindle detection method produced a different pattern of results.ConclusionsThe sleep spindle in stage N2 sleep is highly heritable, but the heritability differs for individual spindle characteristics and depends on the spindle detector used for analysis.     

Validating an automated sleep spindle detection algorithm using an individualized approach

The goal of the current investigation was to develop a systematic method to validate the accuracy of an automated method of sleep spindle detection that takes into consideration individual differences in spindle amplitude. The benchmarking approach used here could be employed more generally to validate automated spindle scoring from other detection algorithms. In a sample of Stage 2 sleep from 10 healthy young subjects, spindles were identified both manually and automatically. The minimum amplitude threshold used by the prana ^® (PhiTools, Strasbourg, France) software spindle detection algorithm to identify a spindle was subject‐specific and determined based upon each subject’s mean peak spindle amplitude. Overall sensitivity and specificity values were 98.96 and 88.49%, respectively, when compared to manual scoring. Selecting individual amplitude thresholds for spindle detection based on systematic benchmarking data may validate automated spindle detection methods and improve reproducibility of experimental results. Given that interindividual differences are accounted for, we feel that automatic spindle detection provides an accurate and efficient alternative approach for detecting sleep spindles.     

Topographic distribution of sleep spindles in young healthy subjects

The application of an automatic sleep spindle detection procedure allowed the documentation of the topographic distribution of spindle characteristics, such as number, amplitude, frequency and duration, as a function of sleep depth and of recording time. Multichannel all-night EEG recordings were performed in 10 normal healthy subjects aged 20–35 years. Although the interindividual variability in the number of sleep spindles was very high (2.7±2.1 spindles per minute stage 2 sleep), all but two subjects showed maximal spindle activity in centro-parietal midline leads. Moreover, this topography was seen in all sleep stages and changed only slightly – to a more central distribution – towards the end of the night. On the other hand, slow (11.5–14 Hz) and fast (14–16 Hz) spindles showed a completely different topography, with slow spindles distributed anteriorly and fast spindles centro-parietally. The number of sleep spindles per min was significant depending on sleep stages, with the expected highest occurrence in stage 2, and on recording time, with a decrease in spindle density from the beginning towards the end of the night. However, spindle amplitude, frequency and individual duration was not influenced by sleep depth or time of the night.     

Sleep electroencephalogram in children with a parental history of alcohol abuse/dependence

We examined the sleep electroencephalogram (EEG) in 9- and 10-year-old children with (PH+) and without (PH−) a parental history of alcohol abuse/dependence to determine whether sleep disturbances associated with alcohol precede the onset of alcohol use. Participants slept on a fixed sleep schedule that ensured at least a 10-h time in bed for 1 week before an adaptation and baseline night. Data were collected in a four-bed sleep research laboratory. Thirty healthy boys and girls aged 9 or 10 years were classified as either PH+ or PH− based on DSM-IV criteria applied to structured parental interviews. All-night polysomnography was performed, sleep data were scored visually in 30-s epochs, and EEG power spectra were calculated for each epoch. All-night EEG spectra were calculated for rapid eye movement (REM) and non-REM (NREM) sleep, and cycle-by-cycle spectra were calculated for NREM sleep. The two groups did not differ on any sleep stage variable. All-night analyses revealed normalized power in the delta band and spindle range were lower in PH+ children. Within NREM sleep cycles PH+ children exhibited less normalized power in the delta band and spindle range compared with PH− children. This effect occurred in the first four cycles and was most pronounced in the first sleep cycle of the night. We found no signs of sleep disruption in sleep stages for PH+ children. Sleep EEG spectral differences, however, suggest that certain circuits responsible for 'protecting' sleep may be impaired in PH+ children, which may lead to disrupted sleep later in life.