Identification of Deep Sleep and Awake with Computational EEG Measures

The objective of the present work was to examine identification of deep sleep and awake with computational analysis of sleep EEG traces from central brain regions. All-night EEG traces from a total of 56 male subjects, 22 healthy control subjects and 34 age-matched apnea patients, were examined. A spectral mean frequency measure, a Hilbert transform based EEG amplitude and a correlation coefficient method were compared. The EEG amplitude provided a good identification of deep sleep, reaching 86.25% but was relatively poor in the identification of wakefulness, reaching 39.06%. Mean frequency provided a relatively good identification of deep sleep and awake, reaching 84.66% and 77.67%, respectively, while the correlation coefficient produced the lowest results of 37.89% and 44.43%. Optimal threshold values for deep sleep and awake identification were determined as 4.20 and 9.76 Hz, respectively, for the mean frequency measure. Mean frequency measure can be used to provide overall context information about sleep depth for automated sleep EEG analysis methods.     

Anteroposterior Difference in EEG Sleep Depth Measure is Reduced in Apnea Patients

In the present work, mean frequencies of FFT amplitude spectra from six EEG derivations were used to provide a frontopolar, a central and an occipital sleep depth measure. Parameters quantifying the anteroposterior differences in these three sleep depth measures during the night were also developed. The method was applied to analysis of 30 all-night recordings from 15 healthy control subjects and 15 apnea patients. Control subjects showed larger differences in sleep depth between frontopolar and central positions than the apnea patients. The relatively reduced frontal sleep depth in apnea patients might reflect the disruption of the dynamic sleep process caused by apneas.     

Computer Based Synchronization Analysis on Sleep EEG in Insomnia

Inter-hemispheric sleep EEG coherence is studied in 10 subjects with psycho physiological insomnia, in 10 with paradoxical insomnia, and in 10 matched controls through different states of the sleep/wakefulness cycle. Inter hemispheric EEG coherence between central electrode pairs are compared to each other within these groups. A linear measure called as Coherence Function (CF) and a nonlinear measure called as Mutual Information (MI) are performed by using the Information Theory Toolbox in the present sleep EEG synchronization study. Regarding as tests, for all-night EEG recordings of participants, both measures indicate higher degree of EEG coherence for insomnia than for controls. The results further validate inter-hemispheric CF as a sign of activity in insomnia where the EEG series from stage2, REM sleep and the eyes closed waking state. In particular, the CF is found to be more useful tool than the MI for detection of insomnia when the power spectral density estimations of sleep stages are provided by the Burg Method. In conclusion, the CF provides insights into functional connectivity of brain regions during sleep. Since the CF has a characteristic shape for sleep states, it can be proposed to identify the degree of EEG complexity depending on sleep disorders.     

The Limitations of Decision Trees and Automatic Learning in Real World Medical Decision Making

The decision tree approach is one of the most common approaches in automatic learning and decision making. The automatic learning of decision trees and their use usually show very good results in various “ theoretical” environments. But in real life it is often impossible to find the desired number of representative training objects for various reasons. The lack of possibilities to measure attribute values, high cost and complexity of such measurements, and unavailability of all attributes at the same time are the typical representatives. For this reason we decided to use the decision trees not for their primary task—the decision making—but for outlining the most important attributes. This was possible by using a well-known property of the decision trees—their knowledge representation, which can be easily understood by humans. In a delicate field of medical decision making, we cannot allow ourselves to make any inaccurate decisions and the “tips,” provided by the decision trees, can be of a great assistance. Our main interest was to discover a predisposition to two forms of acidosis: themetabolic acidosis and respiratory acidosis, which can both have serious effects on child's health. We decided to construct different decision trees from a set of training objects. Instead of using a test set for evaluation of a decision tree, we asked medical experts to take a closer look at the generated trees. They examined and evaluated the decision trees branch by branch. Their comments show that trees generated from the available training set mainly have surprisingly good branches, but on the other hand, for some, no medical explanation could be found.     

Estimation of Sleep Stages by an Artificial Neural Network Employing EEG, EMG and EOG

Analysis and classification of sleep stages is essential in sleep research. In this particular study, an alternative system which estimates sleep stages of human being through a multi-layer neural network (NN) that simultaneously employs EEG, EMG and EOG. The data were recorded through polisomnography device for 7 h for each subject. These collective variant data were first grouped by an expert physician and the software of polisomnography, and then used for training and testing the proposed Artificial Neural Network (ANN). A good scoring was attained through the trained ANN, so it may be put into use in clinics where lacks of specialist physicians.     

癫痫脑电信号自动识别方法研究

在目前临床脑电医生常用的脑癫痫信号识别方法基础上，提出了一种利用计算机自动识别脑癫痫信号的方法及实现过程，该方法通过对脑电信号的形态特征的提取与在时空域进行综合分析的方法，模拟脑电专家的逻辑推理过程，将脑癫痫信号分为５类：尖波、棘波、尖慢波、棘慢波和多棘慢波来识别。经３０例临床脑电信号检验，本系统检出率达９０．３８％，证明了系统的有效性。     

基于特征融合的ARMA短时睡眠状态分析

针对短时睡眠的特点,结合自回归-移动平均模型(Auto-Regressive and Moving Average Model,ARMA)对短时睡眠过程中的睡眠状态变化进行分析研究。以白天短时睡眠中记录的脑电信号为研究对象,首先,从脑电信号中提取了3个与短时睡眠过程相关的特征参数,采用条件概率方法对特征参数进行融合处理,计算得到一个表征睡眠状态的参数;然后,通过ARMA模型分析睡眠状态的变化趋势;最后,采用支持向量机(Support Vector Machine,SVM)方法将整个短时睡眠过程进行了睡眠状态的自动判别,并与人工判别进行比较。结果表明,基于特征融合的ARMA模型显著提高了睡眠状态分析的准确率,7组测试数据得到的平均准确率为88.7%。一方面,特征融合能够有效地提高数据处理速度,为睡眠状态实时检测提供有利的数据处理方式;另一方面,ARMA模型的预测作用,能够分析受试者的睡眠状态变化趋势,为进一步调整和控制睡眠时长提供客观评价依据。     

Artificial Neural Network and Wavelet Based Automated Detection of Sleep Spindles, REM Sleep and Wake States

Backpropagation artificial neural network (ANN) has been designed to classify sleep–wake stages. Four hours continuous three channel polygraphic signals such as EEG (electroencephalogram), EOG (electrooculogram) and EMG (electromyogram) from conscious subjects were digitally recorded and stored in computer. EOG and EMG signals were used for manual identification of sleep states before training and testing of ANN. The percentages power of the 2 s epochs of the digitized EEG signals from each of three sleep–wake patterns, sleep spindles (SS), rapid eye movement (REM) sleep and awake (AWA) sates, were calculated and analyzed to select the manually confirmed sleep–wake states for each epoch. Further, second order Daubechies mother wavelet has been used to get the wavelet coefficients for the selected EEG epochs. The wavelet coefficients for the EEG epochs (64 data) were selected as inputs for the training the network and to classify SS, REM sleep and AWA stages. The ANN architecture used (64–14–3) in present study shows overall very good agreement with manual sleep stage scoring with an average of 95.35% for all the 1,140 samples tested from SS, REM and AWA stages. This architecture of ANN was also found effectively differentiating the EEG power spectra from different sleep–wake states (96.84% in SS, 93.68% in REM sleep, 95.52% in AWA state). The high performance observed with the system based on wavelet coefficients along with the ANN, highlights the need of this computational tool into the field of sleep research. Certificate of Originality  This is to certify that the article submitted for publication in Journal of Medical Systems has not been published, nor is being considered for publication, elsewhere.     

动态脑电图监测癫痫患儿异常睡眠纺锤波与癫痫病因关系的研究

目的:探讨动态脑电图监测癫痫患儿异常睡眠纺锤波与癫痫病因的关系。方法:对2000.12～2006.12利用动态脑电图仪监测到的癫痫患儿异常睡眠纺锤波38例进行分析。结果:本组38例异常睡眠纺锤波的癫痫患儿中,睡眠纺锤波缺失29例(76.3%),其中,两侧睡眠纺锤波缺失23例,(79.3%),左侧缺失4例(13.8%),右侧缺失2例(6.9%)。睡眠纺锤波异常9例(23.7%),其中,左侧不对称5例(55.5%),右侧不对称4例(44.4%)。以上对象的清醒期记录正常17例(44.7%)。异常21例(55.3%),其中,非特异性异常15例(71.4%),痫样波6例(29.6%)。结论:儿童癫痫睡眠纺锤波缺失、不对称、单侧睡眠纺锤波减弱均为异常,与慢性弥漫性器质病变或偏瘫有关,本组还发现急性弥漫性脑病变所致的癫痫中,睡眠纺锤波缺失比慢性脑病变者更为常见。说明动态脑电图在监测儿童癫痫异常睡眠纺锤波与癫痫病因的关系有着重要的作用。     

甲基泼尼松龙冲击治疗在癫痫伴睡眠癫痫电持续状态中的应用

目的:探讨采用甲基泼尼松龙冲击治疗癫痫伴睡眠癫痫电持续状态患儿临床效果。方法:对36例癫痫伴睡眠癫痫电持续状态患儿入院资料进行分析,将患儿根据入院时间顺序分为两组。对照组采用传统方法治疗,试验组采用甲基泼尼松龙冲击治疗,比较两组患儿临床治疗效果。结果:试验组患儿治疗总有效率88.89%高于对照组(66.67%)(P<0.05)。治疗前脑电图检测显示,所有患儿脑电图均为ESES表现,脑电图背景活动正常,睡眠结构大致正常。治疗后脑电图显示,试验组16例(88.89%)脑电图治疗有效高于对照组12例(66.67%)(P<0.05)。结论:对癫痫伴睡眠癫痫电持续患儿采用甲基泼尼松龙冲击治疗效果比较显著,患儿治疗后临床症状明显改善,值得推广使用。     

A study to determine the accuracy of a computerized algorithm for interpretation of EEGs

The main use of computerized EEG has been in sleep studies. A comprehensive system of interpreting routine EEGs by computers has not yet been developed and is technically difficult. We have tried to incorporate computers in the analysis and interpretation of EEGs by using information obtained from visual analysis of EEG in the present work. The purpose of this study was to determine the accuracy of such an algorithm. An electroencephalographer visually analyzed routine EEGs and the data was entered into an EEG Worksheet. The electroencephalographer then interpreted the data and a report was dictated and transcribed. Data from the EEG Worksheet was entered into a computer for interpretation, clinical correlation, and report preparation. Results indicate that the algorithm used with the EEG Worksheet can correctly interpret and clinically correlate visually-analyzed EEG data entered into a computer and reduce time for EEG report generation.     

脑性瘫痪儿童脑CT与脑电图的对比研究

目的 对脑瘫患儿的脑CT及脑电图资料进行对比分析,探讨其意义。方法 对54例脑瘫患儿同时期进行脑CT及脑电图检查。结果 脑CT异常率为59．3％,主要表现为脑室扩大、皮质萎缩、脑软化;脑电图异常率为72．2％,多表现为低电压、慢波节律异常、睡眠纺锤波缺失及发作波。二者符合率为46．3％,无显著性差异。结论 脑CT和脑电图二者结合对脑瘫的诊断治疗可提供科学依据。     

Singular Spectrum Analysis of Sleep EEG in Insomnia

In the present study, the Singular Spectrum Analysis (SSA) is applied to sleep EEG segments collected from healthy volunteers and patients diagnosed by either psycho physiological insomnia or paradoxical insomnia. Then, the resulting singular spectra computed for both C3 and C4 recordings are assigned as the features to the Artificial Neural Network (ANN) architectures for EEG classification in diagnose. In tests, singular spectrum of particular sleep stages such as awake, REM, stage1 and stage2, are considered. Three clinical groups are successfully classified by using one hidden layer ANN architecture with respect to their singular spectra. The results show that the SSA can be applied to sleep EEG series to support the clinical findings in insomnia if ten trials are available for the specific sleep stages. In conclusion, the SSA can detect the oscillatory variations on sleep EEG. Therefore, different sleep stages meet different singular spectra. In addition, different healthy conditions generate different singular spectra for each sleep stage. In summary, the SSA can be proposed for EEG discrimination to support the clinical findings for psycho-psychological disorders.     

A Hybrid Fuzzy-Neural System for Computer-Aided Diagnosis of Ultrasound Kidney Images Using Prominent Features

The objective of this work is to develop and implement a computer-aided decision support system for an automated diagnosis and classification of ultrasound kidney images. The proposed method distinguishes three kidney categories namely normal, medical renal diseases and cortical cyst. For the each pre-processed ultrasound kidney image, 36 features are extracted. Two types of decision support systems, optimized multi-layer back propagation network and hybrid fuzzy-neural system have been developed with these features for classifying the kidney categories. The performance of the hybrid fuzzy-neural system is compared with the optimized multi-layer back propagation network in terms of classification efficiency, training and testing time. The results obtained show that fuzzy-neural system provides higher classification efficiency with minimum training and testing time. It has also been found that instead of using all 36 features, ranking the features enhance classification efficiency. The outputs of the decision support systems are validated with medical expert to measure the actual efficiency. The overall discriminating capability of the systems is accessed with performance evaluation measure, f-score. It has been observed that the performance of fuzzy-neural system is superior compared to optimized multi-layer back propagation network. Such hybrid fuzzy-neural system with feature extraction algorithms and pre-processing scheme helps in developing computer-aided diagnosis system for ultrasound kidney images and can be used as a secondary observer in clinical decision making.     

应用于无线多导睡眠监测的皮肤贴电极制备及验证

目的：开发一种适用于便携式睡眠监测设备的电极,并对该电极监测到的眼动图（electrooculogram, EOG）和脑电图（electroencephalography, EEG）信号结果进行验证。方法：通过微机电系统（microelectromechanical systems, MEMS）技术制备柔性电极,其中电极为金/铬双层结构,柔性衬底采用具有良好生物兼容性的聚对二甲苯（parylene）。电极被设计并制作成网状结构,使得下层的胶带能更有效地与皮肤贴合,降低与皮肤的接触阻抗。在电极制作完成后,使用CHI660E电化学工作站对电极的交流阻抗特性进行测试,此外,将电极连接到包含生物信号采集和数字化处理专用芯片的无线信号采集套件,采集志愿者眼周不同位点、不同方向眼动的电信号,并对眼动信号的信噪比进行分析。最后使用标准多导睡眠监测仪来对比皮肤贴电极和金杯电极采集脑电信号时的噪声幅度。结果：皮肤贴电极在小于100 Hz交流电范围的阻抗为4~13 kΩ,使用皮肤贴电极可以采集到不同方向眼动的眼电信号,皮肤贴电极采集到的脑电信号噪声幅度低于金杯电极采集到的脑电信号噪声幅度。结论：皮肤贴电极可以作为开发适用于便携式睡眠监测设备中眼动电信号和脑电信号监测的备选电极。     

面向特征抽取的EEG信号结构自适应稀疏分解模型建立

课题目的是研究面向特征抽取鉴别EEG信号是否具有癫痫的表征,从而实现EEG信号的自动检测与分析。具体方法是结合临床EEG分析的先验知识,模拟结构匹配性稀疏表示的层次处理机制来实现对EEG信号的结构自适应稀疏分解。结果发现：匹配追踪迭代选择的Gabor字典原子能够匹配EEG信号的内在结构,并具有显式的形态结构参数如位置、尺度、幅度等。由此可得出结论：基于EEG信号形态结构基础建立的过完备原子库,使得稀疏分解获取的信号时频结构参数同人工视觉分析标准建立了直接联系。应用这些时频结构参数与先验参数进行比对可直接判定是否为特征波形。     

数字多媒体技术和医疗脑波检测技术结合辅助矫正精神问题的探讨

现在,针对精神亚健康问题的加重,国内外的一些研究机构与医疗机构,已将脑电波监测技术用于精神问题的辅助治疗.脑电波技术,在多年的研究中形成了十分完善的理论体系,这个理论体系在传统医疗行业中具有极高的权威性.随着科技的发展,脑电波技术具有了走出实验室,进入人们生活中的技术基础.诸多试验证明,利用脑电波的监测技术辅助训练,可以很好地改善患者的精神状态,这为今后脑电波监测技术辅助矫正精神问题的研究提供了很好的理论依据和发展方向.     

204例脑性瘫痪的异常脑电图分析

目的：观察脑性瘫痪的异常脑电图特点，为脑瘫的早期诊断及治疗提供依据。方法：分析用ＮＤ－８２９２型脑电图机，按国际１０－２０系统安放ＦｐｌＦｐ２，Ｆ３Ｆ４，Ｔ３Ｔ４，Ｃ３Ｃ４，Ｏ１Ｏ２，Ｃｚ头皮电极所描记的２０４例脑瘫异常脑电图。结果：轻度异常６８例（３３．３％），中度异常８２（４０．２％），高度异常１６例（７．９％），局灶性异常３８例（１８．６％）。结论：脑性瘫痪异常脑电图特点①以弥漫性改变为主，少见单纯局灶性改变，②低电压驼峰波，睡眠纺锤波，低电压节律失调及驼峰波，睡眠纺锤波缺如，脑瘫的异常改变与病因病情及智能障碍有明显关系。     

“三重一大”集体决策制度在医院临床科室管理中落实及执行情况的调查分析

“三重一大”集体决策制度是我党贯彻落实科学发展观在制度建设方面推出的一项重要举措。自2008年以来,我院将“三重一大”集体决策制度延伸至临床科室,通过不断完善制度建设、加大执行情况的监督,不仅提高了临床科室决策的民主化科学化水平、推进临床科室持续有效发展,还增强了临床科室领导干部廉洁从政的自觉性,对加强党风廉政建设和反腐败工作具有重要的意义。本文通过对“三重一大”集体决策制度在临床科室管理中落实及执行情况的调查分析指出,建立临床科室民主管理集体决策的组织体系、完善临床科室民主管理集体决策的运行机制、通过宣传教育增强临床科室“三重一大”集体决策制度的执行意识、加大对临床科室“三重一大”集体决策制度执行情况的督查力度、持续促进临床科室“三重一大”集体决策制度的规范化管理,“一岗双责”责任到人,是提高临床科室“三重一大”集体决策制度执行力的重要方法。     

发作间期癫痫患者自然睡眠脑电图与常规清醒脑电图的对比分析

目的探讨自然睡眠脑电图（EEG）对发作间期癫痫患者的诊断价值。方法选取2011年4月～2012年2月浙江省东阳市人民医院的73例发作间期癫痫患者、25例间歇期晕厥患者、19例间歇期偏头痛患者、31例发作间期短暂性脑缺血发作患者作为研究对象,分别在自然睡眠状态下、常规清醒状态下对上述研究对象进行EEG检查,比较其灵敏度和特异度。结果自然睡眠EEG诊断发作间期癫痫患者的灵敏度、特异度分别为75.3%、100.0%。常规清醒EEG诊断发作间期癫痫患者的灵敏度、特异度分别为31.5%、100.0%。自然睡眠EEG诊断发作间期癫痫患者的灵敏度显著高于常规清醒EEG（P〈0.05）,两种EEG诊断发作间期癫痫患者的特异度相比差异无统计学意义（P〉0.05）。结论与常规清醒EEG相比,自然睡眠EEG诊断发作间期癫痫的灵敏度较高,有利于降低癫痫患者的漏诊率。