应用小波熵分析大鼠脑电信号的动态变化特性

应用小波熵（一种新的信号复杂度测量方法）分析大鼠在不同生理状态下脑电复杂度的动态时变特性。采用慢性埋植电极记录自由活动大鼠的皮层EEG,使用多分辨率小波变换将EEG信号分解为δ、θ、α和β四个分量,求得随时间变化的小波熵。结果表明：在清醒、慢波睡眠和快动眼睡眠三种生理状态下,EEG的小波熵之间存在显著差别,并且在不同时期其值与各个分解分量之间具有不同的关系,其中,慢波睡眠期小波熵还具有较明显的变化节律,反映了EEG微状态中慢波和纺锤波的互补性。由此可见,小波熵既能区别长时间段EEG复杂度之间的差别,又能反映EEG微状态的快速变化特性。     

婴儿夜间睡眠脑电纺锤波的观察

婴儿夜间睡眠脑电纺锤波的观察张慧秀,朱国庆,张景行,孔秀（安徽医科大学睡眠障碍研究室,安徽医科大学附属医院合肥２３００３２）婴儿睡眠纺锤波是作为慢波睡眠（ＳＷＳ）的一个标志,也可作为中枢神经系统机能正常与否的一个指标。为了解婴儿睡眠纺锤波发育情况并明...     

慢波睡眠的激素与细胞因子调节

慢波睡眠（ＳＷＳ）是最重要的睡眠成分。近年来的研究揭示：腹外侧视前区－结节乳头核（ＶＬＰＯ－ＴＭＮ）可能是睡眠－觉醒的中枢发生部位。基底前脑吻端前列腺素Ｄ２（ＰＧＤ２）敏感性睡眠促进区（ＰＧＤ２－ＳＰＺ）参与睡眠的皖控。ＰＧＤ２延长ＳＷＳ;前列腺素Ｅ２（ＰＧＥ２）延长觉醒,抑制ＳＷＳ和快动眼睡眠（ＲＥＭＳ）。ＳＷＳ与下丘脑－垂体－肾上腺皮质轴的活动呈负相关,与生长激素的分泌呈正相关。褪黑素（ｍｅｌ     

睡眠2期脑电信号产生的生理机制模型仿真研究

目前慢波睡眠生理机制研究已有的理论及动物实验结果显示,慢波睡眠中,皮层-丘脑系统神经元存在三种不同节律的振荡:慢振荡(<1 HZ)、δ振荡(1-4Hz)和纺锤振荡(7-14Hz)。这些神经元电活动在皮层水平广泛同步化,产生慢波睡眠脑电。提出了能分别产生这三种节律的三种神经元环路模型,并将之组合简化成一个七细胞环路模型。由这样的大量环路组成的网络模型在合适的突触连接参数范围内,能在皮层处产生人类慢波睡眠脑电2期的完整波形。这一结果说明了如何将动物实验观察到的睡眠生理机制的结果与人自然睡眠活动的脑电结果联系起来,并提示脑信息处理中由微观神经元群放电特征整合为脑的宏观功能状态的主要环节。     

心电信号特征量的小波提取算法

目的:为了更准确地利用心电图(ECG)进行临床生理疾病诊断,提高心电信号的自动分析准确度.介绍了一种利用小波变换的时频局部化特性以及多分辨率特性对心电信号进行处理的算法.方法:使用定位准确.计算简便的二阶微分Mart小波使用多孔算法来对ECG中QRS波群进行标定.结果:将算法应用到MIT/BIH国际标准心电数据库进行仿真.结论:通过仿真证明,该算法能够很精确地定位QRS波群,为心电信号的后续研究打好基础.     

睡眠研究的科学前沿

关于睡眠机制的研究是一门历史悠久的学科.在过去的几十年中,运用细胞电生理学来研究睡眠取得了可喜的成果.由于种种技术上的困难,近年来该领域的研究多集中于临床和医学范围,例如嗜睡症、抑郁症等.虽说睡眠的节律性较易理解,但作为其本质———睡眠的基因和分子水平的自动平衡调节仍是一个谜.细胞因子(IL-1和TNFα)对睡眠的诱导作用已显示从分子水平上了解睡眠的可能性.到目前为止,关于睡眠的功能已有不少理论和假说,但人类对睡眠的生化机制的认识尚处于起步阶段.     

脑电信号数据压缩及棘波识别的小波神经网络方法

本文在对小波神经网络及其算法研究的基础上,提出了一种对脑电信号压缩表达和痫样脑电棘波识别的新方法。实验结果显示,小波网络在大量压缩数据的同时,能够较好的恢复原有信号,另外,在脑电信号的时频谱等高线图上,得到了易于自动识别的棘波和棘慢复合波特征,说明此方法在电生理信号处理和时频分析方面有着光明的应用前景。     

利用小波变换的一种快速预测基因方法

三周期性是大多数基因组序列的编码区所具有的主要特征.本文提出只计算1/3频率点的傅里叶频谱的快速计算方法,并用它分析DNA序列的三周期性,再利用小波变换在一定尺度下滤波来实现对DNA序列编码区的预测.理论分析和大量计算机实验证实了方法的有效性,预测效果良好.该方法运算快速,不需要任何训练组,也不依赖于现有数据库的信息.     

一个新的脑电信号分析系统：小波分析理论的运用

小波变换是一种把时间、频率（或尺度）两域结合起来的分析方法。它被誉为“分析信号的数学显微镜”。本系统将小波变换用于脑电信号分析,是一个在Ｗｉｎｄｏｗｓ３．１下开发的脑电分析系统。     

小波神经网络在脑电信号数据压缩与棘波识别中的应用

介绍了一种新的神经网络模型———小波神经网络,利用它并适当调节网络、小波基参数,实现了对脑电信号的压缩表达,较好的恢复了原有信号。另外,在其算法研究的基础上,提出了适应于非稳态和非线性信号处理的时频分析新方法。在脑电信号的时频谱等高线图上,得到了易于自动识别的棘波和棘慢复合波特征,与传统的短时傅立叶变换（ＳＴＦＴ）和Ｗｉｇｎｅｒ分布相比,此方法有更高的分辨率和自适应性,而且其时频能量分布没有交叉项干扰。     

Real-Time Monitoring of Slow-Wave Sleep by Electroencephalogram Variance

Based on statistical variance as an index of electroencephalogram (EEG) parameters, we monitored slow-wave sleep in both humans and rats in real time and on-line with a widely used personal computer. This EEG variance method may be a useful tool to carry out biological rhythm research, including sleep studies.     

Prediction of Mitochondrial Proteins Using Discrete Wavelet Transform

A new method was proposed for prediction of mitochondrial proteins by the discrete wavelet transform, based on the sequence–scale similarity measurement. This sequence–scale similarity, revealing more information than other conventional methods, does not rely on subcellular location information and can directly predict protein sequences with different length. In our experiments, 499 mitochondrial protein sequences, constituting a mitochondria database, were used as training dataset, and 681 non-mitochondrial protein sequences were tested. The system can predict these sequences with sensitivity, specificity, accuracy and MCC of 50.30%, 95.74%, 76.53% and 0.54, respectively. Source code of the new program is available on request from the authors.     

Sleep in amphibians and reptiles: a review and a preliminary analysis of evolutionary patterns

Despite the ubiquitous nature of sleep, its functions remain a mystery. In an attempt to address this, many researchers have studied behavioural and electrophysiological phenomena associated with sleep in a diversity of animals. The great majority of vertebrates and invertebrates display a phase of immobility that could be considered as a sort of sleep. Terrestrial mammals and birds, both homeotherms, show two sleep states with distinct behavioural and electrophysiological features. However, whether these features have evolved independently in each clade or were inherited from a common ancestor remains unknown. Unfortunately, amphibians and reptiles, key taxa in understanding the evolution of sleep given their position at the base of the tetrapod and amniote tree, respectively, remain poorly studied in the context of sleep. This review presents an overview of what is known about sleep in amphibians and reptiles and uses the existing data to provide a preliminary analysis of the evolution of behavioural and electrophysiological features of sleep in amphibians and reptiles. We also discuss the problems associated with analysing existing data, as well as the difficulty in inferring homologies of sleep stages based on limited data in the context of an essentially mammalian‐centric definition of sleep. Finally, we highlight the importance of developing comparative approaches to sleep research that may benefit from the great diversity of species with different ecologies and morphologies in order to understand the evolution and functions of sleep.     

Motor Imagery EEG Spectral-Spatial Feature Optimization Using Dual-Tree Complex Wavelet and Neighbourhood Component Analysis

BackgroundFrequency band optimization improves the performance of common spatial pattern (CSP) in motor imagery (MI) tasks classification because MI-related electroencephalograms (EEGs) are highly frequency specific. Many variants of CSP algorithm divided the EEG into various sub bands and then applied CSP. However, the feature dimension of MI-EEG data increases with addition of frequency sub bands and requires efficient feature selection algorithms. The performance of CSP also depends on filtering techniques.MethodIn this study, we designed a dual tree complex wavelet transform based filter bank to filter the EEG into sub bands, instead of traditional filtering methods, which improved the spatial feature extraction efficiency. Further, after filtering EEG into different sub bands, we extracted spatial features from each sub band using CSP and optimized them by a proposed supervised learning framework based on neighbourhood component analysis (NCA). Subsequently, a support vector machine (SVM) is trained to perform classification.ResultsAn experimental study, conducted on two datasets (BCI Competition IV (Dataset 2b), and BCI competition III (Dataset IIIa)), validated the MI classification effectiveness of the proposed method in comparison with standard algorithms such as CSP, Filter bank CSP (CSP), and Discriminative FBCSP (DFBCSP). The average classification accuracy obtained by the proposed method for BCI Competition IV (Dataset 2b), and BCI Competition III (Dataset IIIa) are 84.02 ± 12.2 and 89.1 ± 7.50, respectively and found significant than that achieved by standard methods.ConclusionAchieved superior results suggest that the proposed algorithm can improve the performance of MI-based Brain-computer interface devices.     

基于独立元分析和联合小波熵检测多导联ECG信号的QRS

QRS波群的准确定位是ECG信号自动分析的基础。为提高QRS检测率,提出一种基于独立元分析（ICA）和联合小波熵（CWS）检测多导联ECG信号QRS的算法。ICA算法从滤波后的多导联ECG信号中分离出对应心室活动的独立元;然后对各独立元进行连续小波变换（CWT）,重构小波系数的相空间,结合相空间中的QRS信息对独立元排序;最后检测排序后独立元的CWS得到QRS信息。实验对St.Petersburg12导联心率失常数据库及64导联犬心外膜数据库测试,比较本文算法与单导联QRS检测算法和双导联QRS检测算法的性能。结果表明,该文算法的性能最好,检测准确率分别为99.98%和100%。     

小波变换及滚球算法在单分子荧光能量共振转移图像分析中的应用

单分子荧光共振能量转移技术是通过检测单个分子内的荧光供体及受体间荧光能量转移的效率来研究分子构象的变化．要得到这些生物大分子的信息就需要对大量的单分子信号进行统计分析,人工分析这些信息,既费时费力又不具备客观性和可重复性,因此本文将小波变换及滚球算法应用到单分子荧光能量共振转移图像中对单分子信号进行统计分析．在保证准确检测到单分子信号的前提下,文章对滚球算法和小波变换算法处理图像后的线性进行了分析,结果表明,滚球算法和小波变换算法不但能够很好地去除单分子FRET图像的背景噪声,同时还能很好地保持单分子荧光信号的线性．最后本文还利用滚球算法处理单分子FRET图像及统计15 bp DNA的FRET效率的直方图,通过计算得到了15 bp DNA的FRET效率值．     

Hippocampal memory consolidation during sleep: a comparison of mammals and birds

The transition from wakefulness to sleep is marked by pronounced changes in brain activity. The brain rhythms that characterize the two main types of mammalian sleep, slow‐wave sleep (SWS) and rapid eye movement (REM) sleep, are thought to be involved in the functions of sleep. In particular, recent theories suggest that the synchronous slow‐oscillation of neocortical neuronal membrane potentials, the defining feature of SWS, is involved in processing information acquired during wakefulness. According to the Standard Model of memory consolidation, during wakefulness the hippocampus receives input from neocortical regions involved in the initial encoding of an experience and binds this information into a coherent memory trace that is then transferred to the neocortex during SWS where it is stored and integrated within preexisting memory traces. Evidence suggests that this process selectively involves direct connections from the hippocampus to the prefrontal cortex (PFC), a multimodal, high‐order association region implicated in coordinating the storage and recall of remote memories in the neocortex. The slow‐oscillation is thought to orchestrate the transfer of information from the hippocampus by temporally coupling hippocampal sharp‐wave/ripples (SWRs) and thalamocortical spindles. SWRs are synchronous bursts of hippocampal activity, during which waking neuronal firing patterns are reactivated in the hippocampus and neocortex in a coordinated manner. Thalamocortical spindles are brief 7–14 Hz oscillations that may facilitate the encoding of information reactivated during SWRs. By temporally coupling the readout of information from the hippocampus with conditions conducive to encoding in the neocortex, the slow‐oscillation is thought to mediate the transfer of information from the hippocampus to the neocortex. Although several lines of evidence are consistent with this function for mammalian SWS, it is unclear whether SWS serves a similar function in birds, the only taxonomic group other than mammals to exhibit SWS and REM sleep. Based on our review of research on avian sleep, neuroanatomy, and memory, although involved in some forms of memory consolidation, avian sleep does not appear to be involved in transferring hippocampal memories to other brain regions. Despite exhibiting the slow‐oscillation, SWRs and spindles have not been found in birds. Moreover, although birds independently evolved a brain region—the caudolateral nidopallium (NCL)—involved in performing high‐order cognitive functions similar to those performed by the PFC, direct connections between the NCL and hippocampus have not been found in birds, and evidence for the transfer of information from the hippocampus to the NCL or other extra‐hippocampal regions is lacking. Although based on the absence of evidence for various traits, collectively, these findings suggest that unlike mammalian SWS, avian SWS may not be involved in transferring memories from the hippocampus. Furthermore, it suggests that the slow‐oscillation, the defining feature of mammalian and avian SWS, may serve a more general function independent of that related to coordinating the transfer of information from the hippocampus to the PFC in mammals. Given that SWS is homeostatically regulated (a process intimately related to the slow‐oscillation) in mammals and birds, functional hypotheses linked to this process may apply to both taxonomic groups.     

A Comparison of ECG Signal Pre-processing Using FrFT,FrWT and IPCA for Improved Analysis

ObjectiveElectrocardiogram (ECG) is a diagnostic tool for recording electrical activities of the human heart non-invasively. It is detected by electrodes placed on the surface of the skin in a conductive medium. In medical applications, ECG is used by cardiologists to observe heart anomalies (cardiovascular diseases) such as abnormal heart rhythms, heart attacks, effects of drug dosage on subject's heart and knowledge of previous heart attacks. Recorded ECG signal is generally corrupted by various types of noise/distortion such as cardiac (isoelectric interval, prolonged depolarization and atrial flutter) or extra cardiac (respiration, changes in electrode position, muscle contraction and power line noise). These factors hide the useful information and alter the signal characteristic due to low Signal-to-Noise Ratio (SNR). In such situations, any failure to judge the ECG signal correctly may result in a delay in the treatment and harm a subject (patient) health. Therefore, appropriate pre-processing technique is necessary to improve SNR to facilitate better treatment to the subject. Effects of different pre-processing techniques on ECG signal analysis (based on R-peaks detection) are compared using various Figures of Merit (FoM) such as sensitivity (Se), accuracy (Acc) and detection error rate (DER) along with SNR.MethodsIn this research article, a new fractional wavelet transform (FrWT) has been proposed as a pre-processing technique in order to overcome the disadvantages of other existing commonly used techniques viz. wavelet transform (WT) and the fractional Fourier transform (FrFT). The proposed FrWT technique possesses the properties of multiresolution analysis and represents signal in the fractional domain which consists of representation in terms of rotation of signals in the time–frequency plane. In the literature, ECG signal analysis has been improvised using statistical pre-processing techniques such as principal component analysis (PCA), and independent component analysis (ICA). However, both PCA and ICA are prone to suffer from slight alterations in either signal or noise, unless the basis functions are prepared with a worldwide set of ECG. Independent Principal Component Analysis (IPCA) has been used to overcome this shortcoming of PCA and ICA. Therefore, in this paper three techniques viz. FrFT, FrWT and IPCA are selected for comparison in pre-processing of ECG signals.ResultsThe selected methods have been evaluated on the basis of SNR, Se, Acc and DER of the detected ECG beats. FrWT yields the best results among all the methods considered in this paper; 34.37dB output SNR, 99.98% Se, 99.96% Acc, and 0.036% DER. These results indicate the quality of biology-related information retained from the pre-processed ECG signals for identifying different heart abnormalities.ConclusionCorrect analysis of the acquired ECG signal is the main challenge for cardiologist due to involvement of various types of noises (high and low frequency). Twenty two real time ECG records have been evaluated based on various FoM such as SNR, Se, Acc and DER for the proposed FrWT and existing FrFT and IPCA preprocessing techniques. Acquired real-time ECG database in normal and disease situations is used for the purpose. The values of FoMs indicate high SNR and better detection of R-peaks in a ECG signal which is important for the diagnosis of cardiovascular disease. The proposed FrWT outperforms all other techniques and holds both analytical attributes of the actual ECG signal and alterations in the amplitudes of various ECG waveforms adequately. It also provides signal portrayals in the time-fractional-frequency plane with low computational complexity enabling their use practically for versatile applications.