癫痫脑电棘波的小波变换模极大值对检测方法

本文首次将对小波变换模极大值对检测信号奇异点的理论应用于癫痫脑电信号,对棘波进行检测。采用二进样条小波脑电信号按Ｍａｌｌａｔ算法进行变换,分析含有奇异点的信号,即棘波,与其小波变换模数大值对的关系,对棘波进行识别。     

一种基于EEG特征提取的癫痫棘波综合检测判决方法

癫痫特征的自动检测在临床应用上具有重要的意义。本研究综合小波变换、非线性能量算子、特征提取和神经网络等技术，提出了一种癫痫棘波检测系统，充分发挥各技术的优点，在对真实脑电数据的处理中，表现出良好的性能。     

基于脑电棘波频次的癫痫发作预测算法

癫痫发作预测是近年来在神经科学领域中备受关注的课题.预测癫痫发作可以使医护人员或患者提前采取有效措施来预防和控制癫痫发作,在临床上具有重要意义.棘波是最基本的阵发性异常脑电活动,在分析和统计癫痫发作前期和发作期棘波频次不同表现的基础上,首次提出一种基于脑电棘波频次的癫痫预测算法.对脑电进行滤波以去掉高频干扰后,采用形态学滤波器检测脑电棘波数目,并计算各段脑电中棘波出现的频次,最后根据棘波频次的变化预测癫痫的发作.采用本算法对21例癫痫患者长程颅内脑电进行癫痫预测,准确率达到74.7％,每小时错误预测次数仅为0.111次.结果表明,所提出算法能够有效地预测癫痫发作.     

B样条小波在提取脑电癫痫棘波中的应用

脑电癫痫特征波自动提取对于患者的诊断，以及减轻医生的繁重劳动力都具有重要意义。采用B样条小波对信号突变点灵敏检测的优良特性来进行癫痫棘波提取，取得了比较好的效果。     

脑电棘波识别和噪声消除的小波变换方法

研究了利用二进小波变的的模极大值识别脑电信号奇异点如棘波和消除噪声的方法,该方法在较好保留原脑电信号奇异信息的同时能有效地消除噪声,进一步讨论了信号与白噪声的奇异性指数的区别,以及小波变换模极大值沿各变换尺度传递的不同特性,并利用该特性区分信号中的奇异点和噪声,能准确识别奇异点的位置,这种奇异性识别技术在信号的特征提取和消除噪声方面有广阔的应用前景。     

检测脑电癫痫波的小波分析方法

小波分析是一种时／频域的分析方法,它具有多分辨率,相对带宽恒定,在时、频两域都具有表征信号局部特征能力的特点,被誉为“数字显微镜”本文基于二进小波变换,对嫌疑癫痫波进行多尺度的分析,根据脑电嫌疑波的三种基本成分（棘波、尖波和慢波）在不同尺度上的特性检测出它们。文中最后给出了小波分析检测脑电癫痫波的实例。理论和实践表明,小波分析在脑电癫痫波的检测中有广阔的应用前景。     

脑电信号数据压缩及棘波识别的小波神经网络方法

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。     

基于小波变换的脑电图癫痫波形检测

脑电图中癫痫波形的自动检测与分类是临床上很有意义的工作。我们根据脑电图中的癫痫特征波形，利用小波变换的时频局部化特性，给出了一种高效的癫痫波表的自动检测方法，构造了一个连续的癫痫波检测系统。通过检测不同尺度上的局部极大值，确定出对应的脑电图中的锐变点位置，并由此检测出脑电图中的癫痫波，从初步临床试验的结果来看，系统具有检测精度高，可连续作业等优点，获得了较好的效果。     

基于小波变换的QRS波群实时检测算法

本文研究了基于小波变换方法的心电信号QRS波群检测算法,通过对心电信号进行低通滤波、小波变换、差分平滑、阈值检测和修正策略等技术,提高了QRS波群的检测率.经MIT-BIH心律失常心电数据库全部48例数据的检验,QRS波检测灵敏度达99.82%,真阳性率达99.52%.在Windows环境下可实时实现.     

基于经验模式分解的脑电棘波检测方法

脑电癫痫特征波自动提取对于患者的诊断以及减轻医生的繁重劳动都具有重要意义。本研究结合经验模式分解(EMD)技术提出了一种基于经验模式分解的脑电棘波检测新方法。这种方法提取出EEG信号中与棘波信号相关的高频成分,计算其Hilbert变换后的瞬时幅值,进而检测出棘波信号。对临床EEG数据检测的结果表明,这种方法能有效地从复杂的背景EEG信号中检出棘波,具有良好的应用前景。     

EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms

The visual analysis of common neurological disorders such as epileptic seizures in electroencephalography (EEG) is an oversensitive operation and prone to errors, which has motivated the researchers to develop effective automated seizure detection methods. This paper proposes a robust automatic seizure detection method that can establish a veritable diagnosis of these diseases. The proposed method consists of three steps: (i) remove artifact from EEG data using Savitzky-Golay filter and multi-scale principal component analysis (MSPCA), (ii) extract features from EEG signals using signal decomposition representations based on empirical mode decomposition (EMD), discrete wavelet transform (DWT), and dual-tree complex wavelet transform (DTCWT) allowing to overcome the non-linearity and non-stationary of EEG signals, and (iii) allocate the feature vector to the relevant class (i.e., seizure class "ictal" or free seizure class "interictal") using machine learning techniques such as support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA). The experimental results were based on two EEG datasets generated from the CHB-MIT database with and without overlapping process. The results obtained have shown the effectiveness of the proposed method that allows achieving a higher classification accuracy rate up to 100% and also outperforms similar state-of-the-art methods.     

基于经验模态分解-小波包变换的表面肌电信号手势识别

为了提高表面肌电信号（sEMG）手部运动识别的正确率,比较常规的sEMG预处理和特征提取方法,提出一种基于经验模态分解（EMD）和小波包变换（WPT）的sEMG手势识别模型。首先,使用EMD方法将sEMG进行平稳化,得到一系列的固有模态函数。其次,求取各个固有模态函数与原始信号的相关性,选取相关性较高的前4个分量作为有效分量。然后,采用Db3小波函数进行WPT,提取小波包系数中的平均能量、平均绝对值、最大值、均方根和方差等特征。分别采用线性判别分析和支持向量机对12种手部运动进行模式识别。结果表明基于EMD和WPT的sEMG手势识别正确率比直接提取小波包系数中的特征识别正确率高。     

利用ECG信号检测睡眠呼吸暂停的小波包分析方法

文章根据睡眠呼吸暂停与心率变化的关系。阐述了从心电图中检测睡眠呼吸暂停的方法。该方法首先通过对心率信号进行小波包变换，提取特征向量，然后根据特征向量的变化检测出睡眠呼吸暂停的位置和分布。结果表明，该方法物理意义明确，诊断结果精度高，为睡眠呼吸暂停综合症的早期诊断、监护及预后评估提供了新的分析工具。     

基于小波变换的小鼠QRS复合波检测

采用二进小波变换与斜率和幅度相结合的方法，对小鼠QRS复合波进行检测。根据小鼠QRS复合波的特点，采用Daubechics小波为母函烽，按照ECG的频谱特点选用尺度因子，对有噪声污染和形态变异的QRS复合波进行了检测。结果表明：小波变换对小鼠QRS复合波的检测是一种有效的方法。     

EEG signals classification of epileptic patients via feature selection and voting criteria in intelligent method

Epileptic disease can be diagnosed by using intelligent methods on the Electroencephalograph (EEG) signals. In this paper, wavelet packet transform (WPT) was used in each of the frequency bands and wavelet coefficients were obtained, then the energy and entropy function was done on the wavelet coefficients and used as initial feature vectors. In the next step, eight and 15 features from 30 initial energy and entropy features were selected as the final features because their receiver operating characteristic (ROC) curve areas were higher than others. There were seven classifier inputs. These seven classifiers consisted of four artificial neural networks (ANN) with different structures, support vector machines (SVM), K-nearest neighbours (KNN) and a hybrid network. Each classifier was trained by 0.5, 0.8 and 0.9 EEG signals. After the training process, a fusion network based on a voting criteria was used to make the algorithm robust against the possible changes in each classifier and increase the classification accuracy. Finally, the algorithm was tested by other EEG signals. As a result, normal and epileptic classes were detected with total classification accuracy of 99–100%.     

A multi-level wavelet approach for automatic detection of epileptic spikes in the electroencephalogram

We describe a strategy to automatically identify epileptiform activity in 18-channel human electroencephalogram (EEG) based on a multi-resolution, multi-level analysis. The signal on each channel is decomposed into six sub-bands using discrete wavelet transform. Adaptive threshold is applied on sub-bands 4 and 5. The spike portion of EEG signal is then extracted from the raw data and energy of the signal for locating the exact location of epileptic foci is determined. The key points of this process are identification of a suitable wavelet for decomposition of EEG signals, recognition of a proper resolution level, and computation of an appropriate dynamic threshold.     

基于小波包-交叉频率相干性的肌间耦合特性

人体运动控制系统具有高度的非线性特性,通过量化评价表面肌电(sEMG)信号间的非线性耦合强度,可以得到运动相关肌肉的功能状态,进而探究人体运动控制的机制。本文将小波包分解和n∶m相干性分析相结合,构建基于小波包-n∶m相干性的肌间交叉频率耦合分析模型,探究肌电信号间的非线性耦合关系。在维持30%最大自主收缩力(MVC)的肘部屈伸状态下,采集20名健康成年人的sEMG信号,首先基于小波包分解获取子带分量,然后将子带信号进行n∶m相干性计算,分析肌间耦合特征。结果表明:30%MVC的肘部屈曲运动下,协同肌对和拮抗肌对的线性耦合(频率比为1∶1时)强度高于非线性耦合(频率比为1∶2、2∶1和1∶3、3∶1时);对于肌间非线性耦合,随着频率比的增大,耦合强度随之降低,且频率比为n∶m和m∶n之间没有明显的耦合强度差异;beta和gamma频段内的肌间耦合主要体现在协同肌对之间的线性耦合(1∶1)和低频率比的非线性耦合(1∶2、2∶1)以及拮抗肌对之间的线性耦合上。以上说明:小波包-n∶m相干性方法可以定性、定量地描述肌间非线性耦合强度,为深入揭示人体运动控制机制和运动功能障碍患者的康复评价提供理论参考。     

A novel method for analysis of single ion channel signal based on wavelet transform

A single ion channel signal was analysed by the power distribution fraction constructed by a discrete wavelet transform. Average opening time and energy distribution of the signal can be obtained directly by this method. The method can also be used when the signal is corrupted by noise. By contrast, the conventional frequency domain analysis method--power spectral density--is less effective. Power distribution fraction will therefore give more useful information in analysis of experimental ion channel signals, principally by giving values of the mean channel opening time. The method may be applied to distinguish different ion channels more efficiently and to find their reactions to drugs.