基于双正交小波变换的宫缩信号消噪

小波分析的信号消噪方法是现代信号处理中的重要组成部分,小波基的不同选取将直接影响消噪的效果.本文在全局阈值的标准下,基于不同噪声水平,讨论了小波基的正交性和线性相位性对消噪结果的影响,提出了选取小波基的一般方法,最后利用双正交小波基在软阈值标准下实现了对宫缩信号的消噪处理,并取得了较好的效果.     

基于小波变换心电信号消噪方法研究

小波变换是近年来兴起的热门信号处理技术,是一种非常有用的信号处理工具。本文阐述了连续小波去噪和离散小波去噪的原理,分析了基于小波去噪的几种不同方法（其中包括小波分解与重构,小波变换阈值法,小波变换模极大值法,以及它与独立分量分析相结合去除噪声的方法等）。通过检测和验证,表明该方法能较好的实现心电信号的消噪,都取得了较好的效果;同时,比较了每种方法的不足和缺陷。基于小波变换心电信号消噪的研究进展较快,通过多种方法结合运用进行消噪并取得了很好的效果,展望了利用基于小波变换心电信号消噪的前景。     

基于集合经验模态分解和小波收缩算法的自适应心电信号去噪问题研究

本文将集合经验模态分解(EEMD)与小波软阈值去噪算法相结合,提出了一种新的心电图信号去噪EEMD-WS算法.算法首先对信号进行EEMD分解得到有限个固有模态函数(IMF);其次,根据实际含噪心电信号中各成分的特性,将所有IMF分为低阶含噪、中阶有用信号和高阶含基线漂移三类,对于低阶含噪IMF利用IMF能量变化分界点自适应地确定含噪IMF个数,随后对其利用小波收缩算法中的启发式软阈值选择算法进行去噪;对于高阶含基线漂移IMF根据其自身是否包含周期信息自适应地判断并去除与基线漂移关系密切的IMF.最后通过将滤除噪声的低阶IMF、中阶有用信号重构达到抑制噪声和去除基线漂移的目的.仿真信号和MIT-BIH心电数据库真实心电信号实验显示,EEMD-WS算法不仅能够克服小波去噪算法不能去除基线漂移的不足,而且能够比常用的EMD-WS算法更好地提高消噪效果,总体去噪性能优于传统算法.     

基于小波变换的心电信号去噪算法

目的:去除在心电信号采集过程中混入的肌电干扰、工频干扰、基线漂移等噪声信号,避免噪声对心电信号特征点的识别和提取造成误判和漏判。方法:首先利用coif4小波对心电信号按Mallat算法进行分解,然后采用软、硬阈值折衷与小波重构的算法进行去噪。结果:采用MIT/BIH Arrhythmia Database中的心电信号进行仿真、验证,有效去除了三种常见的噪声信号。结论:本方法实时性好,为临床分析与诊断奠定了基础。     

多通道脑电信号的盲分离

提出一种新的多通道脑电信号盲分离的方法,将小波变换和独立分量分析(independent component analysis,ICA)相结合,利用小波变换的滤噪作用,将混合在原始脑电的部分高频噪声滤除后,再重构原始脑电作为ICA的输入信号,有效地克服了现有ICA算法不能区分噪声的缺陷。实验结果表明,该方法对多通道脑电的盲分离是很有效的。     

近距离交流信号对雌性凹耳蛙 抱对成功率的影响

繁殖期雌性凹耳蛙（Odorrana tormota）的声信号已有过深入的研究,但目前国内对其交配行为研究较少,近距离时,雌性凹耳蛙如何与雄蛙交流并完成抱对尚不清楚。为探究繁殖期雌性凹耳蛙与雄蛙近距离交流、交配过程,采用焦点动物取样法和全事件取样法对雌性凹耳蛙交配前行为进行记录。2013至2016年及2018年记录并分析了49组雌雄蛙抱对过程和42组未抱对个体的视频数据。结果表明,凹耳蛙雌蛙与雄蛙近距离交流过程涉及多种信号,包括视觉信号（眨眼、低头、腹部膨胀、脚趾震动、背转向雄蛙）与声信号两类;在每组雌蛙发出信号且抱对成功的实验中,各视觉信号出现1或2次较多,声信号出现1至3次较多,眨眼、鸣声、腹部膨胀三种信号的总次数较多;5个繁殖期所记录的雌蛙交流信号中视觉信号所占的比例均高于声信号。统计分析结果显示,同一只雌蛙在抱对成功与失败时所发出的眨眼、低头和腹部膨胀三种视觉信号的次数存在显著性差异（P < 0.05）,声信号、腹部膨胀、脚趾震动和背转向雄蛙这四种信号仅在抱对成功时出现。因此,推测这些信号在抱对前出现时,有助于提高雌雄凹耳蛙抱对成功率。     

运动观察与运动想象的皮层节律活动与神经生理机制

研究证实,运动观察与运动想象对大脑的激活有利于中风后的运动功能再学习,可用于探索人类行为过程中大脑的神经机制.为对比分析运动观察和运动想象时皮层神经元的活动特征,选取10名健康被试,采集每名被试在运动观察和运动想象时特定手部抓握动作模式下的脑电信号(EEG);引入Gabor滤波器对感觉运动区和视觉区的EEG进行时频能量谱估计,并在此基础上对EEG进行事件相关去同步/同步化(ERD/ERS)分析;最后建立ERDI(ERD index)指标对左手和右手进行模式分类并量化比较运动观察与运动想象.研究结果表明,运动观察与运动想象类似,均激活大脑感觉运动皮层,并且运动想象产生对侧主导的α和βERD;基于ERDI指标的运动想象左右手识别正确率高于运动观察分类正确率;此外,运动观察过程还同时伴随视觉皮层活动,使β节律能量产生显著衰减.本研究为运动观察和运动想象在临床康复训练以及脑机接口领域的应用提供了神经生理基础和实现途径.     

几种生物电信号的计算机获取与处理方法

生物电信号通常指脑电(EEG)、心电(ECG)、肌电(EMG)、视网膜电流图(ERG),以及各种自发和诱发神经电位等,种类繁多,波形与频率也各异。在不同的研究领域中,对这些信号的获取方式与处理手段也不相同。因此,很难用一种通用的仪器和方法分析和处理。目前国内多将电生理仪器取得的信号,收集放大后,用示波或描记方法进行分析处理。本文介绍几种生物电信号的计算机获取和处理方法与应用     

基于EMD的左右手运动想象脑电信号分析研究

基于经验模态分解（EMD）理论,提出一种左右手运动想象脑电信号分析方法。首先利用时间窗对脑电信号数据进行划分,对每段数据通过经验模态分解法将其分解为一组固有模态函数IMF,提取主要信号所在的IMF层去除信号中的噪声。对含有主要信号的几层IMF进行Hilbert变换,得到瞬时频率与对应的瞬时幅值。再提取左右手想象的特定频段mu节律和beta节律的能量信号作为特征,分别利用支持向量机（SVM）和Fisher进行了分类比较。对EMD和小波包在去噪和特征提取进行了比较。结果表明,EMD是一种很有效的去噪方法,经过EMD分解后提取的能量信号在区分左右手想象上更具有优势,识别率高。     

一种改进的小波阈值法去除耳声发射中的伪迹

本文探讨了耳声发射及其分类、特性、目前存在的问题和相关研究方法,并提出一种改进的小波阈值处理方法,它是基于一种多分辨分析小波阈值去噪,在传统的软、硬阈值去噪基础上设立两个变量,通过调节这两个变量有效地达到去除噪声的效果。     

Optimized Tunable Q Wavelet Transform Based Drowsiness Detection from Electroencephalogram Signals

Early discernment of drivers drowsy state may prevent numerous worldwide road accidents. Electroencephalogram (EEG) signals provide valuable information about the neurological changes for discrimination of alert and drowsy state. A signal is decomposed into multi-components for the analysis of the physiological state. Tunable Q wavelet transform (TQWT) decomposes the signal into low-pass and high-pass sub-bands without a choice of wavelet. The information content captured by these sub-bands depends on the choice of decomposition parameters. Due to the non-stationary nature of EEG signals, the predefined decomposition parameters of TQWT lead to information loss and degrade system performance. Hence it is required to automate the decomposition parameters in accordance with the nature of signals. In this paper, an optimized tunable Q wavelet transform (O-TQWT) is proposed for the adaptive selection of decomposition parameters by using different optimization algorithms. Objective function as a mean square error (MSE) of decomposition is minimized by optimization algorithms. Optimum decomposition parameters are used to decompose the signals into sub-bands. Time-domain based features are excerpted from the sub-bands of O-TQWT. Highly discriminant features selected by using Kruskal Wallis test are used as an input to different classification techniques. Classification accuracy of 96.14% is achieved by least square support vector machine with radial basis function kernel which is better than the other existing methodologies using the same database.     

A new algorithm for wavelet-based heart rate variability analysis

One of the most promising non-invasive markers of the activity of the autonomic nervous system is heart rate variability (HRV). HRV analysis toolkits often provide spectral analysis techniques using the Fourier transform, which assumes that the heart rate series is stationary. To overcome this issue, the Short Time Fourier Transform (STFT) is often used. However, the wavelet transform is thought to be a more suitable tool for analyzing non-stationary signals than the STFT. Given the lack of support for wavelet-based analysis in HRV toolkits, such analysis must be implemented by the researcher. This has made this technique underutilized.This paper presents a new algorithm to perform HRV power spectrum analysis based on the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT). The algorithm calculates the power in any spectral band with a given tolerance for the band's boundaries. The MODWPT decomposition tree is pruned to avoid calculating unnecessary wavelet coefficients, thereby optimizing execution time. The center of energy shift correction is applied to achieve optimum alignment of the wavelet coefficients. This algorithm has been implemented in RHRV, an open-source package for HRV analysis. To the best of our knowledge, RHRV is the first HRV toolkit with support for wavelet-based spectral analysis.     

Feature extraction and recognition of epileptiform activity in EEG by combining PCA with ApEn

This paper proposes a new method for feature extraction and recognition of epileptiform activity in EEG signals. The method improves feature extraction speed of epileptiform activity without reducing recognition rate. Firstly, Principal component analysis (PCA) is applied to the original EEG for dimension reduction and to the decorrelation of epileptic EEG and normal EEG. Then discrete wavelet transform (DWT) combined with approximate entropy (ApEn) is performed on epileptic EEG and normal EEG, respectively. At last, Neyman–Pearson criteria are applied to classify epileptic EEG and normal ones. The main procedure is that the principle component of EEG after PCA is decomposed into several sub-band signals using DWT, and ApEn algorithm is applied to the sub-band signals at different wavelet scales. Distinct difference is found between the ApEn values of epileptic and normal EEG. The method allows recognition of epileptiform activities and discriminates them from the normal EEG. The algorithm performs well at epileptiform activity recognition in the clinic EEG data and offers a flexible tool that is intended to be generalized to the simultaneous recognition of many waveforms in EEG.     

基于小波变换的QRS综合波的检测

本文描述了基于二进制小波变换（DyWT),ECG信号中QRS综合波的检测。设计－小波它适合于QRS检测,将基于心电信号的特殊的特征的特征为小波的尺度。DyWT较之其它方法最基本的优点为强有力的抑制噪声检测以及在分析随时间变化ECG波形时的灵活性。     

Detection of Seizure Event and Its Onset/Offset Using Orthonormal Triadic Wavelet Based Features

Background

Epileptic seizures are unpredictable in nature and its quick detection is important for immediate treatment of patients. In last few decades researchers have proposed different algorithms for onset and offset detection of seizure using Electroencephalogram (EEG) signals.

sEMG wavelet-based indices predicts muscle power loss during dynamic contractions

The purpose of this study was to investigate the sensitivity of new surface electromyography (sEMG) indices based on the discrete wavelet transform to estimate acute exercise-induced changes on muscle power output during a dynamic fatiguing protocol. Fifteen trained subjects performed five sets consisting of 10 leg press, with 2 min rest between sets. sEMG was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were computed. These were: mean rectified voltage (MRV), median spectral frequency (F_med), Dimitrov spectral index of muscle fatigue (FI_nsm5), as well as five other parameters obtained from the stationary wavelet transform (SWT) as ratios between different scales. The new wavelet indices showed better accuracy to map changes in muscle power output during the fatiguing protocol. Moreover, the new wavelet indices as a single parameter predictor accounted for 46.6% of the performance variance of changes in muscle power and the log-FI_nsm5 and MRV as a two-factor combination predictor accounted for 49.8%. On the other hand, the new wavelet indices proposed, showed the highest robustness in presence of additive white Gaussian noise for different signal to noise ratios (SNRs). The sEMG wavelet indices proposed may be a useful tool to map changes in muscle power output during dynamic high-loading fatiguing task.     

Analysis of EEG signals during epileptic and alcoholic states using AR modeling techniques

Electroencephalogram (EEG) analysis remains problematic due to limited understanding of the signal origin, which leads to the difficulty of designing evaluation methods. In spite of these shortcomings, the EEG is a valuable tool in the evaluation of some neurological disorders as well as in the evaluation of overall cerebral activity. In most studies, which use quantitative EEG analysis, the properties of measured EEG are computed by applying power spectral density (PSD) estimation for selected representative EEG samples. The sample for which the PSD is calculated is assumed to be stationary. This work deals with a comparative study of the PSD obtained from normal, epileptic and alcoholic EEG signals. The power density spectra were calculated using fast Fourier transform (FFT) by Welch's method, auto regressive (AR) method by Yule–Walker and Burg's method. The results are tabulated for these different classes of EEG signals.     

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。