Automated diagnostic systems with diverse and composite features for Doppler ultrasound signals

In this paper, we present the automated diagnostic systems for Doppler ultrasound signals classification with diverse and composite features and determine their accuracies. We compared the classification accuracies of six different classifiers, namely multilayer perceptron neural network (MLP), combined neural network (CNN), mixture of experts (ME), modified mixture of experts (MME), probabilistic neural network (PNN), and support vector machine (SVM), which were trained on diverse or composite features. The present study was conducted with the purpose of answering the question of whether the automated diagnostic systems improve the capability of classification of ophthalmic arterial (OA) and internal carotid arterial (ICA) Doppler signals. Our research demonstrated that the SVM trained on composite feature and the MME trained on diverse features achieved accuracy rates which were higher than that of the other automated diagnostic systems.     

Massively parallel classification of single-trial EEG signals using a min-max modular neural network

This paper presents a method for classifying single-trial electroencephalogram (EEG) signals using min-max modular neural networks implemented in a massively parallel way. The method has three main steps. First, a large-scale, complex EEG classification problem is simply divided into a reasonable number of two-class subproblems, as small as needed. Second, the two-class subproblems are simply learned by individual smaller network modules in parallel. Finally, all the individual trained network modules are integrated into a hierarchical, parallel, and modular classifier according to two module combination laws. To demonstrate the effectiveness of the method, we perform simulations on fifteen different four-class EEG classification tasks, each of which consists of 1491 training and 636 test data. These EEG classification tasks were created using a set of non-averaged, single-trial hippocampal EEG signals recorded from rats; the features of the EEG signals are extracted using wavelet transform techniques. The experimental results indicate that the proposed method has several attractive features. 1) The method is appreciably faster than the existing approach that is based on conventional multilayer perceptrons. 2) Complete learning of complex EEG classification problems can be easily realized, and better generalization performance can be achieved. 3) The method scales up to large-scale, complex EEG classification problems.     

Multivariate autoregressive models for classification ofspontaneous electroencephalographic signals during mental tasks

This article explores the use of scalar and multivariate autoregressive (AR) models to extract features from the human electroencephalogram (EEG) with which mental tasks can be discriminated. This is part of a larger project to investigate the feasibility of using EEG to allow paralyzed persons to control a device such as a wheelchair. EEG signals from four subjects were recorded while they performed two mental tasks. Quarter-second windows of six-channel EEG were transformed into four different representations: scalar AR model coefficients, multivariate AR coefficients, eigenvalues of a correlation matrix, and the Karhunen-Loeve transform of the multivariate AR coefficients. Feature vectors defined by these representations were classified with a standard, feedforward neural network trained via the error backpropagation algorithm. The four representations produced similar results, with the multivariate AR coefficients performing slightly better and more consistently with an average classification accuracy of 91.4% on novel, untrained, EEG signals     

S-transform-based intelligent system for classification of power quality disturbance signals

In this paper, a new approach is presented for the detection and classification of nonstationary signals in power networks by combining the S-transform and neural networks. The S-transform provides frequency-dependent resolution that simultaneously localizes the real and imaginary spectra. The S-transform is similar to the wavelet transform but with a phase correction. This property is used to obtain useful features of the nonstationary signals that make the pattern recognition much simpler in comparison to the wavelet multiresolution analysis. Two neural network configurations are trained with features from the S-transform for recognizing the waveform class. The classification accuracy for a variety of power network disturbance signals for both types of neural networks is shown and is found to be a significant improvement over multiresolution wavelet analysis with multiple neural networks.     

基于最佳小波变换和神经网络的气体非线性荧光光谱的识别

将小波变换和神经网络相结合用于非线性荧光光谱的识别,针对非线性荧光光谱的特点,提出了选择最佳小波函数和分解层数的方法,处理后的光谱在保留光谱特征的基础上,大大压缩了数据维数;采用概率神经网络(PNN),对3种污染气体的非线性荧光光谱进行识别,获得了满意的实验结果。由于神经网络的输入是小波压缩后的数据,不仅提取了原始数据中的特征,而且数据的维数也下降7倍多,大大提高了气体识别的速度。     

Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network

There are numerous neurological disorders such as dementia, headache, traumatic brain injuries, stroke, and epilepsy. Out of these epilepsy is the most prevalent neurological disorder in the human after stroke. Electroencephalogram (EEG) contains valuable information related to different physiological state of the brain. A scheme is presented for detecting epileptic seizures from EEG data recorded from normal subjects and epileptic patients. The scheme is based on discrete wavelet transform (DWT) analysis and approximate entropy (ApEn) of EEG signals. Seizure detection is performed in two stages. In the first stage, EEG signals are decomposed by DWT to calculate approximation and detail coefficients. In the second stage, ApEn values of the approximation and detail coefficients are calculated. Significant differences have been found between the ApEn values of the epileptic and the normal EEG allowing us to detect seizures with 100 % classification accuracy using artificial neural network. The analysis results depicted that during seizure activity, EEG had lower ApEn values compared to normal EEG. This gives that epileptic EEG is more predictable or less complex than the normal EEG. In this study, feed-forward back-propagation neural network has been used for classification and training algorithm for this network that updates the weight and bias values according to Levenberg–Marquardt optimization technique.     

在线脑机接口中脑电信号的特征提取与分类方法

在脑机接口研究中,针对运动想象脑电信号的特征抽取,提出了一种基于离散小波变换和AR模型的方法.利用Daubechies类小波函数对脑电信号进行3层分解,抽取小波变换系数的统计特征;利用Burg算法提取脑电信号6阶AR模型系数.将这两类特征进行组合后使用神经网络、支持向量机、马氏距离线性判别进行分类并比较分析.采用BCI...     

小波滤波与AR模型在脑电信号处理的应用

针对脑-计算机接口技术中的脑电信号处理、事件相关同步和事件相关去同步的特点,提出了一种基于离散小波滤波和AR模型来提取脑电信号特征向量的方法。利用Daubechies类小波函数对脑电信号进行4层分解,然后使用Burg算法提取脑电信号8阶AR模型系数,最后用BP神经网络进行分类和比较。得到最优的正确率为71.64%,小波滤波的效果要优于FIR滤波器。     

Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine

The feasibility of classifying different human activities based on micro-Doppler signatures is investigated. Measured data of 12 human subjects performing seven different activities are collected using a Doppler radar. The seven activities include running, walking, walking while holding a stick, crawling, boxing while moving forward, boxing while standing in place, and sitting still. Six features are extracted from the Doppler spectrogram. A support vector machine (SVM) is then trained using the measurement features to classify the activities. A multiclass classification is implemented using a decision-tree structure. Optimal parameters for the SVM are found through a fourfold cross-validation. The resulting classification accuracy is found to be more than 90%. The potentials of classifying human activities over extended time duration, through wall, and at oblique angles with respect to the radar are also investigated and discussed.     

基于LMD和SVM算法的模拟电路故障诊断

对模拟故障电路进行特征提取与分类是模拟电路诊断的两个重要环节。现有方法多对时域响应信号进行小波变换以提取故障特征,并用神经网络或支持向量机方法实现对故障进行分类。为提高模拟电路故障诊断率,提出一种局域均值分解(LMD)与SVM相结合的新算法。该算法运用局域均值算法(LMD),将其自适应地分解为一系列单分量调幅-调频信号(PF),通过提取电路正常和故障状态的特征,运用SVM对其分类,获得诊断效率。仿真实验结果表明,该方法对模拟电路的故障诊断精度达到98%以上,适用于模拟电路的故障诊断。     

基于脑功能网络和样本熵的脑电信号特征提取

针对脑-机接口(BCI)研究中采用单一特征对运动想象脑电信号(EEG)识别率不高的问题,该文提出一种结合脑功能网络和样本熵的特征提取方法。根据事件相关同步/去同步(ERS/ERD)现象以及皮层与肢体运动想象间的对侧映射机制,选取小波包变换消噪重构后的\begin{document}$ \mu$\end{document}节律脑电信号,用左侧27个通道、右侧27个通道分别对左半球脑区和右半球脑区构建脑功能网络,计算网络的平均节点度和平均聚集系数作为运动想象的脑功能网络特征,并结合C3, C4通道节律的样本熵构筑分布性和指向性相结合的特征向量。选用支持向量机(SVM)对左右手运动想象脑电信号进行分类,结果表明基于脑功能网络和样本熵的特征提取方法能够实现更优的分类效果,分类准确率最高可达90.27%。     

联合循环平稳特征PCA与RVM的频谱感知

针对无线信道环境中低信噪比情况下主用户信号检测率较低的问题,提出了一种基于循环平稳特征主成分分析(PCA)与相关向量机(RVM)的认知网络频谱感知算法。该算法结合了主成分分析算法与相关向量机分类方法,应用于解决认知网络频谱感知问题。首先对信号循环平稳特征参数进行特征提取,通过主成分分析进行降维提取信号主成分,生成训练样本和待测样本,并完成对相关向量机的训练,再采用训练完成的相关向量机算法分别对有无主用户情况下的信号进行分类检测,最后获得主用户信号存在性的感知判断。仿真实验表明,与人工神经网络、支持向量机和最大最小特征值算法相比较,所提算法在低信噪比情况下具有较高的分类检测性能,检测率最大可提高61.6%,有效地实现了对主用户信号的感知。     

Analysis on Design of Kohonen-network System Based on Classification of Complex Signals

The key methods of detection and classification of the electroencephalogram(EEG) used in recent years are introduced . Taking EEG for example, the design plan of Kohonen neural network system based on detection and classification of complex signals is proposed, and both the network design and signal processing are analyzed, including pre-processing of signals, extraction of signal features, classification of signal and network topology, etc.     

基于PCA和PNN的模拟电路故障诊断

为了解决模拟电路故障识别困难的问题,提出一种基于主成分分析和概率神经网络的模拟电路故障诊断方法。该方法对采集到的模拟电路故障信息进行特征提取,将提取的故障特征归一化处理后输入概率神经网络,进行训练和故障模式的分类识别。实验结果表明,该方法是有效的,具有较高的故障诊断率。     

Combining wavelet analysis and Bayesian networks for the classification of auditory brainstem response.

The auditory brainstem response (ABR) has become a routine clinical tool for hearing and neurological assessment. In order to pick out the ABR from the background EEG activity that obscures it, stimulus-synchronized averaging of many repeated trials is necessary, typically requiring up to 2000 repetitions. This number of repetitions can be very difficult, time consuming and uncomfortable for some subjects. In this study, a method combining wavelet analysis and Bayesian networks is introduced to reduce the required number of repetitions, which could offer a great advantage in the clinical situation. 314 ABRs with 64 repetitions and 155 ABRs with 128 repetitions recorded from eight subjects are used here. A wavelet transform is applied to each of the ABRs, and the important features of the ABRs are extracted by thresholding and matching the wavelet coefficients. The significant wavelet coefficients that represent the extracted features of the ABRs are then used as the variables to build the Bayesian network for classification of the ABRs. In order to estimate the performance of this approach, stratified ten-fold cross-validation is used.     

Detection of landmines and underground utilities from acoustic and GPR images with a cepstral approach

This paper introduces a cepstral approach for the automatic detection of landmines and underground utilities from acoustic and ground penetrating radar (GPR) images. This approach is based on treating the problem as a pattern recognition problem. Cepstral features are extracted from a group of images, which are transformed first to 1-D signals by lexicographic ordering. Mel-frequency cepstral coefficients (MFCCs) and polynomial shape coefficients are extracted from these 1-D signals to form a database of features, which can be used to train a neural network with these features. The target detection can be performed by extracting features from any new image with the same method used in the training phase. These features are tested with the neural network to decide whether a target exists or not. The different domains are tested and compared for efficient feature extraction from the lexicographically ordered 1-D signals. Experimental results show the success of the proposed cepstral approach for landmine detection from both acoustic and GPR images at low as well as high signal to noise ratios (SNRs). Results also show that the discrete cosine transform (DCT) is the most appropriate domain for feature extraction.     

基于小波高频系数统计特征的电路故障诊断

对故障电路进行特征提取与分类是模拟电路诊断的两个重要环节。现有方法多对时域响应信号进行小波变换以提取故障特征,并用神经网络或支持向量机方法实现对故障进行分类。为提高模拟电路故障诊断率,提出一种新的特征选取方法:在模拟电路的时域响应中对其进行小波变换,并对变换得到的高频细节系数统计平均值、标准偏差、峭度、熵和偏斜度等统计特征,并建立以支持向量机为分类器的故障诊断系统。以两种常见电路为例,实验结果表明,提出方法对常见电路进行故障诊断,准确率得到提升,精度达到99%以上,优于传统单纯小波系数分析方法,适用于模拟电路的故障诊断。     

基于概率神经网络的滚动轴承故障诊断

针对滚动轴承故障征兆与故障模式映射的复杂性,以及BP网络容易陷入局部极小、收敛速度慢等缺点,提出了基于概率神经网络（PNN）的滚动轴承故障诊断方法。采用11个时域统计特征作为样本特征,利用PNN实现样本分类,并与反向传播（BP）网络进行滚动轴承故障诊断的方法进行了对比。结果表明,PNN网络可以实现滚动轴承不同类型的故障识别,其分类结果比BP网络具有更高的准确性,并在避免局部极小和节约训练时间方面有较好的实用性。     

色彩感知中的脑电信号多域特征选择算法研究

针对目前在不同色彩感知中的脑电信号识别方面的研究还不多见,本文提出采用随机森林算法对信号的时域特征和频域特征进行最优组合的方法对不同色彩感知中的脑电信号进行识别。首先采用小波变换,对脑电信号进行7层分解,提取脑电信号在delta、theta、alpha和beta节律频带上的小波能量,并结合脑电信号在时域上的统计量偏度和峰度组成特征向量。然后通过基于随机森林的特征选择算法提取最优的特征组合方案,删除冗余的特征量。使用自适应增强算法进行分类识别,识别的平均正确率可达到85.07%。该结果表明使用本文所提出的特征提取与选择方法用于不同色彩感知中的脑电信号识别上是可行的,并且能够取得较好的识别率。      

A novel method for beat-to-beat detection of ventricular late potentials

A novel method for beat-to-beat detection of ventricular late potentials (VLP) from high-resolution electrocardiograms (ECGs) is presented. ECG signals from the X lead are first filtered using a bandpass filter, and then a time-sequence adaptive filter, to improve its signal-to-noise ratio. Eight features are extracted using wavelet transform, from the VLP time-frequency distribution of the filtered ECG signals, and used as inputs of specially designed artificial neural network for VLP recognition. The artificial neural network was trained and tested using clinical data, respectively. The results show that the presented method can detect beat-to-beat-based VLP with sensitivity of 80% and specificity of 77%, and the detection accuracy is 78%.