Intelligent hybrid approaches for human ECG signals identification

This paper presents hybrid approaches for human identification based on electrocardiogram (ECG). The proposed approaches consist of four phases, namely data acquisition, preprocessing, feature extraction and classification. In the first phase, data acquisition phase, data sets are collected from two different databases, ECG-ID and MIT-BIH Arrhythmia database. In the second phase, noise reduction of ECG signals is performed by using wavelet transform and a series of filters used for de-noising. In the third phase, features are obtained by using three different intelligent approaches: a non-fiducial, fiducial and a fusion approach between them. In the last phase, the classification approach, three classifiers are developed to classify subjects. The first classifier is based on artificial neural network (ANN). The second classifier is based on K-nearest neighbor (KNN), relying on Euclidean distance. The last classifier is support vector machine (SVM) classification accuracy of 95% is obtained for ANN, 98 % for KNN and 99% for SVM on the ECG-ID database, while 100% is obtained for ANN, KNN, and SVM on MIT-BIH Arrhythmia database. The results show that the proposed approaches are robust and effective compared with other recent works.     

Recognition of the Communication Signals Using Particle Swarm Optimization and Support Vector Machine Based on the Multi-Resolution Wavelet Analysis

Automatic recognition of the communication signals plays an important role for various applications. This paper presents a novel intelligent system for recognition of digital communication signals. This system includes three main modules: feature extraction module, classifier module and optimization module. In the feature extraction module, multi-resolution wavelet analysis is proposed for extraction the suitable features. In the classifier module, a multi-class support vector machine (SVM) based classifier is proposed as the multi-class classifier. For optimization module, a particle swarm optimization algorithm is proposed to improve the generalization performance of the recognizer. In this module, it is optimized the SVM classifier design by searching for the best value of the parameters that tune its discriminant function, and upstream by looking for the best subset of features that feed the classifier. Simulation results show that the proposed hybrid intelligent system has high performance even at very low signal to noise ratios (SNRs).     

一种低信噪比下雷达辐射源识别方法

针对低信噪比下雷达辐射源信号分类,首先提出了基于高阶累积量和小波包变换相结合的特征提取方法,然后设计支持向量机分类器,并运用粒子群优化算法对分类器的参数进行寻优,最终实现对雷达辐射源信号的自动分类。仿真实验结果表明,在信噪比为-4dB时,6种雷达辐射源信号的平均识别率仍能达到93.83%,在低信噪比环境下取得了较为理想的分类效果。     

基于滤波重构和卷积神经网络的心电信号分类

计算机自动分类心电信号能够减轻医生工作压力并大幅提高诊断速度和准确率。文中针对传统算法中特征提取过程复杂及抗干扰能力弱的问题,提出了一种结合滤波重构和卷积神经网络的心电信号分类算法。该算法首先通过传统信号滤波和心拍序列重构去除原始心电信号中的噪声干扰,然后构建卷积神经网络来自动学习心电信号特征并完成分类。在PhysioNet/CinC Challenge 2017数据集上的分类实验结果表明,该方法的平均F₁(查准率、召回率的调和平均)达到了0.8471,优于人工特征提取和常规卷积网络方法,且具有很强的抗干扰能力。     

基于小波分解和1D-GoogLeNet的心律失常检测

心电图(ECG)信号的准确分类对于心脏病的自动诊断非常重要。为了实现对心律失常的智能分类,该文提出一种基于小波分解和1D-GoogLeNet的精确分类方法。在该方法中,利用Db6小波对ECG信号进行8级分解,得到既含时域信息又有频域信息的多维数据。随后,分解的样本用作1D-GoogLeNet的输入训练该模型。在提出的1D-GoogLeNet模型中,借鉴Inception在图像特征提取中的优异性能,将2维卷积变换为1维卷积学习ECG的特征,并且简化各个Inception的结构,降低模型参数。该文提出的神经网络分类器能够有效缓解计算效率低、收敛困难和模型退化的问题。在实验中,选用MIT-BIH心律失常数据集测试所提模型的性能,对比了信号的不同分解分量组合作为输入的检测结果,当输入数据由{d2-d7}组合时,所提1D-GoogLeNet模型可以达到96.58%的平均准确率。此外,还对比了该模型与未经结构优化的简单1维GoogLeNet在数据集上的表现,前者在准确率上比后者提高了4.7%,训练效率提高了118%。     

基于LSTM网络和特征融合的通信干扰识别

针对现有通信干扰信号识别方法识别效果不佳的问题,提出了一种基于长短时记忆网络(Long Short-Term Memory,LSTM)和特征融合的通信干扰识别方法.该方法利用LSTM网络提取干扰信号的特征,通过LSTM强大的序列特征提取能力提升干扰信号特征提取的性能;通过提取信号的时域和频域特征后进行特征融合,使用全连...     

Novel ECG Signal Classification Based on KICA Nonlinear Feature Extraction

Electrocardiogram (ECG) signal feature extraction is important in diagnosing cardiovascular diseases. This paper presents a new method for nonlinear feature extraction of ECG signals by combining principal component analysis (PCA) and kernel independent component analysis (KICA). The proposed method first uses PCA to decrease the dimensions of the ECG signal training set and then employs KICA to calculate the feature space for extracting the nonlinear features. Support vector machine (SVM) is utilized to determine the nonlinear features of the ECG signal classification. Genetic algorithm is also used to optimize the SVM parameters. The proposed method is advantageous because it does not require a huge amount of sampling data, and this technique is better than traditional strategies to select optimal features in the multi-domain feature space. Computer simulations reveal that the proposed method yields more satisfactory classification results on the MIT–BIH arrhythmia database, reaching an overall accuracy of 97.78 %.     

Music Genre Classification Using Spectral Analysis and Sparse Representation of the Signals

In this paper, we proposed a robust music genre classification method based on a sparse FFT based feature extraction method which extracted with discriminating power of spectral analysis of non-stationary audio signals, and the capability of sparse representation based classifiers. Feature extraction method combines two sets of features namely short-term features (extracted from windowed signals) and long-term features (extracted from combination of extracted short-time features). Experimental results demonstrate that the proposed feature extraction method leads to a sparse representation of audio signals. As a result, a significant reduction in the dimensionality of the signals is achieved. The extracted features are then fed into a sparse representation based classifier (SRC). Our experimental results on the GTZAN database demonstrate that the proposed method outperforms the other state of the art SRC approaches. Moreover, the computational efficiency of the proposed method is better than that of the other Compressive Sampling (CS)-based classifiers.     

Weighted conditional random fields for supervised interpatient heartbeat classification

This paper proposes a method for the automatic classification of heartbeats in an ECG signal. Since this task has specific characteristics such as time dependences between observations and a strong class unbalance, a specific classifier is proposed and evaluated on real ECG signals from the MIT arrhythmia database. This classifier is a weighted variant of the conditional random fields classifier. Experiments show that the proposed method outperforms previously reported heartbeat classification methods, especially for the pathological heartbeats.     

基于1（1/2）维谱与K-L变换的被动声纳目标识别

采用基于1（1/2）维谱分析与K-L变换相结合的特征提取方法,获取被动声纳噪声信号的有效识别信息,对被动声纳的目标信号进行分类。首先对被动声纳噪声进行1（1/2）维谱子带能量的特征提取,然后运用K-L变换实现高维特征向量的降维,剔除冗余特征,并以BP神经网络作为分类器对三类目标进行识别与分类。计算机仿真结果表明,该方法具有较好的分类效果和稳健性。     

心音信号MFCC特征向量提取方法的优化

为了提高利用梅尔频率倒谱系数（Mel-Frequency Cepstral Coefficients, MFCC）特征向量进行心音信号分类的准确率，本文提出以一种基于独立成分分析（Independent Component Analysis, ICA）及权值优化的MFCC特征向量优化方法。首先，通过消除趋势项、降噪、提取心动周期与基础心音分割等步骤对心音信号预处理；接着，对提取的基础心音信号做Mel频谱变换及倒谱分析提取MFCC特征向量，其中用ICA替代离散余弦变换去除分量间高阶量的相关性，同时采用相关系数为权值优化整体混合矩阵；最后，采用F比衡量特征向量贡献率，并以其为权值优化各维特征向量。通过提取MFCC特征向量采用支持向量机（Support Vector Machine, SVM）的分类器识别第一心音及第二心音，并与人工标注心音状态集进行对比。实验结果表明，基于ICA及权值优化的MFCC特征向量在SVM分类器中识别率得到了有效的提升，且优化算法具备一定抗噪性能。      

雷达海上目标双通道卷积神经网络特征融合智能检测方法

受复杂海洋环境影响,基于统计理论的海面目标检测方法由于假设条件不成立,在实际应用中难以实现高性能检测,本文从特征提取分类角度,通过深度学习分类方法对目标和杂波的雷达回波信号进行二元分类,提出了一种基于双通道卷积神经网络(DCCNN)的雷达海上目标智能检测方法。首先,对实测海杂波和目标雷达信号进行预处理,得到信号的时间-多普勒谱和幅度信息;然后,构建DCCNN对预处理得到的数据进行智能特征提取,得到信号的特征向量,并对不同特征提取模型性能进行测试;最后,通过阈值可设的Softmax分类器作为检测器对特征向量进行分类,实现虚警率的控制。测试结果表明:与传统的单通道CNN以及无虚警控制Hog-SVM分类算法相比,基于二维卷积核VGG16和一维卷积核LeNet的DCCNN特征提取模型和softmax分类器可实现更高的检测性能,并可以实现虚警率控制,为复杂海杂波背景下目标智能检测提供了新的技术途径。     

Nonlinear considerations in EEG signal classification

We investigate the effect of incorporating modeling of nonlinearity on the classification of electroencephalogram (EEG) signals using an artificial neural network (ANN). It is observed that the ANN's predictive ability is improved after preprocessing EEG signals using a particular nonlinear modeling technique, viz. a bilinear model, compared with those obtained by using a particular classical linear analysis method, viz. an autoregressive (AR) model. Until recently, linear time-invariant Gaussian modeling has dominated the development of time series modeling and feature extraction. The advantage of such classical models lies in the fact that a complete signal processing theory is available. In the case of EEG signals, where the underlying theory regarding the dynamical law governing the generation of these signals (e,g., the underlying physiological factors) is not completely understood, a case can be made for using improved signal processing models that are not subject to linear constraints. Such models should recognize important features of the observed data that may not be well modeled by a linear time-invariant model. It is known that EEG signals are nonstationary, and it is possible that they may be nonlinear as well. Thus, one way of gaining further insights on the structure of EEG signals is to introduce nonlinear models and higher order spectra. This paper compares the results of classification using a linear AR model with those obtained from a bilinear model. It is shown that in certain cases, the nonlinearity of the EEG signals is an important factor that ought to be taken into consideration during preprocessing of the signals prior to the classification task     

一种适用于低信噪比的数字调制方式识别方法

针对低信噪比平坦衰落信道,本文提出了一种有效的数字调制方式自动识别的方法,该方法主要采用的技术包括噪声功率估计、构造高维特征矢量、使用基于人工神经网络的分类器.噪声估计有效地抑制了噪声影响,高维特征矢量的应用提高了不同调制信号的区分度,基于人工神经网络的分类器可以实现对高维特征空间较复杂的划分.仿真结果表明该方法显著地提高了低信噪比条件下数字调制方式的正确识别概率.     

Fuzzy wavelet packet based feature extraction method and its application to biomedical signal classification

In this paper, we develop an efficient fuzzy wavelet packet (WP) based feature extraction method for the classification of high-dimensional biomedical data such as magnetic resonance spectra. The key design phases involve: 1) a WP transformation mapping the original signals to many WP feature spaces and finding optimal WP decomposition for signal classification; 2) feature extraction based on the optimal WP decomposition; and 3) signal classification realized by a linear classifier. In contrast to the standard method of feature extraction used in WPs, guided by the criteria of signal compression or signal energy, our method is used to extract discriminatory features from the WP coefficients of the optimal decomposition. The extraction algorithm constructs fuzzy sets of features (via fuzzy clustering) to assess their discriminatory effectiveness. This paper includes a number of numerical experiments using magnetic resonance spectra. Classification results are compared with those obtained from common feature extraction methods in the WP domain.     

基于相关性分析和支持向量机的手部肌电信号动作识别

为了有效提取表面肌电信号(SEMG)的特征,该文提出了一种基于相关性分析的改进的特征提取方法。首先用空域相关法对两路SEMG信号进行消噪预处理,然后对处理后的SEMG信号进行四尺度小波变换,并通过相关性分析提取SEMG信号的重要边缘在各尺度上的小波系数,以各尺度上的这些系数的平方和构建六维特征向量输入支持向量机分类器,对手部的多个动作进行分类。实验结果表明,基于相关性分析和小波变换构筑的特征向量结合支持向量机的方法能够以较高识别率区分伸腕、屈腕、展拳、握拳4种动作,能够得到比传统的神经网络分类器更为准确的分类结果。     

Beamforming‐based feature extraction and RVM‐based method for attacker node classification in CRN

Cognitive radio is a promising technology for the future wireless spectrum allocation to improve the utilization rate of the licensed bands. However, the cognitive radio network is susceptible to various attacks. Hence, there arises a need to develop a highly efficient security measure against the attacks. This paper presents a beamforming‐based feature extraction and relevance vector machine (RVM)‐based method for the classification of the attacker nodes in the cognitive radio network. Initially, the allocation of the Rayleigh channel is performed for the communication. The quaternary phase shift keying method is used for modulating the signals. After obtaining the modulated signal, the extraction of the beamforming‐based features is performed. The RVM classifier is used for predicting the normal nodes and attacker nodes. If the node is detected as an attacker node, then communication with that node is neglected. Particle swarm optimization is applied for predicting the optimal channel, based on the beamforming feature values. Then, signal communication with the normal nodes is started. Finally, the signal is demodulated. The signal‐to‐noise ratio and bit‐error rate values are computed to evaluate the performance of the proposed approach. The accuracy, sensitivity, and specificity of the RVM classifier method are higher than the support vector machine classifier. The proposed method achieves better performance in terms of throughput, channel sensing/probing rate, and channel access delay. Copyright © 2016 John Wiley & Sons, Ltd.     

ECG beat recognition using fuzzy hybrid neural network

This paper presents the application of the fuzzy neural network for electrocardiographic (ECG) beat recognition and classification. The new classification algorithm of the ECG beats, applying the fuzzy hybrid neural network and the features drawn from the higher order statistics has been proposed in the paper. The cumulants of the second, third, and fourth orders have been used for the feature selection. The hybrid fuzzy neural network applied in the solution consists of the fuzzy self-organizing subnetwork connected in cascade with the multilayer perceptron, working as the final classifier. The c-means and Gustafson-Kessel algorithms for the self-organization of the neural network have been applied. The results of experiments of recognition of different types of beats on the basis of the ECG waveforms have confirmed good efficiency of the proposed solution. The investigations show that the method may find practical application in the recognition and classification of different type heart beats.     

基于BP神经网络的地震动信号识别

通过数据采集得到三种不同类型车辆的地震动信号,采用小波消噪和特征提取,得到样本数据对神经网络进行训练,训练完成的神经网络就能实现车辆类型的识别。试验结果表明,BP神经网络对车辆目标具有较高的识别率,证明对地震动信号的特征提取方法是正确的,人工神经网络是有效的目标识别方法。     

基于子空间对齐与自适应CSP算法的运动想象脑电信号分类

在脑机接口中,让分类器从一个用户适应到另一个用户是具有挑战性的,但对于减少新用户的训练时间是必要的.但由于每个个体的神经信号存在着差异,常用的特征提取方法训练的分类器,应用于不同的用户时,准确率很低.因此本文提出了一种新的自适应共空间模式的特征提取方法,该算法通过选择合适的候选试验更新协方差矩阵,然后对提取的特征进行子...