Effective simulation of signals for testing ECG analyzer

In this paper the solution of the specialized measuring system for electrocardiography (ECG) signal recording and introductory recognition is presented. The project aims at designing the complete PC-based Virtual Instrument as a "testing platform" for acquisition, processing, presenting, and distributing ECG data. A new design involving the latest technique in signal simulation was developed to create a controllable model of the electrocardiography signal. Then it was implemented for testing of the developed QRS detection algorithm, based on the time-frequency analysis method. The processing stage involving discrete wavelet transform was used to detect QRS complexes in the ECG signal. By using the controlled signal model and the real ones, the algorithm was shown to be advantageous in reducing ventilation artifacts and motion noise, resulting in good accuracy.     

Estimating the relationship between amplitude phase and integral characteristics of the electrocardiogram of the healthy person medical and biological measurements

A method of studying electrocardiograms in the form of an R-rhythmic volt-second characteristic is proposed. Processing of the ECG record consists in determining the coordinates of the R-peaks and the formation of digital lines of the R and RR units with subsequent weighted-mean averaging of the coordinated variational series. The concept of a normalized mean selective potential is introduced.     

Effectiveness Assessment of the Search-Based Statistical Structural Testing

In this era of electronic health, healthcare data is very important because it contains information about human survival. In addition, the Internet of Things (IoT) revolution has redefined modern healthcare systems and management by providing continuous monitoring. In this case, the data related to the heart is more important and requires proper analysis. For the analysis of heart data, Electrocardiogram (ECG) is used. In this work, machine learning techniques, such as adaptive boosting (AdaBoost) is used for detecting normal sinus rhythm, atrial fibrillation (AF), and noise in ECG signals to improve the classification accuracy. The proposed model uses ECG signals as input and provides results in the form of the presence or absence of disease AF, and classifies other signals as normal, other, or noise. This article derives different features from the signal using Maximal Information Coefficient (MIC) and minimum Redundancy Maximum Relevance (mRMR) technique, and then classifies them based on their attributes. Since the ECG contains some kind of noise and irregular data streams so the purpose of this study is to remove artifacts from the ECG signal by deploying the method of Second-Order-Section (SOS) (filter) and correctly classify them. Several features were extracted to improve the detection of ECG data. Compared with existing methods, this work gives promising results and can help improve the classification accuracy of the ECG signals.     

二维主元分析法在心肌梗死心电信号检测中的应用

利用基于联合能量百分比搜索的二维主元分析法对12导高分辨率心电信号（ECG）进行全局特征提取和分类检测研究。所用数据取自PTB诊断数据库,包括健康状态ECG,早期心肌梗死（MI）ECG,急性期MI ECG,恢复期 MI ECG。结果表明,所用的方法能有效地融合12导ECG信号及其高频分量中的细微结构信息,与常规主元分析法相比,其平均分类检测精度可提高10.43%,与常规二维主元分析法相比,能得到维数更低的特征表示,并可获得99.46 %的平均分类检测精度。     

基于包络解调随机共振和CEEMD的机械早期微弱故障诊断方法研究

针对噪声背景下机械振动信号早期故障特征提取难题,提出一种基于包络解调随机共振和互补总体经验模态分解的机械早期微弱故障提取及诊断新方法。首先对含噪声机械故障信号进行包络解调处理,然后对包络信号进行变尺度随机共振输出,使故障特征信号得到增强,最后对处理后的信号进行互补总体经验模态分解(CEEMD),得到机械振动信号故障特征分量,实现故障特征提取及诊断。对机械故障诊断实例表明,该方法不仅能增强信号幅值,同时减少了虚假分量,提高了CEEMD算法的精度,有效提取出被噪声淹没的微弱故障信号,提高了机械早期故障诊断效果。     

An automated algorithm for online detection of fragmented QRS and identification of its various morphologies

Fragmented QRS (f-QRS) has been proven to be an efficient biomarker for several diseases, including remote and acute myocardial infarction, cardiac sarcoidosis, non-ischaemic cardiomyopathy, etc. It has also been shown to have higher sensitivity and/or specificity values than the conventional markers (e.g. Q-wave, ST-elevation, etc.) which may even regress or disappear with time. Patients with such diseases have to undergo expensive and sometimes invasive tests for diagnosis. Automated detection of f-QRS followed by identification of its various morphologies in addition to the conventional ECG feature (e.g. P, QRS, T amplitude and duration, etc.) extraction will lead to a more reliable diagnosis, therapy and disease prognosis than the state-of-the-art approaches and thereby will be of significant clinical importance for both hospital-based and emerging remote health monitoring environments as well as for implanted ICD devices. An automated algorithm for detection of f-QRS from the ECG and identification of its various morphologies is proposed in this work which, to the best of our knowledge, is the first work of its kind. Using our recently proposed time–domain morphology and gradient-based ECG feature extraction algorithm, the QRS complex is extracted and discrete wavelet transform (DWT) with one level of decomposition, using the ‘Haar’ wavelet, is applied on it to detect the presence of fragmentation. Detailed DWT coefficients were observed to hypothesize the postulates of detection of all types of morphologies as reported in the literature. To model and verify the algorithm, PhysioNet''s PTB database was used. Forty patients were randomly selected from the database and their ECG were examined by two experienced cardiologists and the results were compared with those obtained from the algorithm. Out of 40 patients, 31 were considered appropriate for comparison by two cardiologists, and it is shown that 334 out of 372 (89.8%) leads from the chosen 31 patients complied favourably with our proposed algorithm. The sensitivity and specificity values obtained for the detection of f-QRS were 0.897 and 0.899, respectively. Automation will speed up the detection of fragmentation, reducing the human error involved and will allow it to be implemented for hospital-based remote monitoring and ICD devices.     

Efficient Data Compression of ECG Signal Based on Modified Discrete Cosine Transform

This paper introduced an efficient compression technique that uses the compressive sensing (CS) method to obtain and recover sparse electrocardiography (ECG) signals. The recovery of the signal can be achieved by using sampling rates lower than the Nyquist frequency. A novel analysis was proposed in this paper. To apply CS on ECG signal, the first step is to generate a sparse signal, which can be obtained using Modified Discrete Cosine Transform (MDCT) on the given ECG signal. This transformation is a promising key for other transformations used in this search domain and can be considered as the main contribution of this paper. A small number of wavelet components can describe the ECG signal as related work to obtain a sparse ECG signal. A sensing technique for ECG signal compression, which is a novel area of research, is proposed. ECG signals are introduced randomly between any successive beats of the heart. MIT-BIH database can be represented as the experimental database in this domain of research. The MIT-BIH database consists of various ECG signals involving a patient and standard ECG signals. MATLAB can be considered as the simulation tool used in this work. The proposed method's uniqueness was inspired by the compression ratio (CR) and achieved by MDCT. The performance measurement of the recovered signal was done by calculating the percentage root mean difference (PRD), mean square error (MSE), and peak signal to noise ratio (PSNR) besides the calculation of CR. Finally, the simulation results indicated that this work is one of the most important works in ECG signal compression.     

Sparse electrocardiogram signals recovery based on solving a row echelon‐like form of system

The study of biology and medicine in a noise environment is an evolving direction in biological data analysis. Among these studies, analysis of electrocardiogram (ECG) signals in a noise environment is a challenging direction in personalized medicine. Due to its periodic characteristic, ECG signal can be roughly regarded as sparse biomedical signals. This study proposes a two‐stage recovery algorithm for sparse biomedical signals in time domain. In the first stage, the concentration subspaces are found in advance. Then by exploiting these subspaces, the mixing matrix is estimated accurately. In the second stage, based on the number of active sources at each time point, the time points are divided into different layers. Next, by constructing some transformation matrices, these time points form a row echelon‐like system. After that, the sources at each layer can be solved out explicitly by corresponding matrix operations. It is noting that all these operations are conducted under a weak sparse condition that the number of active sources is less than the number of observations. Experimental results show that the proposed method has a better performance for sparse ECG signal recovery problem.Inspec keywords: electrocardiography, matrix algebra, medical signal processingOther keywords: sparse electrocardiogram signal recovery, row echelon‐like form of system, noise environment, biological data analysis, personalised medicine, dictionary learning algorithm, transformation matrices, sparse biomedical signal recovery     

基于VMD和窄带包络相关的船舶辐射噪声改进DEMON分析

船舶辐射噪声的包络谱中蕴含着轴频和桨叶数等船舶固有特征信息,对船舶目标识别具有重要意义。为了提高船舶辐射噪声包络谱解调性能,提出了基于变分模态分解(Variational Mode Decomposition, VMD)和窄带包络相关的改进DEMON分析方法。首先利用VMD算法代替传统带通滤波器,将船舶辐射噪声信号分解为若干个子带;然后对各子带进行希尔伯特(Hilbert)检波并计算平均窄带包络相关系数,用于衡量信号的包络调制在频域上的非均匀性;最后提取各子带信号包络谱并按照平均窄带包络相关系数进行加权融合,从而得出宽带噪声信号的包络谱。利用该方法对实测不同类型和不同航速船舶辐射噪声信号进行了处理,结果均表明所提方法能有效提高包络谱解调效果,较传统方法更为有效。     

一种自动提取IVUS图像血管包络的新方法

提取血管内超声(IVUS)图像的血管包络对冠状动脉疾病的诊断有一定的积极意义。本文综合考虑IVUS图像的灰度特征、序列时间特性、先验知识等三类信息,提出一种自动提取血管包络的方法。先由序列时间特性和先验知识减少噪声和伪像干扰,提取出第一帧图像的初始包络;然后用结合梯度、灰度方差、灰度均值信息的B样条GVFsnake对初始包络进行变形得到第一帧的最终包络;最后利用序列图像的时间特性提取后续帧的包络。通过实验表明:综合三类信息的包络自动提取方法在精度和鲁棒性等方面优于以往的方法。     

基于Hilbert振动分解和高阶能量算子的行星齿轮箱故障诊断研究

行星齿轮箱广泛应用于各种机械设备中,其故障诊断问题是近年来的研究热点之一。提出了基于Hilbert振动分解和高阶微分能量算子的故障诊断方法。Hilbert振动分解计算复杂性低,能够将复杂信号分解为单分量,应用该方法对信号进行分解,满足高阶微分能量算子的要求。高阶微分能量算子的时间分辨率高,对信号的瞬态变化具有良好的自适应性,应用该方法检测故障引起的瞬态冲击,估计信号的幅值包络和瞬时频率。对高阶微分能量算子输出以及幅值包络和瞬时频率进行Fourier变换,通过频谱识别特征频率,从而诊断行星齿轮箱故障。分析了行星齿轮箱的仿真信号和实验信号,准确地诊断了太阳轮、行星轮和齿圈的故障,验证了该方法的有效性。     

Stabilization of the Barkhausen Noise Parameters

The Barkhausen noise was measured in nonoriented Fe–3%Si steel with different average grain sizes. Air gaps between the yoke and the measured objects were also varied during the measurements. The change of the grain size was achieved by different combinations of cold rolling and heat treatment processes. The rise of the gap size degraded the level of the Barkhausen noise and caused that the parameters of the Barkhausen noise, such as the amplitude of the Barkhausen noise's envelope, decreased. In order to suppress the influence of the air gap size on the measurement results, we analyzed the amplitude probability distribution of the Barkhausen noise and we found that the distribution at small levels of the voltage practically does not change with the grain size, but it increases with rise of the gap size. This change of the amplitude probability distribution with the gap size was used to correct the amplitude of the Barkhausen noise's envelope. It was shown that the correction essentially increases the precision of the evaluation of the grain size at varying air gap. Similar technique can be used also to decrease the error of evaluation of other microstructural changes of ferromagnetic materials using the Barkhausen noise method, without measuring and feedback setting the defined waveform of the magnetic field in the sample. For this method to be applicable, several conditions should be met, especially the level of the Barkhausen noise should be essentially higher than the level of the disturbing noise.      

铁路客车内场噪声预测与控制

统计能量分析方法适于解决高频高模态密度的复杂动力学问题。采用该方法对25型铁路客车进行研究。用模态相似群法建立整车统计能量分析模型,仿真预测客车内场噪声的分布情况和噪声的主要传播路径,进而提出降低车内噪声的措施,尽管得出的是相对的结果,但是这些结论可以使设计者在设计初期考虑噪声指标成为可能。     

差分振子时间历程在微弱调制信号检测中的应用

单一的差分振子仅可实现对周期信号中某一频率成分进行检测,对于强噪声背景下的边频带,尽管可以利用多个差分振子组成差分阵列进行逐个检测,进而确定边频带的间隔,但这种做法无疑会带来巨大的计算量。在对调制信号进行Hilbert变换包络分析时,所得到的时域信号是原始调制信号中的低频分量,亦是调制波信号,若该低频分量仍然包含较强的噪声成分,传统的频谱分析将会失效。此时,我们可借助差分振子时间历程对含较强的噪声的包络进行检测。因此,提出基于差分振子时间历程的微弱调制信号检测方法,即首先对信号进行Hilbert包络解调,然后利用差分振子时间历程对含较强的噪声的幅值包络进行检测,并成功应用于风机早期故障检测中。     

Signal Quality Improvement Using a New TMSSE Algorithm: Application in Delamination Detection in Composite Materials

This paper introduces a novel method to improve the quality of ultrasonic phased array signals for localizing with accuracy delamination defects. The improvement is achieved by the introduction of a new threshold for the Shannon energy. In first, we have applied the threshold modified S-transform algorithm (TMST) in the case of ultrasound B-scan. Thereafter, we have adapted and applied the S-transform Shannon energy (SSE) algorithm in the field of ultrasonic testing. At last, we have proposed a novel algorithm based on threshold modified S-transform and Shannon energy (TMSSE) to increase the improvement of the ultrasound B-scan. A simulation study has been carried out simulating a composite material containing three defects in different positions in order to highlight the phenomenon of delamination. Experimental tests were performed on a sample of carbon fiber reinforced polymer composite material (CFRP) with a delamination defect close to the front face. Both experimental and simulated results show that the proposed method can improve the quality of ultrasound B-scan which enhances the localization of delamination defects.     

基于Frank导联和Hermite展开的心肌梗死检测

多数现有的计算机辅助心电(ECG)诊断技术研究通常是基于常规心电导联展开的,而正交Frank心电导联比常规心电导联有着与解剖学更为密切的联系.基于Frank导联的心肌梗死(MI)心电特征提取和分类检测的研究,对MI ECG信号进行Hermite非线性展开,以Hermite系数为心电特征,并对其进行分类测试.与常规心电导联相比,此方法对早期MI和急性期MI进行分类,检测精度可分别提高30.06%和19.33%.     

Characterization of digital waveforms using thermodynamic analogs: applications to detection of materials defects

We describe characterization of digital signals using analogs of thermodynamic quantities: the topological entropy, Shannon entropy, thermodynamic energy, partition function, specific heat at constant volume, and an idealized version of Shannon entropy in the limit of digitizing with infinite dynamic range and sampling rate. We show that analysis based on these quantities is capable of detecting differences between digital signals that are undetectable by conventional methods of characterization based on peak-to-peak amplitude or signal energy. We report the results of applying thermodynamic quantities to a problem from nondestructive materials evaluation: detection of foreign objects (FO) embedded near the surface of thin graphite/epoxy laminates using backscattered waveforms obtained by C-scanning the laminate. The characterization problem was to distinguish waveforms acquired from the region containing the FO from those acquired outside. In all cases the thermodynamic analogs exhibit significant increases (up to 20-fold) in contrast and for certain types of FO materials permit detection when energy or amplitude methods fail altogether.     

Stationary response of bilinear hysteretic system driven by Poisson white noise

Stationary response of single-degree-of-freedom (SDOF) bilinear hysteretic system driven by Poisson white noise is investigated via stochastic averaging of energy envelope in this paper. The averaged generalized Fokker–Planck–Kolmogorov (GFPK) equation for SDOF bilinear hysteretic system driven by Poisson white noise is derived and the approximate stationary solutions of the averaged GFPK equation are obtain by using a modified exponential polynomial closure method. The effectiveness and accuracy of the approximate solution are assessed by performing appropriate Monte Carlo simulations. It is found that analytical and numerical results agree well and the effect of non-Gaussianity of the excitation process on stationary probability densities of total energy and displacement of bilinear hysteretic system is predicted successfully via stochastic averaging of energy envelope.     

基于改进自适应变分模态分解的滚动轴承微弱故障诊断

滚动轴承早期故障信息微弱,且混有大量背景噪声,难以提取其故障特征。提出了一种改进的自适应变分模态分解(AVMD)与Teager能量谱的微弱故障诊断方法。将最小平均包络熵(MMEE)作为目标函数,自动搜寻影响参数最佳值,确保变分模态分解(VMD)实现最优分解,并提出加权峭度指标(WK)用于选择有效模态分量进行信号重构,对重构信号进行Teager能量谱分析,从而识别故障特征频率。对轴承微弱故障振动信号的研究表明,所提方法改进了传统VMD算法分解精度受参数影响较大,导致信号出现过分解或欠分解的问题;与集合经验模态分解和局部均值分解算法相比所提方法具有更强的噪声鲁棒性和故障信息提取能力。