Visualization analysis of heart diseases using two‐dimensional electrocardiogram sequences

During the last years, there have been large advances in the understanding of cardiac mechanics, and in particular shape, motion and deformation. However, it is important to comprehend how these features interrelate and how they can be interpreted to refine the clinical diagnosis. The goal of our study is to illustrate the use of two‐dimensional ultrasound cardiac motion sequences on some typical cardiac pathologies. The method is applied to preprocessed data from two‐dimensional ultrasound sequences of four different subjects, namely, two healthy volunteers, one patient with left ventricular mechanical dyssynchrony from the left bundle branch block, and one patient with hypertrophic cardiomyopathy. The interpretation based on these features is corroborated with other analyzing techniques such as electrocardiogram and visual interpretation by experienced clinical observers. The proposed method realizes a computer program, which allows the user to easily obtain and visualize information about myocardial shape, motion (displacement and velocity), and deformation (strain) along the left ventricle myocardium wall during the cardiac cycle.     

Superior foetal electrocardiogram signal elicitation using a novel artificial intelligent Bayesian methodology

Innumerable casualties due to intrauterine hypoxia are a major worry during prenatal phase besides advanced patient monitoring with latest science and technology. Hence, the analysis of foetal electrocardiogram (fECG) signals is very vital in order to evaluate the foetal heart status for timely recognition of cardiac abnormalities. Regrettably, the latest technology in the cutting edge field of biomedical signal processing does not seem to yield the desired quality of fECG signals required by physicians, which is the major cause for the pathetic condition. The focus of this work is to extort non-invasive fECG signal with highest possible quality with a motive to support physicians in utilizing the methodology for the latest intrapartum monitoring technique called STAN (ST analysis) for forecasting intrapartum foetal hypoxia. However, the critical quandary is that the non-invasive fECG signals recorded from the maternal abdomen are affected by several interferences like power line interference, baseline drift interference, electrode motion interference, muscle movement interference and the maternal electrocardiogram (mECG) being the dominant interference. A novel hybrid methodology called BANFIS (Bayesian adaptive neuro fuzzy inference system) is proposed. The BANFIS includes a Bayesian filter and an adaptive neuro fuzzy filter for mECG elimination and non-linear artefacts removal to yield high quality fECG signal. Kalman filtering frame work has been utilized to estimate the nonlinear transformed mECG component in the abdominal electrocardiogram (aECG). The adaptive neuro fuzzy filter is employed to discover the nonlinearity of the nonlinear transformed version of mECG and to align the estimated mECG signal with the maternal component in the aECG signal for annulment. The outcomes of the investigation by the proposed BANFIS system proved valuable for STAN system for efficient prediction of foetal hypoxia.     

基于确定性测量矩阵的心电信号压缩采样算法研究

远程心电监测治疗中心电压缩技术至关重要.论文介绍了压缩采样理论的原理,根据确定性矩阵在压缩采样理论硬件实现上的优势,构造了一种多项式确定性矩阵,与块稀疏贝叶斯框架算法相结合,并在此基础上,对多项式确定性矩阵QR分解进行优化,实验表明:该矩阵可以满足实时高精度重建信号的要求,优化后的矩阵更进一步提高了算法性能.     

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。     

织物电极监测心电信号与穿戴压力作用机制分析

为探索穿戴压力对纺织结构心电电极采集心电图(ECG)信号质量的影响,开发了基于镀银锦纶丝束和涤纶的平纹和缎纹交织结构的4种导电织物电极。从皮肤-电极接触阻抗、舒适性以及织物方阻与穿戴压力的关系等方面评价了织物电极的性能,并测试了不同穿戴压力(2、5、10 kPa)下4种不同织物电极采集ECG信号的性能。结果表明:在不同穿戴压力下,不同结构的织物电极表现出不同的ECG信号采集能力和舒适性能;随着穿戴压力增加,所测试的导电织物方阻先呈下降趋势,然后趋于稳定,ECG信号质量不断增高;纯导电缎纹结构织物具有更好的透气透湿性,并且舒适性优于平纹结构织物;然而在舒适的穿戴压力(2、5 kPa)下,平纹结构织物电极表现出更好的ECG信号质量。     

心电信号去噪的数学形态学滤波器

为探索验证一种基于数学形态滤波器的去除心电基线漂移和工频干扰的高性能滤波器设计方法,借鉴数学形态学一维信号滤波原理,提出自适应阈值ECG去噪算法的思路,讨论了3σ统计准则在ECG自适应阈值滤波中的作用,利用改进的算法对心电图中常见的工频干扰和基线漂移进行校正。通过对MIT-BIH心率变异数据库中多组数据的仿真验证研究,验证了该算法能有效实现心电信号的噪声预处理;数学形态学理论在心电信号处理中具有良好性能,是实时处理一维生物医学信号有潜力的工具。     

丹参注射液对大鼠急性心肌缺血及血液流变学的影响

目的: 观察丹参注射液对大鼠急性心肌缺血模型及急性血淤模型的影响。方法: 以垂体后叶素制备大鼠急性心肌缺血模型, 以大剂量肾上腺素配合冰浴制备大鼠急性血淤模型, 观察丹参用药对急性心肌缺血模型大鼠心电图 J点和 T 波及急性血淤模型大鼠血液流变学的影响。结果: 丹参注射液中(4.0 g·kg^-1 )、高(8.0 g·kg^-1 )剂量均明显对抗急性心肌缺血心电图J 点和 T 波的升高(P <0.05或 P <0.01), 丹参各剂量组能明显预防急性血淤模型高、 中、低切变率的血粘度及红细胞压积的升高(P <0.05或 P <0.01)。结论: 丹参可有效降低血液粘度,并具改善冠脉循环,保护缺血心肌的作用。     

基于阻抗匹配与相位补偿的心电检测工频抑制

为有效抑制影响电生理信号检测质量的工频干扰,以心电检测为例,提出了皮肤电极阻抗自动匹配与右腿驱动信号的相位补偿相结合的新方法.其技术流程为：先通过皮肤电极阻抗自动补偿电路,采用逐次逼近优化算法实现阻抗自动平衡;再对右腿驱动放大电路的输出信号进行模数转换和滤波网络反馈处理以补偿其相位.实验证明,皮肤电极阻抗补偿可以将工频干扰降至2/3;改进右腿驱动电路后可将干扰进一步降至1/2.两者结合可将心电信号中工频干扰最终降至1/3.     

Efficient wavelet-based ECG processing for single-lead FHR extraction

This paper describes a novel model for fetal heart rate (FHR) monitoring from single-lead mother?s abdomen ECG (AECG) measurements. This novel method is divided in two stages: the first step consists on a one-step wavelet-based preprocessing for simultaneous baseline and high-frequency noise suppression, while the second stage efficiently detects fetal QRS complexes allowing FHR monitoring. The presented structure has been simplified as much as possible, in order to reduce computational cost and thus enable possible custom hardware implementations. Moreover, the proposed scheme and its fixed-point modeling have been tested using real abdominal ECG signals, which allow the validation of the presented approach and provide high accuracy.     

Blind Source Extraction for Noisy Mixtures by Combining Gaussian Moments and Generalized Autocorrelations

In the blind source extraction problem, the concept of generalized autocorrelations has been successfully used when the desired signal has special temporal structures. However, their applications are only limited to noise-free mixtures, which is not realistic. Therefore, this paper addresses the extraction of the noisy model based on these temporal characteristics of sources. An objective function, which combines Gaussian moments and generalized autocorrelations, is proposed. Maximizing this objective function, we present a blind source extraction algorithm for noisy mixtures. Simulations on synthesized signals, images, artificial electrocardiogram (ECG) data and the real-world ECG data show the better performance of the proposed algorithm. Moreover, comparisons with the existing algorithms further indicate its validity and also show its robustness to the estimated error of time delay.