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BP神经网络的心电图ST段的模式识别

心电图ST段对心脏疾病的诊断具有重要意义.在正确读取采集于郑州大学一附院的运动心电图数据基础上,利用小波变换更准确地确定其ST段的起始和终止位置,初步探讨了BP神经网络用于心电图ST段识别的方法,并用此方法识别出心电图ST段的三种类型-正常、水平压低和抬高.实验结果较好.     

Event-driven,pattern-based methodology for cost-effective development of standardized personal health devices

Experiences applying standards in personal health devices (PHDs) show an inherent trade-off between interoperability and costs (in terms of processing load and development time). Therefore, reducing hardware and software costs as well as time-to-market is crucial for standards adoption. The ISO/IEEE11073 PHD family of standards (also referred to as X73PHD) provides interoperable communication between PHDs and aggregators. Nevertheless, the responsibility of achieving inexpensive implementations of X73PHD in limited resource microcontrollers falls directly on the developer. Hence, the authors previously presented a methodology based on patterns to implement X73-compliant PHDs into devices with low-voltage low-power constraints. That version was based on multitasking, which required additional features and resources. This paper therefore presents an event-driven evolution of the patterns-based methodology for cost-effective development of standardized PHDs. The results of comparing between the two versions showed that the mean values of decrease in memory consumption and cycles of latency are 11.59% and 45.95%, respectively. In addition, several enhancements in terms of cost-effectiveness and development time can be derived from the new version of the methodology. Therefore, the new approach could help in producing cost-effective X73-compliant PHDs, which in turn could foster the adoption of standards.     

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.     

广义高斯核模型在心电信号稀疏分解中的应用

稀疏分解得到的心电信号表示结果对心电信号的压缩、特征提取等非常有利。根据心电信号的波形特点,提出了新的基于广义高斯函数的核模型。新的核模型通过正交最小二乘算法进行逐步回归建模,每一个核函数的中心,尺度及形状参数由重复加权提升搜索算法优化得到。为了避免过拟合,运用交叉验证的方法进行迭代终止时的阈值选择。实验数据来自MIT-BIH心电数据库,结果表明,对于心电信号,新的核模型具有稀疏性好,泛化能力高等优点。     

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.     

基于改进小波阈值法的平移不变心电信号去噪

人体心电信号在采集过程中掺杂着各种噪声信号,而常用的小波阈值法在心电信号去噪中存在缺陷,为此在小波阈值法的基础上进行改进,得到新的阈值去噪方法,该方法能够较好保护心电信号特征,有效保持各种心电波形的幅度,并在一定程度上有效抑制脉冲噪声.此外,由于传统小波阈值法去噪都是基于离散小波变换进行的,所以在幅度较小的Q,S波处会产生Pseudo-Gibbs现象,而文中以改进的小波阈值法为基础对心电信号进行平移不变的去噪处理,有效地克服了这个问题.利用美国麻省理工学院的MIT-BIH心电数据库对以上方法进行验证,取得了良好的去噪效果.实验结果表明,所提出的算法与传统的小波阈值法比起来能够更好的保持心电信号的几何特征,且具有更高的信噪比.     

用于心电信号捕捉的高性能棉/银复合织物电极

导电织物在可穿戴电子领域前景广阔,但在使用过程的稳定性仍需提高。通过缩合反应,在棉织物(CF)上接枝了γ-巯丙基三甲氧基硅烷(MPTS),再利用化学镀的方式沉积银颗粒,制备出棉/银复合织物电极(Ag/M-CF)。Ag/M-CF的表面方阻低至0.04 Ω/sq,而且在多个方面(洗涤、拉伸、弯曲和氧化)都有良好的稳定性。洗涤200次后,表面方阻仅为1.88 Ω/sq,导电性能良好,满足实际需要；拉伸5%后,表面方阻仅为1.00 Ω/sq；经过3000次弯曲后,Ag/M-CF的表面方阻增加至1.38 Ω/sq；在恒温恒湿环境中放置9周后,表面方阻仅为0.50 Ω/sq。将洗涤前后的Ag/M-CF植入智能服装中,都能成功捕捉到人体在运动状态下的心电信号,而且测得的心率几乎相同。除此之外,Ag/M-CF还具有良好的机械性能和抗菌性。关键词：织物电极；洗涤稳定性；弯曲性能；耐氧化性能；心电图中图分类号：TS195.5 文献标识码： A 文章编号：1003-5214 (2020) 01-0000-00     

心脏病与高频心电研究

寻求心脏病的早期诊断方法是当务之急,高频心电除了具有较宽的通频带和较小信号幅度外,它比常规心电图具有更多的有关心脏活动状态的信息,可以观察到更多有关心脏主要部位病变的细节,能揭示出QRS综合波上某些部位的波形特征,所谓切迹(motch)和转折(slur)。本文重点研究高频心电的分析和识别方法及其技术实现。