Early detection of sudden cardiac death by using classical linear techniques and time-frequency methods on electrocardiogram signals

Early detection of sudden cardiac death may be used for surviving the life of cardiac patients. In this paper we have investigated an algorithm to detect and predict sudden cardiac death, by processing of heart rate variability signal through the classical and time-frequency methods. At first, one minute of ECG signals, just before the cardiac death event are extracted and used to compute heart rate variability (HRV) signal. Five features in time domain and four features in frequency domain are extracted from the HRV signal and used as classical linear features. Then the Wigner Ville transform is applied to the HRV signal, and 11 extra features in the time-frequency (TF) domain are obtained. In order to improve the performance of classification, the principal component analysis (PCA) is applied to the obtained features vector. Finally a neural network classifier is applied to the reduced features. The obtained results show that the TF method can classify normal and SCD subjects, more efficiently than the classical methods. A MIT-BIH ECG database was used to evaluate the proposed method. The proposed method was implemented using MLP classifier and had 74.36% and 99.16% correct detection rate (accuracy) for classical features and TF method, respectively. Also, the accuracy of the KNN classifier were 73.87% and 96.04%.

Emotion Measurement Using Biometric Signal

In recent years, research on the estimation of human emotions has been active, and its application is expected in various fields. Biological reactions, such as electroencephalography (EEG) and root mean square successive difference (RMSSD), are indicators that are less influenced by individual arbitrariness. The present study used EEG and RMSSD signals to assess the emotions aroused by emotion-stimulating images in order to investigate whether various emotions are associated with characteristic biometric signal fluctuations. The participants underwent EEG and RMSSD while viewing emotionally stimulating images and answering the questionnaires. The emotions aroused by emotionally stimulating images were assessed by measuring the EEG signals and RMSSD values to determine whether different emotions are associated with characteristic biometric signal variations. Real-time emotion analysis software was used to identify the evoked emotions by describing them in the Circumplex Model of Affect based on the EEG signals and RMSSD values. Emotions other than happiness did not follow the Circumplex Model of Affect in this study. However, ventral attentional activity may have increased the RMSSD value for disgust as the β/θ value increased in right-sided brain waves. Therefore, the right-sided brain wave results are necessary when measuring disgust. Happiness can be assessed easily using the Circumplex Model of Affect for positive scene analysis. Improving the current analysis methods may facilitate the investigation of face-to-face communication in the future using biometric signals.

Detection of healthy and pathological heartbeat dynamics in ECG signals using multivariate recurrence networks with multiple scale factors

The electrocardiogram(ECG)is one of the physiological signals applied in medical clinics to determine health status.The physiological complexity of the cardiac system is related to age,disease,etc.For the investigation of the effects of age and cardiovascular disease on the cardiac system,we then construct multivariate recurrence networks with multiple scale factors from multivariate time series.We propose a new concept of cross-clustering coefficient entropy to construct a weighted network,and calculate the average weighted path length and the graph energy of the weighted network to quantitatively probe the topological properties.The obtained results suggest that these two network measures show distinct changes between different subjects.This is because,with aging or cardiovascular disease,a reduction in the conductivity or structural changes in the myocardium of the heart contributes to a reduction in the complexity of the cardiac system.Consequently,the complexity of the cardiac system is reduced.After that,the support vector machine(SVM)classifier is adopted to evaluate the performance of the proposed approach.Accuracy of 94.1%and 95.58%between healthy and myocardial infarction is achieved on two datasets.Therefore,this method can be adopted for the development of a noninvasive and low-cost clinical prognostic system to identify heart-related diseases and detect hidden state changes in the cardiac system.

A Novel Radial Basis Function Neural Network Approach for ECG Signal Classification

ions in the ECG signal.The cardiologist and medical specialistfind numerous difficulties in the process of traditional approaches.The specified restrictions are eliminated in the proposed classifier.The fundamental aim of this work is tofind the R-R interval.To analyze the blockage,different approaches are implemented,which make the computation as facile with high accuracy.The information are recovered from the MIT-BIH dataset.The retrieved data contain normal and pathological ECG signals.To obtain a noiseless signal,Gaborfilter is employed and to compute the amplitude of the signal,DCT-DOST(Discrete cosine based Discrete orthogonal stock well transform)is implemented.The amplitude is computed to detect the cardiac abnormality.The R peak of the underlying ECG signal is noted and the segment length of the ECG cycle is identified.The Genetic algorithm(GA)retrieves the primary highlights and the classifier integrates the data with the chosen attributes to optimize the identification.In addition,the GA helps in performing hereditary calculations to reduce the problem of multi-target enhancement.Finally,the RBFNN(Radial basis function neural network)is applied,which diminishes the local minima present in the signal.It shows enhancement in characterizing the ordinary and anomalous ECG signals.

Attention-Based Residual Dense Shrinkage Network for ECG Denoising

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac diagnosis.However,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological analysis.Therefore,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic research.Aiming at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this paper.Firstly,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise suppression.Finally,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG signal.Experiments on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

刺绣心电电极设计与性能分析

为提高刺绣心电电极的灵敏性和稳定性,规范电极设计与应用,分别对刺绣电极的面积、图案、针迹进行设计,并分析其影响要素。采用控制变量的实验方案,分析电极厚度、平整度、皮肤-电极界面阻抗、信噪比、基线稳定时间、基线偏移幅度的变化规律,找出刺绣电极物理特征和电学性能的影响因素。研究结果发现:刺绣电极面积过大或过小都会对其传感灵敏性和捕捉的信号质量造成负面影响;刺绣电极图案越接近圆形其各项性能均越好;成品厚度均匀、表面平整的针迹制备的刺绣电极传感稳定性和信号质量更好;经过优化设计得出的5.3 cm^(2)圆形缎纹针迹刺绣电极,与3M公司生产的2223CN医用电极相比,皮肤-电极界面阻抗降低了1.064 MΩ,信噪比提高了3.133 dB,基线稳定时间缩短了3 s,基线偏移幅度减小0.07 mV,具有良好的外观平整度、电学灵敏性及稳定性。

一种融合KPCA、FastICA及SVD的腹壁源胎儿心电 信号提取算法研究

目的:为实现从母体腹壁混合信号中提取高信噪比和波形清晰的胎儿心电信号,提出一种融合核主成分分析(kernel principal component analysis,KPCA)、快速独立成分分析(fast independent component analysis,FastICA)及奇异值分解(singular value decomposition,SVD)的胎儿心电信号提取算法。方法:首先,采用KPCA对母体心电信号进行降维,再利用改进的基于负熵的FastICA处理降维后的数据,得到独立成分。随后,引入样本熵进行信号通道选择,挑选出包含最多母体信息的信号通道。在选中的母体通道上进行SVD,得到母体心电信号的近似估计,再用腹壁源信号减去该信号得到胎儿心电的初步估计。最后,采用改进的基于负熵的FastICA成功分离出纯净的胎儿心电信号。在腹部和直接胎儿心电图数据库(Abdominal and Direct Fetal Electrocardiogram Database,ADFECGDB)和PhysioNet 2013挑战赛数据库中对提出的算法进行验证。结果:提出的算法在主观视觉效果和客观评价指标上都表现出优越的性能。在ADFECGDB数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.74%、98.85%和99.30%;在PhysioNet 2013挑战赛数据库中,胎儿QRS复合波检测的敏感度、阳性预测值和F1值分别为99.10%、97.87%和98.48%。结论:融合KPCA、FastICA及SVD的胎儿心电信号提取算法在提取胎儿心电信号的同时有效处理了附加噪声,为胎儿疾病的早期诊断提供了有力支持。

基于LabVIEW的心电信号与多数据采集分析系统设计

近年来心血管疾病的发病率和死亡率不断攀升。为了给人们提供一个较为准确的心电信号分析结果,该系统以心电信号为感知节点,结合虚拟仪器技术,打造一个准确性更高、成本更低、更可靠的心电信号分析系统。它的优点在于会结合使用者的各种因素,如:所处气象、饮食、地理位置、身体状况和生活习惯等因素,并结合已经发展较为成熟的心电信号数据分析手段,在对连续的心电图数据实时采集处理与分析功能方面,此设备具有数据实时性、精度较高等特点。结果表明,结合了多数据的分析结果,比单一分析系统给出的结果更为准确,也更加能够满足人们的需要,这将对预防和治疗心血管疾病产生重要作用。

基于遗传算法优化最小二乘支持向量机的矿工疲劳程度识别模型

为精准识别矿工疲劳程度,减少因疲劳引发的煤矿人因事故,提出了一种基于遗传算法(GA)优化最小二乘支持向量机(LSSVM)的矿工疲劳程度识别模型。首先,通过疲劳诱发试验采集矿工心电数据,利用Friedman检验优选矿工疲劳程度的特征指标;然后,采用主成分分析法对选取的特征指标进行降维处理,建立表征矿工疲劳程度的特征集;在此基础上,利用遗传算法优化最小二乘支持向量机的关键参数,构建矿工疲劳程度识别模型。结果表明:选取的矿工疲劳程度特征指标能够有效反映矿工的疲劳程度;相较GA-SVM和LSSVM模型,融合GA-LSSVM模型可显著提高矿工疲劳程度的识别准确率(平均识别准确率为96.87%)。构建的矿工疲劳程度识别模型可较为高效地识别矿工的疲劳程度,对煤矿人因事故的防控具有一定的现实指导意义。

基于优化自适应模型的心律失常辅助诊断方法

针对心律失常诊断算法中存在的不平衡数据集诊断准确率及阳性预测值较低的问题,提出一种基于优化自适应模型的心律失常辅助诊断方法;提取心电信号的77维特征并将其融合,使用融合特征训练诊断模型,同时利用改进的粒子群算法优化自适应模型参数;采用优化模型对MIT-BIH心律失常数据库进行诊断实验并与现有方法进行对比。结果表明,本文所提方法在测试数据集的诊断准确率达到98.2%,正常或束支传导阻滞节拍、室上性异常节拍、心室异常节拍、融合节拍的阳性预测值分别达到98.5%、96.1%、95.5%、92.0%,诊断准确率和阳性预测值明显大于现有方法的。

基于特征融合的多通道心肌梗死定位模型

心肌梗死(MI)是心血管疾病(CVD)中常见的临床表现形式,在发病时具有较高的致命性,因此心肌梗死的快速定位对于避免死亡至关重要。目前基于心电图的心肌梗死位置定位模型在面对患者间的个体差异时泛化性能不足,同时传统的基于卷积的模型难以深入挖掘心电图导联与心肌梗死位置之间的关系。为解决这些问题,提出一种基于特征融合的多通道心肌梗死定位模型FF-ANN,该模型主要由特征融合模块和自适应的多通道注意力模块组成。通过特征融合模块整合临床知识中的关键波型特征(例如Q波、ST段等),使模型具有多种感受域,从而在不同维度上捕捉心肌梗死的特征;利用自适应的多通道注意力模块对融合后的特征进行重新标定,通过注意力权重加权对应的特征,使模型聚焦对预测有重要贡献的导联特征。通过在混合数据集PTB上验证模型的拟合能力,并使用迁移学习的方法将从PTB数据集中学习到的模型架构迁移到PTBXL数据集中进行泛化能力验证,结果表明,与现有研究相比,该模型在患者间方案下实现了约2.5%的提升,证明了该模型不仅具有较好的定位性能,也显示优越的泛化能力,其架构适用于现实世界中辅助心肌梗死定位的诊断。

胎儿心电图的应用与进展

对胎儿心电图(fetal electrocardiogram,FECG)进行简单介绍,阐述近年来FECG技术在检测胎儿心律失常、监测妊娠高血压综合征患者宫内胎儿缺氧状况等方面的临床应用,并梳理了FECG本身存在的局限性。本文还总结了FECG信号处理技术的进展,并展望FECG未来的发展前景。

两步式自适应阈值法滤除心电信号中运动伪迹

心电信号广泛应用于心脏疾病的医学检测中,可穿戴动态心电监测设备可以实现对心律失常的风险识别并预警.相比于静息心电信号,动态心电信号在采集过程中会受到更大运动伪迹的干扰,这些干扰会覆盖心电信号的关键信息,限制其临床应用.本文兼顾心电信号局部和全局特征,利用其周期性,研究了一种将心电信号低频PT波和高频QRS波群分开处理的两步式自适应阈值滤波算法,适用于单通道心电信号中的运动伪迹滤除.第一步先通过多分辨率阈值初步抑制心电信号低频部分中的运动伪迹;第二步,对受运动伪迹影响而不平衡的QRS波进行自适应阈值修复,通过对QRS波形调节,减少心电信号中高频部分运动伪迹,同时设置自适应阈值对心电信号P波、T波对应的小波系数进行处理,超出自适应阈值范围的小波系数通过波形缩放进行调整,进一步抑制低频运动伪迹.研究通过不同心电数据库评估算法的性能.在输入信噪比从-10~10 dB时,心电信号信噪比提升了10.9122 dB和4.3912 dB,滤波后心电信号与纯净心电信号的相关系数分别为0.6876和0.9783,提取的运动伪迹与原运动伪迹相关系数分别为0.9530和0.8529.实验结果表明,算法在不同噪声水平下,利用自适应阈值的优点,能有效复原受运动伪迹污染的心电信号波形特征,最大限度保留心电信号的临床信息,可作为可穿戴心电设备滤除运动伪迹的有效工具.

基于对比学习的心电信号情绪识别方法

现有的机器学习和深度学习在解决基于心电信号的情绪识别问题时主要使用全监督的学习方法。这种方法的缺点在于需要大量的有标签数据和计算资源。同时,全监督方法学习到的特征表示通常只能针对特定任务,泛化性较差。针对这些问题,提出了一种基于对比学习的心电信号情绪识别方法,该方法分为预训练和微调两步。预训练的目的是从未标记的心电数据中学习特征表示,具体为:设计了两种简单高效的心电信号增强方式,将原始数据通过这两种数据增强转换成两个相关但不同的视图;接着这两种视图在时间对比模块中学习鲁棒的时间特征表示;最后在上下文对比模块中学习具有判别性的特征表示。微调阶段则使用带标记数据来学习情绪识别任务。在三个公开数据集上的实验表明,该方法在心电信号情绪识别准确率上与现有方法相比提高了0.21%~3.81%。此外,模型在半监督设定场景中表现出高有效性。

Efficient ECG classification based on Chi-square distance for arrhythmia detection

This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.

声电成像在生物电流检测中的应用

心电、头皮脑电、表面肌电等传统无创生物电检测方法可为相关疾病诊断提供电学依据。由于生物电信号是机体细胞群共同放电的混叠集合结果,上述生物电检测方法空间分辨率相对有限。近些年兴起的声电成像利用无创聚焦超声空间编码生物电流,靶向获得精确聚焦位置的电信号,可实现毫米级空间分辨率、毫秒级时间分辨率的无创生物电信号检测,有望成为精准检测生命体深层电活动的新型成像技术。本文首先简述声电成像原理与声电信号特征,进而从声电耦合机理、声电成像方法、声电脑成像及声电心脏成像等方面详细介绍声电成像的典型研究,最后围绕声电成像关键技术环节所面临的挑战,对未来研究方向进行探讨,以期为建立完善的声电成像技术体系和实现其临床转化提供依据与启发。

基于多通道的睡眠呼吸暂停检测

目的为了提高检测性能和验证不同生理信号对睡眠呼吸暂停的检测结果,本文提出一种信号叠加和通道相加检测睡眠呼吸暂停的方法。方法首先对100例睡眠呼吸障碍患者的心电(electrocardiogram,ECG)和脑电(electroencephalogram,EEG)信号通过小波阈值方法进行预处理,其次进行通道相加和信号叠加,然后通过Relief特征选择算法对30个特征进行分析,最后采用支持向量机(support vector machine,SVM)构建睡眠呼吸暂停分类模型,并验证该模型的准确性。结果实验结果表明,通道相加和信号叠加时睡眠呼吸暂停检测的最高准确率分别为96.24%和96.18%。结论ECG和EEG两种信号叠加和通道相加的方法均可提高睡眠呼吸暂停检测结果,且X2(ECG)和C3-A2(EEG)通道相加检测结果最好。

Biomimetic Synthesized Conductive Copolymer EDOT-Pyrrole Electrodes for Electrocardiogram Recording in Humans

We report on electrodes fabricated with EDOT-Pyrrole copolymer through electrophoretic deposition and used for recording and sensing bio-electrical signals. We measured the electrical properties of the copolymer deposited on a stainless-steel substrate, and we performed Cyclic Voltammetry (CV) and Scanning Electron Microscopy (SEM) studies to characterize the morphological properties and copolymer distribution on the metal surface. We found that electrodes fabricated with EDOT-Pyrrole copolymer exhibit a high signal-to-noise ratio as well as an accurate and stable conductivity compared with other commonly used electroconductive polymers. Stainless-steel-coated EDOT-Pyrrole electrodes are suitable to record electrocardiograms in humans with high resolution comparable to standard silver-electrodes.

改进的两层BiLSTM的心电信号分割方法

心电信号的分割方法可以有效地反映运动员的心脏功能和身体机能状况.通过人工对心电信号的手动分割往往耗费大量的时间和精力.为了实现自动化的心电信号分割,本文提出了一种改进的两层双向长短期记忆网络(bi-directional long short-term memory,BiLSTM)的心电图分割算法,可以前向和后向分析时间序列,以检测和定位重要波段,如P波、QRS波群和T波.实验使用公开QT数据集进行验证,以模拟运动员在赛前的心电数据.在与LSTM,BiLSTM以及两层BiLSTM的对比实验中,本方法的所有评价指标均有所提升.其准确率达95.68%,召回率为91.62%,精确度为91.05%,特异性为96.64%,F1分数为91.41%.结果表明该方法对心电信号进行分割具有较好的效果.

葛根素通过调控NF-κB通路对大鼠心肌梗死后室性心律失常及体表心电图参数的影响

目的:探讨葛根素对大鼠心肌梗死后室性心律失常(VA)和体表心电图参数的影响及其可能作用机制。方法:选取65只SD大鼠,结扎冠状动脉制备心肌梗死模型,造模成功后随机分为模型组、葛根素组、脂多糖(LPS)组、LPS+葛根素组,另取15只大鼠只剪开心包不结扎冠状动脉作为假手术组。葛根素组腹腔注射120 mg/kg葛根素,每日1次,连续7 d;LPS组腹腔注射0.5 mg/kg LPS 1次;LPS+葛根素组腹腔注射0.5 mg/kg LPS 1次后,再注射120 mg/kg葛根素,每日1次,连续7 d;假手术组和模型组实验期间给予腹腔注射等量生理盐水。程序电刺激室性期前收缩法诱发大鼠VA后检测有效不应期(ERP)、心室颤动阈值。肢体导联心电图机检测大鼠体表心电图。酶联免疫吸附实验(ELISA)法检测血清肿瘤坏死因子α(TNF-α)和白细胞介素1β(IL-1β)含量。2,3,5-氯化三苯基四氮唑(TTC)染色观察大鼠心肌梗死面积。蛋白免疫印迹(Western Blot)法检测心肌组织中核转录因子κB(NF-κB)通路相关蛋白表达水平。结果:与模型组比较,葛根素组VA诱发率、T波幅度、ST段抬高幅度、心肌梗死面积比例、血清TNF-α和IL-1β水平、p-p65相对表达量降低,ERP延长,心室颤动阈值、NF-κB抑制剂α(IκBα)相对表达量升高,心电图QT间期缩短,均P<0.05;LPS组VA诱发率、T波幅度、ST段抬高幅度、心肌梗死面积比例、血清TNF-α和IL-1β水平、p-p65相对表达量升高,ERP缩短,心室颤动阈值、IκBα相对表达量降低,心电图QT间期延长,均P<0.05。与葛根素组比较,LPS+葛根素组VA诱发率、T波幅度、ST段抬高幅度、心肌梗死面积比例、血清TNF-α和IL-1β水平、p-p65相对表达量升高,ERP缩短,心室颤动阈值、IκBα相对表达量降低,心电图QT间期延长,均P<0.05。与LPS组比较,LPS+葛根素组VA诱发率、T波幅度、ST段抬高幅度、心肌梗死面积比例、血清TNF-α和IL-1β水平、p-p65相对表达量降低,ERP延长,心室颤动阈值、IκBα相对表达量升高,心电图QT间期缩短,均P<0.05。结论:葛根素可以改善大鼠心肌梗死后VA,影响体表心电图参数,其作用机制可能与调控NF-κB通路有关。