基于双阶段特征提取网络的ECG降噪分类算法

临床采集到的标准12导联心电图常含有噪声,影响了心电信号分类结果的准确度,为此提出了一种基于双阶段特征提取网络的心电图(ECG)降噪分类算法。首先,在空间特征提取阶段,由深度耦合软阈值化去噪方法的残差收缩网络从输入的12导联标准心电信号中提取空间特征;其次,在时间特征提取阶段,由长短期记忆网络与注意力机制结合继续从心电信号中提取时间特征;最后,通过全连接网络层融合提取到的空间特征与时间特征,输出9个类别的概率预测分布。在CPSC2018数据集上与其他同类型先进分类算法进行了对比实验,验证所提算法的效果,实验结果表明:提出的分类算法在对9类ECG信号进行分类时平均F1分数达到0.854,在各项指标上表现更优。此外,实验证明所提算法在含噪数据中的表现也优于其他主流网络,充分证明了所提算法对于含噪心电信号的降噪分类性能,该算法也可应用于其他类似含噪声生理信号的分析和处理。

腔内ECG定位技术联合体外测量法在PICC中的应用

目的:探讨腔内心电图(ECG)定位技术联合体外测量法在经外周静脉穿刺的中心静脉导管(PICC)中的应用。方法:选取2021年1月—2023年1月阜宁县人民医院收治的100例行上肢PICC置管的患者作为研究对象。根据抛币法将其随机分为观察组和对照组,各50例。两组均进行PICC,对照组PICC应用体外测量法,观察组PICC应用腔内ECG定位技术联合体外测量法。比较两组一次性置管情况、导管相关并发症、置管满意度。结果:观察组置管准确率为98.00%,高于对照组的86.00%,置管过深率低于对照组,差异有统计学意义(P<0.05)。两组并发症发生率比较,差异无统计学意义(P>0.05)。观察组总满意度为100%,高于对照组的92.00%,差异有统计学意义(P<0.05)。结论:腔内ECG定位技术联合体外测量法可提高一次置管准确率,提高患者满意率。

基于深度学习的ECG信号分类与诊断

心电图(ECG)信号描绘了心脏的电活动,提供了有关心脏状态的重要信息。ECG信号分类可用于临床预测、诊断、评估的成果,对于心脏病的自动诊断非常重要。但是基于机器学习的ECG信号分类研究也存在一些如模型复杂度与临床数据实时传输和及时更新等未能解决的问题。因此,笔者首先对近10年来基于机器学习的ECG信号分类从波形形态分类、疾病诊断分类和纯粹的机器学习分类研究进行了回顾与综述,总结出了目前的研究遇到的困境,最后对未来面临的问题进行展望。深入学习模型在现实应用中仍存在一些挑战,未来的研究将进一步探索在芯片中实现机器学习模型的便携性和成本效益的硬件解决方案。此外,机器学习算法应寻求最佳的计算开销平衡,并重视在现实世界环境中的应用。在未来研究中,ECG应多进行临床试验,以评估机器学习模型在处理实际生物医学信号时的有效性和可行性,同时构造性价比高的深度学习模型,以帮助医学专家进行精确和及时的预测和诊断。

超声引导下改良送鞘术结合ECG在血液病患者PICC置管中的应用

目的 探讨超声引导下改良送鞘术结合心电图(ECG)在血液病患者PICC置管中的应用。方法 选取广西医科大学第一附属医院2021年1月至2023年1月收治的110例血液病患者为研究对象,按照随机数字表法分为对照组(右侧55例,采用塞丁格技术送血管鞘进行)和观察组(右侧55例,采用超声引导下改良送鞘术结合ECG进行)。置管7 d后观察2组穿刺点情况、置管情况、换药次数、一次性成功率、穿刺点出血量以及并发症情况。结果 观察组穿刺点渗血、异常、机械性静脉炎发生率低于对照组(P<0.05)。观察组患者送鞘完成时间短于对照组,置管即刻疼痛评分低于对照组(P<0.05)。96 h内观察组患者换药次数低于对照组(P<0.05)。观察组一次性成功率高于对照组(P<0.05)。观察组患者穿刺点出血量与对照组比较差异无统计学意义(P>0.05)。治疗后,观察组感染、渗液等并发症发生率与对照组比较差异无统计学意义(P>0.05)。结论 超声引导下改良送鞘术结合ECG的成功率更高,能够改善穿刺点情况,缩短送鞘完成时间,降低疼痛,值得临床推广。

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.

Wearable multilead ECG sensing systems using on-skin stretchable and breathable dry adhesives

Electrocardiogram(ECG)monitoring is used to diagnose cardiovascular diseases,for which wearable electronics have attracted much attention due to their lightweight,comfort,and long-term use.This study developed a wearablemultilead ECG sensing system with on-skin stretchable and conductive silver(Ag)-coated fiber/silicone(AgCF-S)dry adhesives.Tangential and normal adhesion to pigskin(0.43 and 0.20 N/cm2,respectively)was optimized by the active control of fiber density and mixing ratio,resulting in close contact in the electrode–skin interface.The breathableAgCF-S dry electrodewas nonallergenic after continuous fit for 24 h and can be reused/cleaned(>100 times)without loss of adhesion.The AgCF encapsulated inside silicone elastomers was overlapped to construct a dynamic network under repeated stretching(10%strain)and bending(90°)deformations,enabling small intrinsic impedance(0.3,0.1 Hz)and contact impedance variation(0.7 k)in high-frequency vibration(70 Hz).All hard/soft modules of the multilead ECG system were integrated into lightweight clothing and equipped with wireless transmission for signal visualization.By synchronous acquisition of I–III,aVR,aVL,aVF,and V4 lead data,the multilead ECG sensing system was suitable for various scenarios,such as exercise,rest,and sleep,with extremely high signal-to-noise ratios.

Automatic Extraction of Medical Latent Variables from ECG Signals Utilizing a Mutual Information-Based Technique and Capsular Neural Networks for Arrhythmia Detection

From a medical perspective,the 12 leads of the heart in an electrocardiogram(ECG)signal have functional dependencies with each other.Therefore,all these leads report different aspects of an arrhythmia.Their differences lie in the level of highlighting and displaying information about that arrhythmia.For example,although all leads show traces of atrial excitation,this function is more evident in lead II than in any other lead.In this article,a new model was proposed using ECG functional and structural dependencies between heart leads.In the prescreening stage,the ECG signals are segmented from the QRS point so that further analyzes can be performed on these segments in a more detailed manner.The mutual information indices were used to assess the relationship between leads.In order to calculate mutual information,the correlation between the 12 ECG leads has been calculated.The output of this step is a matrix containing all mutual information.Furthermore,to calculate the structural information of ECG signals,a capsule neural network was implemented to aid physicians in the automatic classification of cardiac arrhythmias.The architecture of this capsule neural network has been modified to perform the classification task.In the experimental results section,the proposed model was used to classify arrhythmias in ECG signals from the Chapman dataset.Numerical evaluations showed that this model has a precision of 97.02%,recall of 96.13%,F1-score of 96.57%and accuracy of 97.38%,indicating acceptable performance compared to other state-of-the-art methods.The proposed method shows an average accuracy of 2%superiority over similar works.

Effect of Exogenous Hydrogen Sulfide(H_2S) on the Electrocardiogram(ECG) of Rats Generally Anaesthetized by Zoletil

Hydrogen sulfide （H2S） is the third gaseous signaling molecule discovered in recent years, and plays an important physiological role in the cardivascular system. To explore the effects of different doses of exogenous H2S on the electrocardiogram （ECG） of rats generally anesthetized by zoletil, different doses of NariS solution were used for the intervention of intraperitoneal injection 20 rain before the zoletil anesthesia. The ECGs of rats from each treatment group during the time range of 10^th-50^th min were determined under general anesthesia, and then were compared with those from the control group. The results showed that exogenous H2S could significantly reduce the Q-T interval time limit, thus played a role in slowing tachycardia or arrhythmia and other anomalies, thereby protecting the heart. S-T segment and T segment evaluation values were significantly reduced, which might be associated with bradycardia.

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.

The Role of Electrocardiogram DETERMINE Score in Prediction of Coronary Artery Disease Severity

Background: A major cause of mortality and disability on a global scale is myocardial infarction (MI). These days, the most reliable way to detect and measure MI is via cardiovascular magnetic resonance imaging (CMR). Aims and Objectives: To evaluate the effectiveness of the Electrocardiogram DETERMINE Score in predicting the severity of coronary artery disease (CAD) in patients who have experienced an Acute Myocardial Infarction (AMI) & to assess improvements in left ventricular function at 6 months following coronary artery bypass grafting (CABG). Subjects and Methods: This Observational cohort study was done at the Cardiology and Radiology department and cardiac surgery department, Al-Azhar university hospitals and Helwan University hospital. The study involved 700 cases who patients diagnosed with Acute Myocardial Infarction and fulfilled specific criteria for selection. Result: There was highly statistically significant relation between Myocardial infarction size and ECG Marker Score as mean infarct size elevated When the number of ECG markers increased. There was a highly statistically significant relation between myocardial infarct segments, myocardial infarction size and improvement of cardiac function 6 months post-CABG. Conclusion: The study found that larger myocardial infarctions corresponded with higher DETERMINE Scores. It concluded that an ECG-based score better estimates infarct size than LVEF alone. Additionally, there was a significant statistical correlation between the size and segmentation of myocardial infarction and better cardiac function six months after CABG.

Emotion Detection Using ECG Signals and a Lightweight CNN Model

Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction(HCI).However,physical methods of emotion recognition such as facial expressions,voice,and text data,do not always indicate true emotions,as users can falsify them.Among the physiological methods of emotion detection,Electrocardiogram(ECG)is a reliable and efficient way of detecting emotions.ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments.Researchers use deep machine learning techniques for emotion recognition using ECG signals,but there is a need to develop efficient models by tuning the hyperparameters.Furthermore,most researchers focus on detecting emotions in individual settings,but there is a need to extend this research to group settings aswell since most of the emotions are experienced in groups.In this study,we have developed a novel lightweight one dimensional(1D)Convolutional Neural Network(CNN)model by reducing the number of convolution,max pooling,and classification layers.This optimization has led to more efficient emotion classification using ECG.We tested the proposed model’s performance using ECG data from the AMIGOS(A Dataset for Affect,Personality and Mood Research on Individuals andGroups)dataset for both individual and group settings.The results showed that themodel achieved an accuracy of 82.21%and 85.62%for valence and arousal classification,respectively,in individual settings.In group settings,the accuracy was even higher,at 99.56%and 99.68%for valence and arousal classification,respectively.By reducing the number of layers,the lightweight CNNmodel can process data more quickly and with less complexity in the hardware,making it suitable for the implementation on the mobile phone devices to detect emotions with improved accuracy and speed.

Deep Learning-Based ECG Classification for Arterial Fibrillation Detection

The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.

以BP神经网络为工具的短时ECG信号情感分类

针对目前生理信号情感识别领域采用的生理信号种类太多或使用的生信号长度较长的问题,本文使用BP神经网络对单一、短时ECG信号进行情感识别分类,并对识别时间进行了估计。通过诱发被试喜、怒、哀、惧和平静5种基本情感状态,采集到ECG生理信号,处理后利用神经网络建立模型。实验结果表明,本文方法得到的情感分类的平均识别率为89.14%,且生理信号进行特征提取和识别分类的时间总和小于0.15s,有效地降低了对生理信号种类和窗口长度的依赖。

基于卷积神经网络的ECG心律失常分类研究

基于心电信号进行心律失常自动检测和分类识别研究,辅助临床医生进行心血管相关疾病诊断。采用MIT-BIH数据库作为数据源,对该数据库心电数据进行小波分解与重构去噪后,构建卷积神经网络模型,结合Adam优化器,并优化丢弃值、训练步数和批大小三个超参数来优化模型,使用准确率、灵敏性和正预测率三个指标评价模型性能。实验结果表明,模型实现心律失常五分类的整体准确率大于99%,与现有模型性能相比,准确率提升1.2%。

全面解读IA ECG广色域测试版ICC文件

七色分色技术已发展多年,欧美印刷机构和协会相继投人研发和制定更新相关标准体系,当前色彩校准技术手段等条件正走向成熟,本文对IAECG广色域测试版ICC文件进行全面解读。

缓慢型心房颤动(Af)伴发长R-R间期在静态心电图(ECG)中发生率及意义

探讨ECG(静态心电图)在缓慢型Af(心房颤动)伴发长R-R间期中的应用意义。方法 截选2021年03月至2022年03月54例ECG提示缓慢型心房颤动患者,按照有无伴随相关症状(头晕、黑朦、晕厥等),分为甲组33例(有相关症状)和乙组21例(无相关症状);按照年龄,分为老年组30例(≥80岁)和非老年组24例(<80岁);比较各组伴发长R-R间期发生率。结果 本试验中,甲、乙组伴发长R-R间期发生率差异明显,甲组发生率显著更高(P<0.05)。老年组、非老年组伴发长R-R间期发生率差异明显,老年组发生率显著更高(P<0.05)。结论 缓慢型心房颤动行静态心电图检查,能够准确诊断患者有无伴发长R-R间期;针对老年患者和伴随头晕、黑朦、晕厥等症状患者,应加强其静态心电图检查,以便更好诊断、鉴别其病情,促使患者尽早接受专业治疗和干预,保证其生命安全与预后质量。

基于卷积神经网络与ECG信息的多模态疲劳驾驶检测研究

为解决驾驶员疲劳驾驶引发的交通事故问题,本研究致力于设计一款高精度、及时预警的疲劳驾驶检测与预警装置。文章提出了一种基于卷积神经网络与ECG信息的多模态疲劳驾驶检测方法:首先,通过训练数据集获取模型文件,并将其与预设行为进行对比,得出预警结果;接着,结合ECG信号对驾驶员的驾驶状态进行进一步分析,输出最终结果并触发预警。实验结果表明,该方法能够准确识别驾驶员的疲劳状态并及时发出预警,最高检测正确率达到了99%,验证了方法的可行性。

基于LSTM网络与ECG信号的青少年运动强度识别方法

适当的体育运动有利于青少年身体健康,但是大多数青少年在运动过程中,盲目地进行高强度的体育锻炼,很容易造成身体的损伤甚至危及生命。因此,为了实现对青少年运动的合理安排和监测,本研究提出了一种基于长短期记忆人工神经网络(Long Short-Term Memory,LSTM)与心电图(Electrocardiogram,ECG)信号的青少年运动强度识别方法。该方法可以在体育锻炼中实时监测运动强度,防止体育运动中不合理锻炼带来的危险。本研究算法采用多层的LSTM网络提取运动过程中的ECG信号特征,在网络中加入注意力机制,模仿生物的视觉注意力行为,对一段时间序列中的不同区域区别对待,重点关注特征区域,抑制无用信息,进一步提升监测效率和准确率。实验识别准确率可达99.40%,表明所提方法所构建的青少年运动强度诊断模型具有较高的诊断精度,且具有较强的泛化能力。

基于VMD和平滑滤波的ECG去噪方法

针对目前变分模态分解法在心电信号降噪时存在模态分量难以取舍的问题,提出了一种改进的变分模态分解方法。首先对含噪心电信号进行变分模态分解,通过各模态分量的中心频率和模态分量与原始心电信号的互相关来确定噪声占优的模态分量与信号占优的模态分量。然后选取中心频率处于医学心跳频率范围的模态分量来提取心跳频率对应的采样点数,根据心跳频率对噪声占优的模态分量和信号占优的模态分量分别进行平滑滤波。最后使用处理过的模态分量重构心电信号,完成基线漂移和肌电噪声的去除。实验结果表明该方法的去噪效果优于小波阈值法、变分模态分解法及两者相结合的方法。

基于生成对抗网络的PPG⁃ECG信号转换方法

心电(Electrocardiogram,ECG)信号的长期检测与评估对心血管疾病的诊断和预防至关重要。心电信号的检测通常需要在患者身上安装电极,易使受试者产生不适感,适用范围有限。相对而言,使用光电容积描记法(Photoplethysmography,PPG)检测得到的脉搏波(Pulse wave)信号不仅包含丰富的心血管生理和病理信息,而且易于测量。考虑到PPG与ECG信号间存在固有的映射关系,本文基于生成对抗网络(Generative adversarial network,GAN)提出了一种将PPG转换为ECG信号的模型。该模型生成器由Unet模型组成,并且在特征图融合方面参考了Unet++的结构,而其判别器由卷积神经网络组成。在训练过程中,采用梯度惩罚方式增加了生成模型的稳定性。基于公用数据集进行了实验,通过对比53名受试者样本的处理结果,新模型所生成ECG信号的均方根误差(Root mean square error,RMSE)、Pearson相关系数(ρ)和Fréchet距离(Fréchet distance,FD)三个指标分别提升了3.4%、5.5%和0.4%,证明新模型具有更好的PPG⁃ECG转换效果。