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.

Influence of intra-cavity loss on transmission characteristics of fiber Bragg grating Fabry–Perot cavity

A theoretical model of the fiber Bragg grating Fabry–Perot（FBG-FP） transmission spectrum considering loss of FBG and intra-cavity fiber is presented. Several types of FBG-FPs are inscribed experimentally, and their spectra are measured.The results confirm that weak intra-cavity loss is enhanced at the resonance transmission peak, that is, loss of transmission peaks is observably larger than other wavelengths. For FBG-FPs with multi resonance peaks, when the resonance peak wavelength is closer to the Bragg wavelength, the more significant loss effect of resonance transmission peak is exhibited.The measured spectra are fitted with the presented theoretical model. The fitted coefficient of determinations are near 1,which proves the validity of the theoretical model. This study can be applied to measure FBG loss more accurately, without a reference light. It can play an important role in FBG and FBG-FP writing process optimization and application parameter optimization.

Unfamiliar waveforms spanning from the ST to TP segments only observed in certain limb leads of the standard 12-lead electrocardiogram due to Aslanger’ s sign

Aslanger’s sign,also known as the arterial pulse tapping artifact or electromechanical association artifact,is an electrocardiographic artifact caused by arterial pulsation at the site where the limb leads of the standard 12-lead electrocardiogram near the radial or posterior tibial arteries are positioned,particularly in hyperdynamic states.[1–8]It occurs in every cardiac cycle with a constant coupling interval between the QRS complex and artifact.This synchronization with the underlying heart rhythm makes it less likely to be recognized as an artifact compared to unsynchronized artifacts,such as those caused by limb movement and inadequate contact between the electrode and skin.[1,2,7,8]Almost all reported cases of Aslanger’s sign exhibit an unusual waveform morphology in all 12 leads except one of the standard 12-lead electrocardiogram.This sign is often confused with an electrocardiographic finding commonly observed during acute coronary events.

An intra-cavity absorption fiber gas sensor based on performanceenhanced techniques

The performance of an erbium-doped fiber ring laser based intra-cavity absorption gassensor was evaluated with performance enhanced techniques. A multi-line wavelength sweep techniqueand a weighted averaging technique were proposed for better gas detection. By selecting appropriatesystem parameters, 6 strong absorption lines near 1 530 nm of C2H2 were obtained with good spectrumresolution in one scanning period. One group with higher absorption coefficients was used for relativelylow gas concentration detection and the other with lower absorption coefficients was used for relativelyhigh gas concentration. Both the groups can be used for medium gas concentration detection. For variousconcentration cases, by choosing proper absorption lines and performing weighted averaging, detectionaccuracy can be obtained over an extended detection range. The minimum detection limit could be verylow after optimization.

Intelligent Electrocardiogram Analysis in Medicine:Data,Methods,and Applications

Electrocardiogram(ECG)is a low-cost,simple,fast,and non-invasive test.It can reflect the heart’s electrical activity and provide valuable diagnostic clues about the health of the entire body.Therefore,ECG has been widely used in various biomedical applications such as arrhythmia detection,disease-specific detection,mortality prediction,and biometric recognition.In recent years,ECG-related studies have been carried out using a variety of publicly available datasets,with many differences in the datasets used,data preprocessing methods,targeted challenges,and modeling and analysis techniques.Here we systematically summarize and analyze the ECGbased automatic analysis methods and applications.Specifically,we first reviewed 22 commonly used ECG public datasets and provided an overview of data preprocessing processes.Then we described some of the most widely used applications of ECG signals and analyzed the advanced methods involved in these applications.Finally,we elucidated some of the challenges in ECG analysis and provided suggestions for further research.

Dipper Throated Algorithm for Feature Selection and Classification in Electrocardiogram

Arrhythmia has been classified using a variety of methods.Because of the dynamic nature of electrocardiogram(ECG)data,traditional handcrafted approaches are difficult to execute,making the machine learning(ML)solutions more appealing.Patients with cardiac arrhythmias can benefit from competent monitoring to save their lives.Cardiac arrhythmia classification and prediction have greatly improved in recent years.Arrhythmias are a category of conditions in which the heart's electrical activity is abnormally rapid or sluggish.Every year,it is one of the main reasons of mortality for both men and women,worldwide.For the classification of arrhythmias,this work proposes a novel technique based on optimized feature selection and optimized K-nearest neighbors(KNN)classifier.The proposed method makes advantage of the UCI repository,which has a 279-attribute high-dimensional cardiac arrhythmia dataset.The proposed approach is based on dividing cardiac arrhythmia patients into 16 groups based on the electrocardiography dataset’s features.The purpose is to design an efficient intelligent system employing the dipper throated optimization method to categorize cardiac arrhythmia patients.This method of comprehensive arrhythmia classification outperforms earlier methods presented in the literature.The achieved classification accuracy using the proposed approach is 99.8%.

Optimization of Electrocardiogram Classification Using Dipper Throated Algorithm and Differential Evolution

Electrocardiogram(ECG)signal is a measure of the heart’s electrical activity.Recently,ECG detection and classification have benefited from the use of computer-aided systems by cardiologists.The goal of this paper is to improve the accuracy of ECG classification by combining the Dipper Throated Optimization(DTO)and Differential Evolution Algorithm(DEA)into a unified algorithm to optimize the hyperparameters of neural network(NN)for boosting the ECG classification accuracy.In addition,we proposed a new feature selection method for selecting the significant feature that can improve the overall performance.To prove the superiority of the proposed approach,several experimentswere conducted to compare the results achieved by the proposed approach and other competing approaches.Moreover,statistical analysis is performed to study the significance and stability of the proposed approach using Wilcoxon and ANOVA tests.Experimental results confirmed the superiority and effectiveness of the proposed approach.The classification accuracy achieved by the proposed approach is(99.98%).

一种改进U-Net网络的心电图分类算法研究

基于CPSC-2018十二导联数据,提出了一种U-Net网络和注意力机制结合的心电图分类算法。首先,针对数据集数据长度长短不一的问题,对数据进行等长处理和归一化处理。然后,利用U-Net网络中跳层连接和编码解码方式,对预处理后较长的数据进行处理。在U-Net网络解码的最后一层加入注意力机制对抗噪声,提升模型的有效信息关注度和准确性。最后,利用CPSC-2018数据集进行验证。实验结果表明:所提模型能够取得较好的分类效果,识别房颤(AF)和右束支传导阻滞(RBBB)心律失常的精准率、召回率、F1值都可以达到90%以上,平均F1值可以达到82.5%。

CR导联心电图的原理及临床应用

目的探讨CR导联心电图的原理及临床应用。方法CR导联系统是有共同负极的双极胸导联系统,其共同负极是右上肢(R),正极是胸部某点(C),多选V导联各点。获取CR导联心电图的方法有两种:一是利用常规心电图机实采,四个肢体电极全接右上肢,胸导联电极正常连结;二是基于常规心电图数据通过导联转换获取,首先调取aVR、V_(1)-V_(6)、V_(3R)-V_(5R)、V_(7)-V_(9)等导联的原始数据,再利用固定的转换系数CR-V_(i)=V_(i)-2/3 aVR计算目标导联(使用Python语言在Pycharm编译环境下实现算法),最终转换完成后画图。结果实采法与转换法获取了相同的CR导联心电图,但与相应的Wilson导联心电图相比,左胸导联的CR导联心电图形态类似而波幅略大,右胸导联CR导联心电图与Wilson导联心电图波幅相近但形态截然不同:前者P波清晰,QRS波群主波向上、无宽深的Q波,T波直立;后者P波较低,QRS波群主波向下,可出现非梗死性宽深Q波及非缺血性T波倒置,即所谓的右心室盲区。结论CR导联心电图开放了右心室盲区,实现了对左、右心室的平等探测,为临床提供了更有价值的心电学形态信息,弥补了Wilson胸导联系统的不足,值得推广普及。

基于多尺度卷积和自注意力特征融合的多模态情感识别方法

基于生理信号的情感识别受噪声等因素影响,存在准确率低和跨个体泛化能力弱的问题。对此,提出一种基于脑电(EEG)、心电(ECG)和眼动信号的多模态情感识别方法。首先,对生理信号进行多尺度卷积,获取更高维度的信号特征并减少参数量;其次,在多模态信号特征的融合中使用自注意力机制,以提升关键特征的权重并减少模态之间的特征干扰;最后,使用双向长短期记忆(Bi-LSTM)网络提取融合特征的时序信息并进行分类。实验结果表明,所提方法在效价、唤醒度和效价/唤醒度四分类任务上分别取得90.29%、91.38%和83.53%的识别准确率,相较于脑电单模态和脑电/心电双模态方法,准确率上提升了3.46~7.11和0.92~3.15个百分点。所提方法能够准确识别情感,在个体间的识别稳定性更好。

基于慕课的混合式教学模式在心电图教学中的应用

目的探索基于慕课的混合式教学模式在心电图教学中的应用效果。方法选择2021年3—7月暨南大学第一临床医学院2018级临床医学专业本科生为研究对象,共91名。在诊断学心电图章节的教学中,采用以心电图慕课为基础的线上自学和线下面授课相结合的混合式教学模式,分析期末心电图理论和阅图考试成绩和问卷结果。结果理论和阅图成绩分别为(21.00±3.89)分和(19.33±4.63)分。理论考试中,一些心电图基础知识的准确率低于50%,仅有50%的学生能识别心室颤动。阅图考试结果显示,大部分图形节律、心率等基本参数的准确率较高,可达90%以上,而心电图结论的准确率均较低。超过75%的学生认为心电图慕课对学习心电图有很大帮助,阅图训练的15个问题可以帮助理清心电图分析思路;83.15%的学生认为基本掌握了心电图分析要点,8.99%的学生是熟练掌握。结论基于慕课的混合式教学模式对学生掌握心电图分析要点很有帮助,学生对心电图基本参数掌握较好,对于异常心电图特征的记忆和分析有待加强。

病案为载体的心电图教学与传统心电图教学效果的比较

目的探讨适合护理实习生的心电图教学法,提高学生对临床心电图的识别和护理实践能力。方法选取2021年9月至2022年12月江西省胸科医院心内科的160名护理实习生作为研究对象,按照随机数字表法分为研究组(n=80)与对照组(n=80),研究组采用临床心电图案例教学方法,对照组采用传统心电图教学方法,比较两组护理实习生的心电图基础理论评分、心电图测试评分,心电监护考核评分以及心电图护理教学满意度。结果研究组的心电图基础理论评分、心电图测试评分均高于对照组,差异有统计学意义(P<0.05)。研究组的心电监护考核评分、心电图护理教学效果满意度高于对照组,差异有统计学意义(P<0.05)。结论相较于传统心电图教学,病案为载体的心电图教学法更具优势,能切实有效增强护理实习生心电图理论及实践能力,提高护理实习生对心电图护理教学的满意度,从而提升教学质量。

基于CNN‑LSTM‑SE的心电图分类算法研究

心血管疾病是我国死亡率较高的疾病之一,通过观察心电图来判断心电信号是否出现异常能够对心血管疾病进行预防和筛查。由于心电图数据规模大且繁杂,临床医护人员在心电图筛查时,工作负担大且容易出现误诊或漏诊的情况。为了提高心电图的筛查效率、减少医护人员的压力,提出了一种基于卷积神经网络、长短期记忆神经网络和SE网络的心电图分类算法模型(CNN-LSTM-SE),该模型将心电图分成5种不同的类别。主要研究内容包括:选用MIT-BIH心律失常数据集作为心电信号的数据来源,使用巴特沃斯带通滤波器对心电信号进行去噪处理,通过Z-score方法对心电信号进行标准化处理,利用独热编码方法对心电信号标签进行编码,最后使用处理后的心电数据对所提算法模型进行训练和测试。实验结果表明:所提模型相较于其它模型,能够有效提高心电图分类的准确性,在实验数据集上的分类准确率达到99.1%。

无导线起搏器联合PCI术治疗急性心肌梗死合并心律失常的疗效及对心电图、不良心血管事件的影响

目的:观察无导线起搏器联合经皮冠状动脉介入(PCI)术治疗急性心肌梗死(AMI)合并心律失常的疗效及对心电图参数、不良心血管事件的影响。方法:选取2020年6月—2021年6月我院收治的接受无导线起搏器联合PCI术治疗的80例AMI合并心律失常病人,统计无导线起搏器置入情况、PCI术手术效果、心功能、心肌灌注分级、起搏器电学参数、心电图参数、再灌注损伤与不良心血管事件。结果:介入治疗后全部病人均符合冠状动脉再通标准。80例均成功置入MicraTM无导线起搏器,起搏成功率为100%。治疗后左室舒张末期内径(LVEDD)、左室收缩末期内径(LVESD)、阈值、阻抗、QT间期均小于治疗前(P<0.05);左室射血分数(LVEF)、心排血量(CO)、感知、PR间期、QRS间期均大于治疗前(P<0.05);心肌组织灌注(TIMI)分级情况均优于治疗前(P<0.05),TIMI分级Ⅱ级以上的比例82.50%。80例中16例发生再灌注损伤,但无因再灌注损伤发生死亡者。随访6个月18例发生不良心血管事件。结论:无导线起搏器联合PCI术可改善AMI合并心律失常病人的心功能、心肌灌注分级、起搏器电学参数、心电图参数,冠状动脉再通率高、起搏成功率高,但可能发生再灌注损伤与不良心血管事件。

中国高水平运动员心电图异常分析

目的:通过对中国高水平运动员进行心电图检查并诊断,评估性别、运动年限和运动类型对运动员心电图改变的影响。方法:对2016年至2019年进行心电图检查的891名中国高水平运动员的心电图进行检测。根据2010年欧洲心脏病学会指南(ESC)将心电图分为:组0(正常心电图)、组1(常见与训练相关心电图)、组2(不常见与训练不相关心电图)。分析运动员的性别、训练年限(4~6年、7~10年和≥11年)和运动类型(技巧类、力量类、耐力类、混合类)对各组心电图改变的影响。结果:(1)不常见与训练不相关的心电图改变的发生率在不同性别、不同训练年限和不同训练类型的运动员间均不存在显著性差异。(2)常见与训练相关的心电图改变在男运动员中发生率显著高于女运动员(58.9%vs 47.4%);在训练超过11年运动员中发生率最高,与训练4~6年相比具有显著性差异(P<0.05);在不同运动类型间具有显著差异(P<0.01),在混合类中发生率最高,为64.6%;窦性心动过缓在混合类和耐力类运动中的发生率显著高于其他运动类型(P<0.05)。结论:中国高水平运动员与训练相关的心电图改变与性别、训练年限和运动类型均有关。

益气活血汤治疗不稳定型心绞痛老年患者的疗效及对其血流动力学和心电图的影响

目的探究益气活血汤对不稳定型心绞痛(Unstable angina pectoris,UAP)老年患者的疗效及对其血流动力学和心电图的影响。方法选取2019年5月-2022年6月期间大连市友谊医院老年医学中心老年病科收治的92例UAP老年患者,按随机数字表法分为对照组和观察组,每组各46例。对照组给予常规治疗,观察组在对照组基础上给予益气活血汤治疗,均持续治疗4周。观察比较两组患者临床疗效、心电图改善情况,治疗前后血脂[总胆固醇(Total cholesterol,TC)、甘油三酯(Triglyceride,TG)、高密度脂蛋白胆固醇(High density lipoprotein cholesterol,HDL-C)、低密度脂蛋白胆固醇(Low density lipoprotein cholesterol,LDL-C)]水平、血流动力学(全血还原黏度、纤维蛋白原、红细胞聚集指数、血浆黏度)指标及不良反应发生情况。结果治疗后观察组临床总有效率91.30%(42/46)明显高于对照组73.91%(34/46),差异有统计学意义(P<0.05)。治疗后两组患者血脂TC、TG、LDL-C水平均较治疗前降低,HDL-C水平较治疗前升高,差异有统计学意义(P<0.05);且观察组TC、TG、LDL-C水平均明显低于对照组,HDL-C水平明显高于对照组,差异有统计学意义(P<0.05)。治疗后两组患者全血还原黏度、红细胞聚集指数、纤维蛋白原、血浆黏度均较治疗前降低,差异有统计学意义(P<0.05);且观察组血流动力学指标明显低于对照组,差异有统计学意义(P<0.05)。治疗后观察组心电图改善总有效率86.96%(40/46)明显高于对照组67.39%(31/46),差异有统计学意义(P<0.05)。治疗期间,两组患者不良反应发生率比较,差异无统计学意义(P>0.05)。结论益气活血汤治疗老年UAP的疗效显著,可有效改善患者血流动力学指标和心电图情况。

基于GAN和MS-ResNet的房颤自动检测模型

房颤是一种常见的心律失常疾病,针对现有研究工作大多依赖于单尺度信号段而忽略了不同尺度下潜在的互补信息和数据不平衡问题导致诊断性能下降的问题,提出了一种新颖的基于生成对抗网络(generative adversarial network, GAN)和多尺度残差网络(multiscale residual net, MS-ResNet)的房颤自动检测模型,该网络使用GAN合成具有高形态相似性的单导联心电数据来解决数据的隐私和不平衡问题。同时,设计了MS-ResNet特征提取策略,从不同尺度提取不同大小信号段的特征,从而有效地捕捉P波消失和RR间期不规则特征。该模型联合这两种策略不仅为房颤自动检测生成高质量心电图(electrocardiogram,ECG)数据,还可以利用多尺度网格提取不同波之间的时序特征。在PhysioNet Challenge2017公开ECG数据集上以及平衡后的数据集上评估了MS-ResNet的性能,并将其与现有的房颤分类模型进行了比较。实验结果表明,MS-ResNet在平衡后的数据集上平均F1值和精确率分别达到0.914 1和91.56%,与不平衡数据集相比,F1提高了4.5%,精确率提高了3.5%。

浅谈住院医师规范化培训的心电图实践教学

住院医师规范化培训(简称住培)是目前较受认可的培养合格临床医师的方式,而心电图作为一项重要的临床常规检查,是临床医生必须掌握的基本技能之一,也是住培中要求掌握的重要内容。昆明医科大学第二附属医院心功能科在不断完善和改进中摸索出了一套更适合住培学员的教学方式,利用多元化的教学形式增强学员学习兴趣,保证学习质量,从而使其更好地将所学知识和技能应用于临床。

使用异质集成学习和心电信号异构特征融合的睡眠呼吸暂停分类方法

睡眠呼吸暂停(SA)会影响睡眠质量,增加心脑血管疾病风险,其准确分类有助于在SA早期阶段及时开展针对性治疗。本文提出一种使用异质集成学习和异构特征融合的SA分类新方法。首先从原始心电信号中提取小波时频谱,使用SE-ResNet作为初级分类器;然后提取RR间期序列和R峰值序列,使用1D CNN-LSTM作为初级分类器;再提取心率变异性特征,使用SVM作为初级分类器。最后采用堆叠法作为异质集成学习的融合策略,再使用另一个SVM作为次级分类器实现SA分类。在Apnea-ECG数据集上进行实验,所提出的SA分类方法的准确率为89.12%。实验结果表明,所提方法有效利用了各初级分类器的多样性和异构特征的互补性,其性能优于传统的SA分类方法。

心电图线上线下一体化教学模式探索与实践

探讨线上线下一体化教学模式在心电图教学中的实践应用和效果评价。将400名学生分为试验组和对照组,试验组采用线上线下一体化教学模式进行教学,对照组采用传统教学模式进行教学。课程结束后,对两组考核成绩和课程评价分数进行分析。结果显示,线上线下一体化教学模式能够提高学生分析和诊断心电图的能力,获得了多维度多层次的肯定评价。