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.

A wearable real-time telemonitoring electrocardiogram device compared with traditional Holter monitoring

Arrhythmias are very common in the healthy populations as well as patients with cardiovascular diseases.Among them,atrial fibrillation(AF)and malignant ventricular arrhythmias are usually associated with some clinical events.Early diagnosis of arrhythmias,particularly AF and ventricular arrhythmias,is very important for the treatment and prognosis of patients.Holter is a gold standard commonly recommended for noninvasive detection of paroxysmal arrhythmia.However,it has some shortcomings such as fixed detection timings,delayed report and inability of remote real-time detection.To deal with such problems,we designed and applied a new wearable 72-hour triple-lead H3-electrocardiogram(ECG)device with a remote cloud-based ECG platform and an expertsupporting system.In this study,31 patients were recruited and 24-hour synchronous ECG data by H3-ECG and Holter were recorded.In the H3-ECG group,ECG signals were transmitted using remote real-time modes,and confirmed reports were made by doctors in the remote expert-supporting system,while the traditional modes and detection systems were used in the Holter group.The results showed no significant differences between the two groups in 24-hour total heart rate(HR),averaged HR,maximum HR,minimum HR,premature atrial complexes(PACs)and premature ventricular complexes(PVCs)(P>0.05).The sensitivity and specificity of capture and remote automatic cardiac events detection of PACs,PVCs,and AF by H3-ECG were 93%and 99%,98%and 99%,94%and 98%,respectively.Therefore,the long-term limb triple-lead H3-ECG device can be utilized for domiciliary ECG self-monitoring and remote management of patients with common arrhythmia under medical supervision.

The future of remote ECG monitoring systems

监视系统的遥远的 ECG 为遥远的心脏监测器正在成为平凡医药设备。在最近的年里，监视系统的遥远的 ECG 在监视心疾病的各种各样的类型被使用了，并且 ECG 的传播和接收的质量在保留的遥远的过程期间发信号进展。然而，在那里留下伴随挑战。这份报告集中于监视系统的遥远的 ECG 的三个部件：病人(终端用户)，医生工作站，和遥远的服务器，在便携系统，在遥远的传播的包损失，便携式的 ECG 监视系统，耐心的 ECG 数据收集系统，和 ECG 上考察并且评估逼近的挑战包括即时处理表明传播圣片断， R 波浪， RR 间隔和 QRS 飘动，等等。这篇论文试着澄清未来 ECG 的发展策略遥远的监视，它能在指导监视的遥远的 ECG 的研究和发展是有用的。

An ECG Monitoring and Alarming System Based On Android Smart Phone

ECG monitoring in daily life is an important means of treating heart disease. To make it easier for the medical to monitor the ECG of their patients outside the hospital, we designed and developed an ECG monitoring and alarming system based on Android smart phone. In our system, an ECG device collects the ECG signal and transmits it to an Android phone. The Android phone detects alarms which come from the ECG devices. When alarms occur, Android phone will capture the ECG images and the details about the alarms, and sends them to the cloud Alarm Server (AS). Once received, AS push the messages to doctors’ phone, so the doctors could see the ECG images and alarm details on their mobile phone. In our system, high resolution ECG pictures are transmitted to doctors’ phone in a user-friendly way, which can help doctors keep track of their patient’s condition easily.

Evaluation of Pencil Lead Based Electrodes for Electrocardiogram Monitoring in Hot Spring

Electrocardiogram(ECG)electrodes are conductive pads applied to the skin to measure cardiac activity.Ag/AgCl electrodes are the commercial product which widely used to obtain ECGs.When monitoring the ECG in a hot spring,Ag/AgCl electrodes must be waterproofed;however,this is time-consuming,and the adhesive may tear the skin on removal.For solving the problem,we developed the carbon pencil lead(CPL)electrodes for use in hot springs.Both CPL and Ag/AgCl electrodes were connected to ECG100C’s cables.The Performance was evaluated in three conditions as following:hot spring water with and without bubble,and in cold water.In each environment,the procedure was followed by three different protocols that are recording from the dry condition,hot spring water immersion with and without movement,post hot spring water condition.Under dry and wet conditions,both electrodes can obtain the waveform of the ECG signal in which all PQRST waves were identifiable.Nevertheless,the signal quality of both types of electrodes was different in water immersion with and without movement.The overall morphology obtained by Ag/AgCl electrodes was unstable higher than that of CPL electrodes in immersion without movement condition.The CPL electrodes provided better ECG waveform quality compared to Ag/AgCl electrodes in which the ECG signal had high waveforms distortion in water immersion with movement condition.

A ECG Tele-monitoring Method and System Based on Embedded Web Server

This paper describes the development of a new ECG tele-monitoring method and system based on the embedded web server. The system consists of ECG recorders with network interface and the embedded web server, internet networks and computers, with the system operating on browser/server(B/S) mode. The ECG recorder was designed by ARM9 (S3C2410X) and embedded operating system (Linux). Once the ECG recorder has been connected to the internet network, medical experts can use the internet to access the server of the ECG recorder, monitor ECG signals, and diagnose patients by browsing the dynamic web pages in the embedded web server. The experimental results reveal that the designed system is stable, reliable, and suitable for the use in real-time ECG tele-monitoring for both family and community health care.

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。

Arrhythmia Prediction on Optimal Features Obtained from the ECG as Images

A critical component of dealing with heart disease is real-time identifi-cation,which triggers rapid action.The main challenge of real-time identification is illustrated here by the rare occurrence of cardiac arrhythmias.Recent contribu-tions to cardiac arrhythmia prediction using supervised learning approaches gen-erally involve the use of demographic features(electronic health records),signal features(electrocardiogram features as signals),and temporal features.Since the signal of the electrical activity of the heartbeat is very sensitive to differences between high and low heartbeats,it is possible to detect some of the irregularities in the early stages of arrhythmia.This paper describes the training of supervised learning using features obtained from electrocardiogram(ECG)image to correct the limitations of arrhythmia prediction by using demographic and electrocardio-graphic signal features.An experimental study demonstrates the usefulness of the proposed Arrhythmia Prediction by Supervised Learning(APSL)method,whose features are obtained from the image formats of the electrocardiograms used as input.

Monitoring Heart Rate Variability Based on Self-powered ECG Sensor Tag

This paper proposes a batteryless sensing and computational device to collect and process electrocardiography(ECG)signals for monitoring heart rate variability(HRV).The proposed system comprises of a passive UHF radio frequency identification(RFID)tag,an extreme low power microcontroller,a low-power ECG circuit,and a radio frequency(RF)energy harvester.The microcontroller and ECG circuits consume less power of only~30μA and~3 mA,respectively.Therefore,the proposed RF harvester operating at frequency band of 902 MHz~928 MHz can sufficiently collect available energy from the RFID reader to supply power to the system within a maximum distance of~2 m.To extract R-peak of the ECG signal,a robust algorithm that consumes less time processing is also developed.The information of R-peaks is stored into an Electronic Product Code(EPC)Class 1st Generation 1st compliant ID of the tag and read by the reader.This reader is functioned to collected the R-peak data with sampling rate of 100ms;therefore,the user application can monitor fully range of HRV.The performance of the proposed system shows that this study can provide a good solution in paving the way to new classes of healthcare applications.

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。

结合卷积神经网络与注意力机制的多域特征融合ECG心率失常分类

心率失常是心血管疾病诊断的重要手段,其自动分类具有重要的临床意义。为了提高心率失常分类的准确性,结合一维卷积神经网络(Convolutional Neural Networks,CNN)和注意力机制(Attention)提出了一种CNN+Attention的深度学习模型,使用CNN提取心电信号的一维时域特征。针对一维时序心电信号时域特征表征能力有限的问题,使用短时傅里叶变换(Short-Time Fourier transform,STFT)将心电信号变换到时频域,通过Attention提取心电信号的时频域全局相关依赖关系,将时域与时频域特征融合对5种类型心电信号进行分类。在MIT-BIH数据集上验证了模型的有效性,所提模型对5种类型心电信号的平均分类准确率、精准率、召回率、灵敏度以及F1_Score分别为99.72%、98.55%、99.46%、99.90%以及99.00%。与已有先进方法对比,验证了所提模型具有先进的性能表现。

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%。

成年病人经下肢行PICC的研究进展

就成年病人经下肢对外周静脉穿刺中心静脉置管(PICC)适应证、禁忌证、应用区域选择法(ZIM)技术优化置管穿刺部位,应用隧道、超声下不同平面穿刺技术以及心电图(ECG)定位下腔静脉系统内导管尖端位置等进行综述,以期为临床实践提供参考。

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。

基于单导联ECG监护系统的嵌入式技术课程案例设计

针对嵌入式技术课程中理论抽象和设备技术深度不足的问题,文章设计出单导联ECG监护系统的案例,致力于提升学生在嵌入式领域的学习体验。课堂教学采用案例展示、教师分析、学生分组设计仿真和小组答辩等形式,以提高学生的参与度。研究结果表明,以嵌入式技术在单导联心电监护系统中的应用为背景,能够有效地激发学生的学习兴趣,加深学生对系统概念的理解,提高学生在心电信号分析设计方面的综合能力,实现理论与实践的有机结合。

多功能监护仪操作培训步骤与临床实际操作应用的现状调查分析

目的调查分析护士多功能监护仪操作培训步骤及临床实际操作应用的现状。方法采取方便抽样方法,对2020年9月来自四川、广西、重庆、北京、福建、云南、贵州、河南、江苏、黑龙江等全国20个省(自治区、直辖市)100余家二乙以上医院,参加中华护理学会重症专科护士培训的261名ICU护理人员进行问卷调查。共发放问卷261份,收回有效问卷261份,问卷有效回收率为100.0%。调查护理人员接受监护仪操作培训步骤及临床实际操作步骤情况及护理人员非抢救状态下单人操作心电监护(ECG)、无创血压(BP)监测、血氧饱和度监测(SPO2)耗时情况。结果65.5%的护理人员接受培训的首要步骤是进行ECG监测,24.9%接受培训的首要步骤是进行SPO2监测,9.6%的护理人员接受培训的首要步骤是进行无创BP监测;35.2%的护理人员接受培训步骤的顺序与实际操作步骤不一致;49.4%的护理人员在实际临床操作中是先进行SPO2监测而非ECG监测,且认为不管是培训还是实际操作应先进行SPO2监测。非抢救状态下单人操作ECG监测、无创BP监测、SPO2监测耗时分布比较差异有统计学意义(P<0.05);SPO2监测耗时<20s占比高于与ECG监测、无创BP监测,耗时20~40、41~60、61~80s占比低于ECG监测、无创BP监测,差异有统计学意义(P<0.05)。结论多功能监护仪操作培训主要步骤的先后顺序未规范,培训步骤顺序与临床实际操作存在脱节现象,建议监护仪操作培训流程应先进行SPO2监测,其次是ECG与无创BP监测。

老年患者心电监护拔牙的全流程规范化管理

目的分析全流程规范化管理在口腔外科门诊老年心电监护拔牙管理中的应用效果。方法研究阶段为2023年1月至10月,共纳入研究对象360例,均为口腔外科门诊心电监护拔牙就诊患者,采用随机抽样法进行分组,对照组采用心电监护拔牙管理模式,观察组采取全流程规范化管理模式,比较两组管理效果。结果观察组心率、血压波动、焦虑值均低于对照组,两组比较有显著差异(P<0.05),患者满意率99.7%高于对照组85.7%,差异有统计学意义(P<0.05)。结论通过实施规范化全流程管理,有效优化了老年患者心电监护拔牙的流程,缩短了心电监护拔牙预约时间,进而有助于改善老年患者拔牙预后、提升患者就医体验。

心电监护仪虚拟仿真系统的设计与开发

针对医护类专业实践教学中存在的难题,基于虚拟现实技术开发了心电监护仪虚拟仿真系统,系统包含设备认识、仿真操作和专项学习三个模块。文章描述了软件系统结构和各模块的设计目标、需求与功能,同时,对场景渲染资源控制、多摄影机设置与使用、交互触发与UI的实现、动画的实现、操作进程与逻辑控制等虚拟仿真的关键技术进行研究和特色化设计。系统运行稳定,渲染效果好,可有效辅助医护类专业的实践教学。

基于医疗失效模式与效应分析优化急诊抢救室心电监护仪报警应答管理流程的研究

目的 探讨医疗失效模式与效应分析(health failure mode and effect analysis, HFMEA)在急诊抢救室心电监护仪报警应答管理流程中的应用效果。方法 将2023年7月-9月急诊抢救室收治的48例患者设为对照组,将2023年10月—12月急诊抢救室收治的48例患者设为观察组,对照组采用常规流程对心电监护仪报警进行应答管理,观察组采用基于HFMEA优化后的流程对心电监护仪报警进行应答管理。比较2组心电监护仪误报警率、报警应答有效率以及护士报警疲劳评分。结果 观察组心电监护仪误报警率低于对照组(χ2=156.225,P<0.001),报警应答有效率高于对照组(χ2=81.823,P<0.001),且实施HFMEA后急诊抢救室护士报警疲劳得分低于实施前(t=5.093,P<0.001)。结论 应用HFMEA对急诊抢救室监护仪报警应答管理流程进行优化,降低了心电监护仪误报警率,提高了报警应答有效率,减轻了护士的报警疲劳,从而有效防范因监护仪报警应答管理缺陷而导致的医疗风险。

一种透气减压防压力性损伤的心电监护电极片的临床应用效果观察

目的探讨一种透气减压防压力性损伤的心电监护电极片的临床应用效果。方法筛选2022年1月至12月我科住院且医生开具心电监护医嘱的患者200例,将其随机分为观察组和对照组,每组100例。观察组使用一种透气减压防压力性损伤的心电监护电极片,对照组使用普通电极片,比较两组患者使用后并发症发生率、每天更换电极片的频率、额外更换电极片及路上来回平均时间、总资源费用支出、舒适度、患者满意度及医护满意度。结果观察组患者的并发症远远低于普通电极片,差异具有统计学意义(P<0.01)。观察组在电极片更换的频率、额外更换电极片及路上来回平均时间、额外费用支出方面均低于对照组,差异具有统计学意义(P<0.01)。观察组舒适度、患者满意度、医护满意度均高于对照组,差异具有统计学意义(P<0.01)。结论透气减压防压力性损伤的心电监护电极片优于普通电极片,降低了粘胶相关皮肤损伤的发生率,节省了护理人员时间,减少了患者费用额外支出,舒适度较好,经济实用,节约成本,减轻患者痛苦,提高了患者满意度、医护满意度,实用性强。