The future of remote ECG monitoring systems

Remote ECG monitoring systems are becoming commonplace medical devices for remote heart monitoring. In recent years, remote ECG monitoring systems have been applied in the monitoring of various kinds of heart diseases, and the quality of the transmission and re- ception of the ECG signals during remote process kept advancing. However, there remains accompanying challenges. This report focuses on the three components of the remote ECG monitoring system： patient （the end user）, the doctor workstation, and the remote server, reviewing and evaluating the imminent challenges on the wearable systems, packet loss in remote transmission, portable ECG monitoring system, pa- tient ECG data collection system, and ECG signals transmission including real-time processing ST segment, R wave, RR interval and QRS wave, etc. This paper tries to clarify the future developmental strategies of the ECG remote monitoring, which can be helpful in guiding the research and development of remote ECG monitoring.

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.

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.

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 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.

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.

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.

Design and Implementation of Long-Term Single-Lead ECG Monitor

Some heart diseases need long-term monitoring to diagnose. In this paper, we present a wearable single lead ECG monitoring device with low power consumption based on MSP430 and single-lead ECG front-end AD8232, which could acquire and store patient’s ECG data for 7 days continuously. This device is available for long-term wearing with a small volume. Also, it could detect user’s motion status with an acceleration sensor and supports Bluetooth 4.0 protocol. So it could be expanded to be a dynamic heart rate monitor and/or sleep quality monitor combined with smart phone. The device has huge potential of application for health care of human daily life.

Development of Portable Monitoring System for Real-Time Detection of Fetal Movement

Effective fetal monitoring is an important guarantee for fetal health and early treatment. Fetal movement is one of critical indicators of fetal monitoring, which plays an important role in fetal health. Counting the number of fetal movement by pregnant women is a traditional method for long-term monitoring. However, there are many defects in pregnant women’s feeling count, which cannot meet the accurate requirements of modern perinatal medicine. With the rapid development of biological and electronic technology, various sensors are used to probe the fetal dynamic monitoring, but not on fetal movement. This research proposes a monitoring method for fetal movement via three electrodes. Briefly: first, three electrodes are used to extract electrical signals in the abdomen of pregnant women;second, these signals are amplified and filtered;third, A/D converter with microprocessor is used to make analog digital conversion, which can be stored in the SD card under the control of the microprocessor;finally, the SD card data are processed by computer software and the fetal movement information is analyzed.

Cardiac monitoring for patients with palpitations

Palpitations are one of the most common reasons for medical consultation. Theytend to worry patients and can affect their quality of life. They are often asymptom associated with cardiac rhythm disorders, although there are otheretiologies. For diagnosis, it is essential to be able to reliably correlate the symptomswith an electrocardiographic record allowing the identification or rulingout of a possible rhythm disorder. However, reaching a diagnosis is not alwayssimple, given that they tend to be transitory symptoms and the patient isfrequently asymptomatic at the time of assessment. In recent years, electrocardiographicmonitoring systems have incorporated many technical improvements thatsolve several of the 24-h Holter monitor limitations. The objective of this review isto provide an update on the different monitoring methods currently available,remarking their indications and limitations, to help healthcare professionals toappropriately select and use them in the work-up of patients with palpitations.

Monitoring Biosignals with Low Cost Wearable Sensors

With the increase of aging population, we have been witnessing a decline in the quality of life influenced by numerous social, cultural and economic factors. Several studies have addressed these facts and some emerging technologies are capable of monitoring and anticipating these problems. With the advance in the development of smart textiles, it＇s possible to use these technologies in the acquisition of biosignals, which allows obtaining a better comfort regarding the use of smart clothes over traditional Ag/AgCI electrodes. In this way, it is possible to monitor for longer periods reducing the discomfort to the user. This paper reports the development of a low cost sensor with the capability of monitoring the electrical activity of the heart, measuring the heart rate and body temperature and is applied in the scenario： health ＆ wellbeing, targeting the continuous measurement of vital signs.

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.

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。

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。

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。