EFFECTS OF BODY TEMPERATURE ON ELECTROCARDIOGRAMS OF LIZARD Eremias multiocellata *

Electrocardiograms (ECG) of Eremias multiocellata were studied at 5-35℃ in body temperature. Electrocardiogram wave intervals (R-R,P-R,QRS,T-P,and R-T) shortened while heart rate increased with the increasing of body temperature. The average heart rate was 14.6/min at 5℃,whereas it was 201/min at 35℃. The duration of wave intervals of ECG and the heart rate were related significantly to the body temperature (P<0.001). Among the components of a cardiac cycle the cardiac rest period (TP intervals) and the atria-ventricular conduction time (PR interval) were affected mostly by body temperature. In the other hand the ventricular depolarization and repolarization (QRS and R-T intervals) were relatively less affected by the body temperature. The increasing of heart rate with body temperature was mainly caused by the shortening of ECG wave intervals,and the T-P interval (the cardiac rest period) was shortened more noticeably than other intervals.

Electrocardiograms changes in children with functional gastrointestinal disorders on low dose amitriptyline

AIM: To study the effects of low dose amitriptyline on cardiac conduction in children.

Compression algorithm for electrocardiograms based on sparse decomposition

Sparse decomposition is a new theory in signal processing,with the advantage in that the base(dictionary)used in this theory is over-complete,and can reflect the nature of a signal.Thus,the sparse decomposition of signal can obtain sparse representation,which is very important in data compression.The algorithm of compression based on sparse decomposition is investigated.By training on and learning electrocardiogram(ECG)data in the MIT-BIH Arrhythmia Database,we constructed an over-complete dictionary of ECGs.Since the atoms in this dictionary are in accord with the character of ECGs,it is possible that an extensive ECG datum is reconstructed by a few nonzero coefficients and atoms.The proposed compression algorithm can adjust compression ratio according to practical request,and the distortion is low(when the compression ratio is 20∶1,the standard error is 5.11%).The experiments prove the feasibility of the proposed compression algorithm.

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.

Heart-Net: AMulti-Modal Deep Learning Approach for Diagnosing Cardiovascular Diseases

Heart disease remains a leading cause of morbidity and mortality worldwide,highlighting the need for improved diagnostic methods.Traditional diagnostics face limitations such as reliance on single-modality data and vulnerability to apparatus faults,which can reduce accuracy,especially with poor-quality images.Additionally,these methods often require significant time and expertise,making them less accessible in resource-limited settings.Emerging technologies like artificial intelligence and machine learning offer promising solutions by integrating multi-modality data and enhancing diagnostic precision,ultimately improving patient outcomes and reducing healthcare costs.This study introduces Heart-Net,a multi-modal deep learning framework designed to enhance heart disease diagnosis by integrating data from Cardiac Magnetic Resonance Imaging(MRI)and Electrocardiogram(ECG).Heart-Net uses a 3D U-Net for MRI analysis and a Temporal Convolutional Graph Neural Network(TCGN)for ECG feature extraction,combining these through an attention mechanism to emphasize relevant features.Classification is performed using Optimized TCGN.This approach improves early detection,reduces diagnostic errors,and supports personalized risk assessments and continuous health monitoring.The proposed approach results show that Heart-Net significantly outperforms traditional single-modality models,achieving accuracies of 92.56%forHeartnetDataset Ⅰ(HNET-DSⅠ),93.45%forHeartnetDataset Ⅱ(HNET-DSⅡ),and 91.89%for Heartnet Dataset Ⅲ(HNET-DSⅢ),mitigating the impact of apparatus faults and image quality issues.These findings underscore the potential of Heart-Net to revolutionize heart disease diagnostics and improve clinical outcomes.

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.

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.

Comparison of QT Correction Methods in the Pediatric Population of a Community Hospital: A Retrospective Study

Objective:Accurate measurement of QT interval,the ventricular action potential from depolarization to repolarization,is important for the early detection of Long QT syndrome.The most effective QT correction(QTc)formula has yet to be determined in the pediatric population,although it has intrinsically greater extremes in heart rate(HR)and is more susceptible to errors in measurement.The authors of this study compare six dif-ferent QTc methods(Bazett,Fridericia,Framingham,Hodges,Rautaharju,and a computer algorithm utilizing the Bazett formula)for consistency against variations in HR and RR interval.Methods:Descriptive Retrospective Study.We included participants from a pediatric cardiology practice of a community hospital who had an ECG performed in 2017.All participants were healthy patients with no past medical history and no regular med-ications.Results:ECGs from 95 participants from one month to 21 years of age(mean 9.7 years)were included with a mean HR of 91 beats per minute(bpm).The two-sample paired t-test or Wilcoxon signed-rank test assessed for any difference between QTc methods.A statistically significant difference was observed between every combination of two QTc formulae.The Spearman’s rank correlation analysis explored the QTc/HR and QTc/RR relationships for each formula.Fridericia method was most independent of HR and RR with the lowest absolute value of correlation coefficients.Bazett and Computer had moderate correlations,while Framingham and Rautaharju exhibited strong correlations.Correlations were positive for Bazett and Computer,reflecting results from prior studies demonstrating an over-correction of Bazett at higher HRs.In the linear QTc/HR regression analysis,Bazett had the slope closest to zero,although Computer,Hodges,and Fridericia had comparable values.Alternatively,Fridericia had the linear QTc/RR regression coefficient closest to zero.The Bland-Altman method assessed for bias and the limits of agreement between correction formulae.Bazett and Computer exhibited good agreement with minimal bias along with Framingham and Rautaharju.To account for a possible skewed distri-bution of QT,all the above analyses were also performed excluding the top and bottom 2%of data as sorted by heart rate ranges(N=90).Results from this data set were consistent with those derived from all participants(N=95).Conclusions:Overall,the Fridericia correction method provided the best rate correction in our pedia-tric study cohort.

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.

Review on Development and Application of Fabric Electrodes in Electrocardiogram Monitoring Garments

Cardiovascular disease persists as the primary cause of human mortality,significantly impacting healthy life expectancy.The routine electrocardiogram(ECG)stands out as a pivotal noninvasive diagnostic tool for identifying arrhythmias.The evolving landscape of fabric electrodes,specifically designed for the prolonged monitoring of human ECG signals,is the focus of this research.Adhering to the preferred reporting items for systematic reviews and meta-analyses(PRISMA)statement and assimilating data from 81 pertinent studies sourced from reputable databases,the research conducts a comprehensive systematic review and meta-analysis on the materials,fabric structures and preparation methods of fabric electrodes in the existing literature.It provides a nuanced assessment of the advantages and disadvantages of diverse textile materials and structures,elucidating their impacts on the stability of biomonitoring signals.Furthermore,the study outlines current developmental constraints and future trajectories for fabric electrodes.These insights could serve as essential guidance for ECG monitoring system designers,aiding them in the selection of materials that optimize the measurement of biopotential signals.

Effects of atrial septal defects on the cardiac conduction system

The case report presented in this edition by Mu et al.The report presents a case of atrial septal defect(ASD)associated with electrocardiographic changes,noting that the crochetage sign resolved after Selective His Bundle Pacing(S-HBP)without requiring surgical closure.The mechanisms behind the appearance and resolution of the crochetage sign remain unclear.The authors observed the dis-appearance of the crochetage sign post-S-HBP,suggesting a possible correlation between these specific R waves and the cardiac conduction system.This editorial aims to explore various types of ASD and their relationship with the cardiac con-duction system,highlighting the diagnostic significance of the crochetage sign in ASD.

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.

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.

基于心动周期和经验模式分解的心电信号去噪处理

针对现有心电信号(Electrocardiogram,ECG)去噪方法难以精准剥离与之频带重叠的肌电干扰并无损提取到“干净”ECG的问题,提出了利用心动周期和经验模式分解对含噪ECG进行去噪处理。本文方法首先对含噪ECG进行经验模式分解,然后利用心动周期判断固有模态函数分量属于噪声还是有用信号,最后将有用信号的固有模态函数分量重构ECG。为验证本文去噪方法,首先采用ECG动力学仿真模型评估本文方法在不同参数噪声下的去噪效果;其次选取MIT⁃BIH数据库中的基线漂移信号bw,肌电干扰信号ma和105号、107号、123号ECG分别构建3组真实含噪ECG进行实验。评估与实验结果均表明本文方法可以简单、有效地同时去除ECG中的肌电干扰和基线漂移,去噪效果优于普通经验法。

PDNet:A Convolutional Neural Network Has Potential to be Deployed on Small Intelligent Devices for Arrhythmia Diagnosis

Heart arrhythmia is a group of irregular heartbeat conditions and is usually detected by electrocardiograms(ECG)signals.Over the past years,deep learning methods have been developed to classify different types of heart arrhythmias through ECG based on computer-aided diagnosis systems(CADs),but these deep learning methods usually cannot trade-off between classification performance and parameters of deep learning methods.To tackle this problem,this work proposes a convolutional neural network(CNN)model named PDNet to recognize different types of heart arrhythmias efficiently.In the PDNet,a convolutional block named PDblock is devised,which is comprised of a pointwise convolutional layer and a depthwise convolutional layer.Furthermore,an improved loss function is utilized to improve the results of heart arrhythmias classification.To verify the proposed CNN model,extensive experiments are conducted on publicMIT-BIH ECG databases.The experimental results demonstrate that the proposed PDNet achieves an accuracy of 98.2%accuracy and outperforms state-of-the-art methods about 2%.

Serum carbohydrate antigen-125 is elevated in patients with chronic atrial fibrillation

Objective： To determine whether CA-125 is elevated in medically stable patients with chronic atrial fibrillation （AF） compared with patients without AF and to examine whether levels of CA-125 are associated with demographic and clinical variables in a sample of patients under study. Methods： 55 patients with chronic AF and 58 control patients in sinus rhythm were included into the study. Patients with acute heart failure （HF）, chronic inflammatory or neoplastic disease were excluded from the study. Circulating levels of CA-125 were assessed; all patients underwent clinical examination, assessment and medical records including demographic data, history of comorbid conditions, current use of cardiac medications, and the results of cardiac tests including electrocardiograms. Results： The mean age of the study sample was 53.2±6.5 years and 48% were men. Patients with sinus rhythm were significantly more likely to have lower heart rates, smaller dimensions of left atrium, and to be treated with aspirin. Coumadin, coumadin and digoxin were more often prescribed in patients with chronic AF. The CA-125 levels were significantly higher in patients with chronic AF than in patients in sinus rhythm （48.5±7.65 U/ml and 28.43±5.48 U/ml, P〈0.005）. An inverse relation was found between CA-125 levels and left ventricular ejection fraction （LVEF） （r=-0.48, P〈0.001）. CA-125 was significantly related to the left atrium （LA） diameter, left ventricular end-diastolic diameter （LVEDD）, left ventricular end-systolic diameter （LVESD） and brain natriuretic peptide （BNP）. There was no significant correlation between CA-125 and age. Conelusion： In subjects with chronic AF, CA-125 levels are increased; CA-125 was significantly related to the LA diameter, LVEDD, LVESD and BNP.

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.

A Mobile Cloud-Based eHealth Scheme

Mobile cloud computing is an emerging field that is gaining popularity across borders at a rapid pace.Similarly,the field of health informatics is also considered as an extremely important field.This work observes the collaboration between these two fields to solve the traditional problem of extracting Electrocardiogram signals from trace reports and then performing analysis.The developed system has two front ends,the first dedicated for the user to perform the photographing of the trace report.Once the photographing is complete,mobile computing is used to extract the signal.Once the signal is extracted,it is uploaded into the server and further analysis is performed on the signal in the cloud.Once this is done,the second interface,intended for the use of the physician,can download and view the trace from the cloud.The data is securely held using a password-based authentication method.The system presented here is one of the first attempts at delivering the total solution,and after further upgrades,it will be possible to deploy the system in a commercial setting.

Deep Learning Approach for Automatic Cardiovascular Disease Prediction Employing ECG Signals

Cardiovascular problems have become the predominant cause of death worldwide and a rise in the number of patients has been observed lately.Currently,electrocardiogram(ECG)data is analyzed by medical experts to determine the cardiac abnormality,which is time-consuming.In addition,the diagnosis requires experienced medical experts and is error-prone.However,automated identification of cardiovascular disease using ECGs is a challenging problem and state-of-the-art performance has been attained by complex deep learning architectures.This study proposes a simple multilayer perceptron(MLP)model for heart disease prediction to reduce computational complexity.ECG dataset containing averaged signals with window size 10 is used as an input.Several competing deep learning and machine learning models are used for comparison.K-fold cross-validation is used to validate the results.Experimental outcomes reveal that the MLP-based architecture can produce better outcomes than existing approaches with a 94.40%accuracy score.The findings of this study show that the proposed system achieves high performance indicating that it has the potential for deployment in a real-world,practical medical environment.

Early association of electrocardiogram alteration with infarct size and cardiac function after myocardial infarction

Objective: Myocardial infarction (MI) is the main cause of heart failure, but the relationship between the extent of MI and cardiac function has not been clearly determined. The present study was undertaken to investigate early changes in the electrocardiogram associated with infarct size and cardiac function after MI. Methods: MI was induced by ligating the left anterior descending coronary artery in rats. Electrocardiograms, echocardiographs and hemodynamic parameters were assessed and myocardial infarct size was measured from mid-transverse sections stained with Masson抯 trichrome. Results: The sum of pathological Q wave amplitudes was strongly correlated with myocardial infarct size (r = 0.920, P < 0.0001), left ventricular ejection fraction (r = -0.868, P < 0.0001) and left ventricular end diastolic pressure (r = 0.835, P < 0.0004). Furthermore, there was close relationship between MI size and cardiac function as assessed by left ventricular ejection fraction (r = -0.913, P < 0.0001) and left ventricular end diastolic pressure (r = 0.893, P < 0.0001). Conclusion: The sum of pathological Q wave amplitudes after MI can be used to estimate the extent of MI as well as cardiac function.