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.

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.

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.

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.

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.

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.

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.

Predicting apical hypertrophic cardiomyopathy using T-wave inversion:Three case reports

BACKGROUND Apical hypertrophic cardiomyopathy(AHCM)is a subtype of hypertrophic cardiomyopathy.Due to its location,the thickening of the left ventricular apex can be missed on echocardiography.Giant negative T waves(GNTs)in left-sided chest leads are the hallmark electrocardiogram(ECG)change of AHCM.CASE SUMMARY The first patient was a 68-year-old woman complaining of recurrent chest tightness persisting for more than 3 years.The second was a 59-year-old man complaining of spasmodic chest tightness persisting for more than 2 years.The third was a 55-year-old woman complaining of recurrent chest pain persisting for 4 mo.In all three cases,GNTs were observed several years prior to apical cardiac hypertrophy after other causes of T-wave inversion were ruled out.CONCLUSION Electrophysiological abnormalities of AHCM appear earlier than structural abnormalities,confirming the early predictive value of ECG for AHCM.

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.

Detection of healthy and pathological heartbeat dynamics in ECG signals using multivariate recurrence networks with multiple scale factors

The electrocardiogram(ECG)is one of the physiological signals applied in medical clinics to determine health status.The physiological complexity of the cardiac system is related to age,disease,etc.For the investigation of the effects of age and cardiovascular disease on the cardiac system,we then construct multivariate recurrence networks with multiple scale factors from multivariate time series.We propose a new concept of cross-clustering coefficient entropy to construct a weighted network,and calculate the average weighted path length and the graph energy of the weighted network to quantitatively probe the topological properties.The obtained results suggest that these two network measures show distinct changes between different subjects.This is because,with aging or cardiovascular disease,a reduction in the conductivity or structural changes in the myocardium of the heart contributes to a reduction in the complexity of the cardiac system.Consequently,the complexity of the cardiac system is reduced.After that,the support vector machine(SVM)classifier is adopted to evaluate the performance of the proposed approach.Accuracy of 94.1%and 95.58%between healthy and myocardial infarction is achieved on two datasets.Therefore,this method can be adopted for the development of a noninvasive and low-cost clinical prognostic system to identify heart-related diseases and detect hidden state changes in the cardiac system.

Convolution-Based Heterogeneous Activation Facility for Effective Machine Learning of ECG Signals

Machine Learning(ML)and Deep Learning(DL)technologies are revolutionizing the medical domain,especially with Electrocardiogram(ECG),by providing new tools and techniques for diagnosing,treating,and preventing diseases.However,DL architectures are computationally more demanding.In recent years,researchers have focused on combining the computationally less intensive portion of the DL architectures with ML approaches,say for example,combining the convolutional layer blocks of Convolution Neural Networks(CNNs)into ML algorithms such as Extreme Gradient Boosting(XGBoost)and K-Nearest Neighbor(KNN)resulting in CNN-XGBoost and CNN-KNN,respectively.However,these approaches are homogenous in the sense that they use a fixed Activation Function(AFs)in the sequence of convolution and pooling layers,thereby limiting the ability to capture unique features.Since various AFs are readily available and each could capture unique features,we propose a Convolutionbased Heterogeneous Activation Facility(CHAF)which uses multiple AFs in the convolution layer blocks,one for each block,with a motivation of extracting features in a better manner to improve the accuracy.The proposed CHAF approach is validated on PTB and shown to outperform the homogeneous approaches such as CNN-KNN and CNN-XGBoost.For PTB dataset,proposed CHAF-KNN has an accuracy of 99.55%and an F1 score of 99.68%in just 0.008 s,outperforming the state-of-the-art CNN-XGBoost which has an accuracy of 99.38%and an F1 score of 99.32%in 1.23 s.To validate the generality of the proposed CHAF,experiments were repeated on MIT-BIH dataset,and the proposed CHAF-KNN is shown to outperform CNN-KNN and CNN-XGBoost.

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%).

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%.

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.