Body Surface Electrocardiogram Research Articles

Basis and applicability of noninvasive inverse electrocardiography: a comparison between cardiac source models.

The body surface electrocardiogram (ECG) is a direct result of electrical activity generated by the myocardium. Using the body surface ECGs to reconstruct cardiac electrical activity is called the inverse problem of electrocardiography. The method to solve the inverse problem depends on the chosen cardiac source model to describe cardiac electrical activity. In this paper, we describe the theoretical basis of two inverse methods based on the most commonly used cardiac source models: the epicardial potential model and the equivalent dipole layer model. We discuss similarities and differences in applicability, strengths and weaknesses and sketch a road towards improved inverse solutions by targeted use, sequential application or a combination of the two methods.

Duration of left and right atrial activation wavefront assessed non-invasively is significantly higher in patients with atrial fibrillation than in controls

Abstract Introduction Ablation strategies in atrial fibrillation (AF) are currently a "one-size- fits- all" type of approach. Pulmonary vein isolation as the standard of care is not perfect, especially in the persistent forms, but none of the alternatives has shown better results. The dichotomic paroxysmal versus persistent AF classification in no longer acceptable. Characterization of the atrial substrate before ablation is needed. Electrocardiographic imaging (ECGI) is capable of non-invasively assess atrial activation in a single beat and in a panoramic fashion. The aim of this study was to evaluate the feasibility and accuracy of ECGI to assess the activation speed and identify possible targets for ablation. Methods We studied 18 consecutive patients, 9 referred for ablation of paroxysmal AF and 9 without AF, referred for ablation of premature ventricular contractions (PVCs), that had an ECGI performed before ablation. No patient had structural heart disease and all patients had normal electrocardiographic evaluation in sinus rhythm (SR). The ECGI was performed in SR with the AMYCARD 01 system, based on body surface electrocardiograms of a maximum of 224 electrodes and computed tomography imaging data. Total duration of atrial activation, left atrial (LA), and right atrial (RA) activation duration, and presence of lines of conduction block (LOB) defined as the presence of a jump in the local activation time of 50 ms or more between contiguous points, were assessed. The results were compared in patients with and without AF. Results The results are depicted in the Table. Patients with AF were older. Total atrial activation duration, LA, and RA activation duration were longer in patients with AF and there was a trend towards a higher prevalence of LOB in the LA in patients with AF (Figure). Discussion & Conclusion Total atrial activation duration as well as LA and RA activation duration assessed with the ECGI were significantly prolonged in patients with AF. Presence of LOB in the LA showed a trend towards a higher prevalence in AF patients. These pilot study has proved that this technology is accurate in identifying a pathological substrate in AF patients. We intend to use ECGI in a subsequent study that will include patients with AF both in paroxysmal and persistent forms, in order to identify markers of disease as well as possible targets to perform a customized AF ablation.Comparison between AF and control groupsECGI showing local activation times

Abstract 16400: Development of Deep Neural Network Models for Identification of Pulmonary Vein Potentials From Body Surface Electrocardiograms of Patients With Atrial Fibrillation

[Background] Pulmonary vein (PV) potential is essential as the origin of atrial fibrillation (AF). However, no established method exists to evaluate PV potentials on a body surface ECG (BS-ECG). [Objective] To develop a method to identify PV potentials from BS-ECGs using state-of-the-art deep neural network (DNN) technology. [Methods and results] A cohort of 799 consecutive paroxysmal AF patients, who underwent Pulmonary Vein Isolation (PVI), were enrolled, and pre- and post-PVI 12-lead ECGs (12ECGs) were collected, amounting to a dataset of 1598 ECGs. Four DNN models were constructed, each employing distinct architectures: 1D-ResNet, 1D-DenseNet, 1D-EfficientNet-b3, and 1D-ViT (Vision Transformer). These models were rigorously trained and evaluated for the identification of PV potentials. The 1D-EfficientNet-b3-based model exhibited optimal performance, achieving an accuracy of 89.4% in the training set and 89.1% in the test set. The Gradient-weighted Class Activation Mapping (Grad-CAM) effectively demonstrated that the segments 200-300ms before the P-wave is crucial for identifying PV potentials. Furthermore, DNN models were developed for each single-lead ECG. While there were variations in the accuracy of PV potential identification among single-lead models, the V6-lead model demonstrated superior performance with an accuracy of 83.2%. [Conclusions] The findings of this research strongly suggest that our DNN-based model can accurately differentiate PV potentials from BS-ECGs. Additionally, Grad-CAM visualizations indicate that pertinent features for the identification of PV potentials are predominantly clustered in the P-wave frontal region. This signifies a promising advancement in the non-invasive detection and analysis of PV potentials, paving the way for improved management of paroxysmal AF.

Resveratrol activation of SIRT1/MFN2 can improve mitochondria function, alleviating doxorubicin‐induced myocardial injury

AbstractBackgroundDoxorubicin is a widely used cytotoxic chemotherapy agent for treating different malignancies. However, its use is associated with dose‐dependent cardiotoxicity, causing irreversible myocardial damage and significantly reducing the patient's quality of life and survival. In this study, an animal model of doxorubicin‐induced cardiomyopathy was used to investigate the pathogenesis of doxorubicin‐induced myocardial injury. This study also investigated a possible treatment strategy for alleviating myocardial injury through resveratrol therapy in vitro.MethodsAdult male C57BL/6J mice were randomly divided into a control group and a doxorubicin group. Body weight, echocardiography, surface electrocardiogram, and myocardial histomorphology were measured. The mechanisms of doxorubicin cardiotoxicity in H9c2 cell lines were explored by comparing three groups (phosphate‐buffered saline, doxorubicin, and doxorubicin with resveratrol).ResultsCompared to the control group, the doxorubicin group showed a lower body weight and higher systolic arterial pressure, associated with reduced left ventricular ejection fraction and left ventricular fractional shortening, prolonged PR interval, and QT interval. These abnormalities were associated with vacuolation and increased disorder in the mitochondria of cardiomyocytes, increased protein expression levels of α‐smooth muscle actin and caspase 3, and reduced protein expression levels of Mitofusin2 (MFN2) and Sirtuin1 (SIRT1). Compared to the doxorubicin group, doxorubicin + resveratrol treatment reduced caspase 3 and manganese superoxide dismutase, and increased MFN2 and SIRT1 expression levels.ConclusionDoxorubicin toxicity leads to abnormal mitochondrial morphology and dysfunction in cardiomyocytes and induces apoptosis by interfering with mitochondrial fusion. Resveratrol ameliorates doxorubicin‐induced cardiotoxicity by activating SIRT1/MFN2 to improve mitochondria function.

Detection of Atrial Fibrillation from Variable-duration ECG Signal based on Time-adaptive Densely Network and Feature Enhancement Strategy.

Atrial fibrillation (AF) is one of the clinic's most common arrhythmias with high morbidity and mortality. Developing an intelligent auxiliary diagnostic model of AF based on a body surface electrocardiogram (ECG) is necessary. Convolutional neural network (CNN) is one of the most commonly used models for AF recognition. However, typical CNN is not compatible with variable-duration ECG, so it is hard to demonstrate its universality and generalization in practical applications. Hence, this paper proposes a novel Time-adaptive densely network named MP-DLNet-F. The MP-DLNet module solves the problem of incompatibility between variable-duration ECG and 1D-CNN. In addition, the feature enhancement module and data imbalance processing module are respectively used to enhance the perception of temporal-quality information and decrease the sensitivity to data imbalance. The experimental results indicate that the proposed MP-DLNet-F achieved 87.98% classification accuracy, and F1-score of 0.847 on the CinC2017 database for 10-second cropped/padded single-lead ECG fragments. Furthermore, we deploy transfer learning techniques to test heterogeneous datasets, and in the CPSC2018 12-lead dataset, the method improved the average accuracy and F1-score by 21.81% and 16.14%, respectively. Experimental results indicate that our method can update the constructed model's parameters and precisely forecast AF with different duration distributions and lead distributions. Combining these advantages, MP-DLNet-F can exemplify all kinds of varied-duration or imbalance medical signal processing problems such as Electroencephalogram (EEG) and Photoplethysmography (PPG).

IEMS: An IoT-Empowered Wearable Multimodal Monitoring System in Neurocritical Care

IoT-empowered wearable multimodal monitoring system (IEMS), an IEMS for neurocritical care is developed to perform simultaneous monitoring of 8-channel electroencephalogram (EEG), 2-channel regional cerebral oxygen saturation (rSO2) based on near-infrared spectrum (NIRS), body surface temperature, electrocardiogram (ECG), photoplethysmography (PPG), and bioimpedance (Bio-Z). The IoT platform and wireless devices enable the patients’ signals available for remote diagnosis. Besides, analysis functions and artificial intelligence (AI) algorithms could be embedded in both the bedside platform and the cloud server to support physicians with clinical decisions. In the multimodal neural monitoring device, the following designs are adopted to face the neurological intensive care unit (NICU) application. Active electrodes and preamplifying free topology provide better signal quality and a larger dynamic range (DR). Dedicated low-power designs ensure the device lasts 10 h of operation. A nonwoven headset improves long-term wearability, which is also quick and easy to install. In the cardiovascular patch, the disposable electrode patch based on elastic materials ensures tight and comfortable contact with skin. Besides, the reusable wireless sensing module is tiny (20 mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times16$ </tex-math></inline-formula> mm <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times9$ </tex-math></inline-formula> mm) but could measure ECG, PPG, and Bio-Z simultaneously. Electrical tests and human subject (healthy volunteers and NICU patients) studies were conducted to examine the performance. The EEG channels show 130.75-dB DR and 0.84- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu {}\text{V}_{\mathrm{ RMS}}$ </tex-math></inline-formula> input-referred noise, which also yields signals with high quality during human EEG monitoring. The NIRS channels exhibit good linearity and are able to operate under severe ambient interference. The temperature sensors show ±0.2 °C accuracy. Moreover, the system complies with mandatory standards for medical equipment. Overall, an IEMS can meet the requirements for NICU applications and could provide better comfort during long-term wearing.

Machine learning for distinguishing right from left premature ventricular contraction origin using surface electrocardiogram features.

Precise localization of the site of origin of premature ventricular contractions (PVCs) before ablation can facilitate the planning and execution of the electrophysiological procedure. The purpose of this study was to develop a predictive model that can be used to differentiate PVCs between the left ventricular outflow tract and right ventricular outflow tract (RVOT) using surface electrocardiogram characteristics. A total of 851 patients undergoing radiofrequency ablation of premature ventricular beats from January 2015 to March 2022 were enrolled. Ninety-two patients were excluded. The other 759 patients were enrolled into the development (n = 605), external validation (n = 104), or prospective cohort (n = 50). The development cohort consisted of the training group (n = 423) and the internal validation group (n = 182). Machine learning algorithms were used to construct predictive models for the origin of PVCs using body surface electrocardiogram features. In the development cohort, the Random Forest model showed a maximum receiver operating characteristic curve area of 0.96. In the external validation cohort, the Random Forest model surpasses 4 reported algorithms in predicting performance (accuracy 94.23%; sensitivity 97.10%; specificity 88.57%). In the prospective cohort, the Random Forest model showed good performance (accuracy 94.00%; sensitivity 85.71%; specificity 97.22%). Random Forest algorithm has improved the accuracy of distinguishing the origin of PVCs, which surpasses 4 previous standards, and would be used to identify the origin of PVCs before the interventional procedure.

Electrocardiographic imaging a valid tool or an inaccurate toy?

Abstract Funding Acknowledgements Type of funding sources: Private hospital(s). Main funding source(s): Learning Health Background and aim Electrocardiographic imaging (ECGI) is capable of performing an activation map with a single beat. However, previous studies using the epicardial-only system, have suggested a bad accuracy for the assessment of the epicardial breakthrough. Recent systems using endo-epicardial analysis have shown promising results. The aim of this study was to assess the accuracy and reproducibility of two endo-epicardial ECGI systems using different cardiac sources one based on the extracellular-potential, and the other on the equivalent double layer model, respectively the AMYCARD (EP Solutions SA, Switzerland) and VIVO (Catheter Precision, NJ USA) systems. Methods We studied 11 consecutive patients referred for ablation of frequent idiopathic premature ventricular contractions at our center that had an ECGI performed using both systems on the same day. The AMYCARD system uses a dense array of body-surface electrocardiograms with up to 224 leads and VIVO uses just the 12-leads ECG. Both systems use a patient-specific heart torso geometry obtained with a CT-scan or cardiac magnetic resonance. The localisation of the PVCs based on ECGI was done using a segmental model with 22 segments on the left ventricle, to include the classical 17 segment model plus the aortic cusps and the papillary muscles, and 12 segments on the right ventricle including 4 on the right ventricular outflow tract (RVOT): (anterior, lateral, right septum and left septum). A perfect match was defined as a predicted location within the same anatomic segment, whereas a near match as a predicted location within the same segment or a contiguous one. Results The median (Q1-Q3) number of leads used for the AMYCARD was 131 (118-144). Seven patients underwent ablation and in 4 ablation is pending. The predicted locations and the ablation site are depicted on the Table. We found a perfect match between both systems in 73% (Figure) and near match in 91% of cases. In patients that underwent ablation the systems localised the site of origin of the PVCs within the same segment or the contiguous segment in all patients with VIVO and in six out of seven with AMYCARD. Conclusions ECGI is an accurate diagnostic tool with reproducible results regardless the cardiac source used for analysis.

Association of Soluble Suppression of Tumorigenicity 2 with New-Onset Atrial Fibrillation in Acute Myocardial Infarction

Background: The combination of acute myocardial infarction (AMI) and atrial fibrillation (AF) is still a thorny problem in the clinic. At present, there are few reports on the role of soluble suppression of tumorigenicity 2 (sST2) in AF after AMI. This study was to explore the predictive value of sST2 in patients with AMI for new-onset AF. Methods: This is a single-center retrospective clinical observation study. We continuously included AMI patients from September 2019 to November 2021. The concentration of sST2 in blood samples was determined. During admission, a suspicious heart rhythm was recorded by electrocardiogram (ECG) monitoring, and new-onset AF was confirmed by immediate body surface ECG. Results: After multiple factors were included, age, right coronary artery, high-sensitivity C-reactive protein, left ventricular ejection fraction, and sST2 were still risk factors for new-onset AF. The area under curve value of age and sST2 was more than 0.7, which showed good diagnostic value. For reevaluation, the sST2 was added to the clinical new-onset AF prediction model. It was found that the integrated discrimination improvement and net reclassification index in the model were improved significantly. Conclusion: sST2 is an independent predictor of new-onset AF in patients with AMI and can improve the accuracy of the AF risk model.

Long-term study on electrophysiological characteristics and catheter ablation of idiopathic ventricular arrhythmias originating from the left ventricular posterior papillary muscles guided by intracardiac ultrasound.

BackgroundThis study aimed to investigate the electrophysiological characteristics of idiopathic ventricular arrhythmias (VAs) originating from the left ventricular posterior papillary muscles (LPPM) and explore the efficiency of catheter ablation using three‐dimensional intracardiac ultrasound technology.MethodsTwenty‐seven cases of premature ventricular contraction/ventricular tachycardia (PVC/VT) originating from the left ventricular posterior papillary muscles were recorded from July 2015 to June 2019 in the Central Hospital of Shengli Oil Field and the First Affiliated Hospital of Zhengzhou University. Electrophysiological mapping and radiofrequency catheter ablation (RFCA) were performed using three‐dimensional intracardiac ultrasound technology. The characteristics of the body surface and intracavity electrocardiogram were analyzed. All cases were followed up for 24 months after the operation.ResultsThe VAs of all 27 cases were successfully eliminated by catheter ablation. QRS complexes were observed with a right bundle branch block (RBBB) pattern and a steep slope in the initial segment. Lead I appeared with an Rs pattern, and inferior leads (lead II, III, and aVF) were usually with an S wave. The lead aVR appeared with a qR pattern, while the R wave was commonly found in aVL. The main wave in leads V1‐V3 was positive but negative in V5 and V6.ConclusionVentricular arrhythmias originating from the left ventricular posterior papillary muscles have similar electrophysiological characteristics. The origin site was accurately located using three‐dimensional intracardiac ultrasound technology. Catheter ablation effectively eliminated VAs.

Effect of Shenfu Injection on Differentiation of Bone Marrow Mesenchymal Stem Cells into Pacemaker-Like Cells and Improvement of Pacing Function of Sinoatrial Node.

Sick sinus syndrome (SSS), a complex type of cardiac arrhythmia, is a major health threat to humans. Shenfu injection (SFI), a formula of traditional Chinese medicine (TCM), is effective in improving bradyarrhythmia. However, the underlying mechanism of SFI's therapeutic effect is subject to few systematic investigations. The purpose of the present research is to examine whether SFI can boost the differentiation effectiveness of bone marrow mesenchymal stem cells (BMSCs) into pacemaker-like cells and whether the transplantation of these cells can improve the pacing function of the sinoatrial node (SAN) in a rabbit model of SSS. BMSCs from New Zealand rabbits were extracted, followed by incubation in vitro. The flow cytometry was utilized to identify the expression of CD29, CD44, CD90, and CD105 surface markers. The isolated BMSCs were treated with SFI, and the whole-cell patch-clamp method was performed to detect hyperpolarization-the activated cyclic nucleotide-gated potassium channel 4 (HCN4) channel current activation curve. The SSS rabbit model was established using the formaldehyde wet dressing method, and BMSCs treated with SFI were transplanted into the SAN of the SSS rabbit model. We detected changes in the body-surface electrocardiogram and recorded dynamic heart rate measurements. Furthermore, transplanted SFI-treated BMSCs were subjected to HE staining, TUNEL staining, qPCR, western blotting, immunofluorescence, immunohistochemistry, and enzyme-linked immunosorbent assay to study their characteristics. Our results indicate that the transplantation of SFI-treated BMSCs into the SAN of SSS rabbits improved the pacing function of the SAN. In vitro data showed that SFI induced the proliferation of BMSCs, promoted their differentiation capacity into pacemaker-like cells, and increased the HCN4 expression in BMSCs. In vivo, the transplantation of SFI treated-BMSCs preserved the function of SAN in SSS rabbits, improved the expression of the HCN4 gene and gap junction proteins (Cx43 and Cx45), and significantly upregulated the expression of cAMP in the SAN, compared to the SSS model group. In summary, the present research demonstrated that SFI might enhance the differentiation capacity of BMSCs into pacemaker-like cells, hence offering a novel approach for the development of biological pacemakers. Additionally, we confirmed the effectiveness and safety of pacemaker-like cells differentiated from BMSCs in improving the pacing function of the SAN.

Catecholamines are key modulators of ventricular repolarization patterns in the ball python (Python regius).

Ectothermic vertebrates experience daily changes in body temperature, and anecdotal observations suggest these changes affect ventricular repolarization such that the T-wave in the ECG changes polarity. Mammals, in contrast, can maintain stable body temperatures, and their ventricular repolarization is strongly modulated by changes in heart rate and by sympathetic nervous system activity. The aim of this study was to assess the role of body temperature, heart rate, and circulating catecholamines on local repolarization gradients in the ectothermic ball python (Python regius). We recorded body-surface electrocardiograms and performed open-chest high-resolution epicardial mapping while increasing body temperature in five pythons, in all of which there was a change in T-wave polarity. However, the vector of repolarization differed between individuals, and only a subset of leads revealed T-wave polarity change. RNA sequencing revealed regional differences related to adrenergic signaling. In one denervated and Ringer's solution-perfused heart, heating and elevated heart rates did not induce change in T-wave polarity, whereas noradrenaline did. Accordingly, electrocardiograms in eight awake pythons receiving intra-arterial infusion of the β-adrenergic receptor agonists adrenaline and isoproterenol revealed T-wave inversion in most individuals. Conversely, blocking the β-adrenergic receptors using propranolol prevented T-wave change during heating. Our findings indicate that changes in ventricular repolarization in ball pythons are caused by increased tone of the sympathetic nervous system, not by changes in temperature. Therefore, ventricular repolarization in both pythons and mammals is modulated by evolutionary conserved mechanisms involving catecholaminergic stimulation.

Critical appraisal of technologies to assess electrical activity during atrial fibrillation: a position paper from the European Heart Rhythm Association and European Society of Cardiology Working Group on eCardiology in collaboration with the Heart Rhythm Society, Asia Pacific Heart Rhythm Society, Latin American Heart Rhythm Society and Computing in Cardiology.

We aim to provide a critical appraisal of basic concepts underlying signal recording and processing technologies applied for (i) atrial fibrillation (AF) mapping to unravel AF mechanisms and/or identifying target sites for AF therapy and (ii) AF detection, to optimize usage of technologies, stimulate research aimed at closing knowledge gaps, and developing ideal AF recording and processing technologies. Recording and processing techniques for assessment of electrical activity during AF essential for diagnosis and guiding ablative therapy including body surface electrocardiograms (ECG) and endo- or epicardial electrograms (EGM) are evaluated. Discussion of (i) differences in uni-, bi-, and multi-polar (omnipolar/Laplacian) recording modes, (ii) impact of recording technologies on EGM morphology, (iii) global or local mapping using various types of EGM involving signal processing techniques including isochronal-, voltage- fractionation-, dipole density-, and rotor mapping, enabling derivation of parameters like atrial rate, entropy, conduction velocity/direction, (iv) value of epicardial and optical mapping, (v) AF detection by cardiac implantable electronic devices containing various detection algorithms applicable to stored EGMs, (vi) contribution of machine learning (ML) to further improvement of signals processing technologies. Recording and processing of EGM (or ECG) are the cornerstones of (body surface) mapping of AF. Currently available AF recording and processing technologies are mainly restricted to specific applications or have technological limitations. Improvements in AF mapping by obtaining highest fidelity source signals (e.g. catheter-electrode combinations) for signal processing (e.g. filtering, digitization, and noise elimination) is of utmost importance. Novel acquisition instruments (multi-polar catheters combined with improved physical modelling and ML techniques) will enable enhanced and automated interpretation of EGM recordings in the near future.

Analysis of six cancer patients with persistent left superior vena cava identified during central venous access device placement via an intracavitary electrocardiogram.

Persistent left superior vena cava (PLSVC) is a rare congenital anomaly. PLSVC can be associated with clinically significant atrial septal defect (ASD) or ventricular septal defect (VSD). It is usually asymptomatic and accidentally detected during invasive procedures or imaging examinations. However, whether central venous access device (CVAD) can be placed and used in patients with PLSVC is controversial. A total of six patients were diagnosed with PLSVC and confirmed by chest CT among 3391 cancer patients who underwent CVAD placement via intracavitary electrocardiogram (IC-EKG) at the Venous Access Center (VAC) from May 2019 to December 2020. The CVADs (peripherally inserted central catheter in four patients and Ports in two patients) of these six patients were left in PLSVC. We analyzed changes in the P-wave in the IC-EKG during CVAD placement and the characteristics of the body surface electrocardiogram in these patients and discussed the catheter tip position in PLSVC. All six patients showed negative P-waves in lead II via IC-EKG from the beginning of catheterization: four patients showed negative P-waves and two showed biphasic P-waves in the body surface electrocardiogram (lead III) before catheterization. CVAD function was normal and no obvious complications were observed during the treatment of these patients. The total retention time of CVADs was 1537 days. For patients with a negative P-wave in lead II via IC-EKG during catheterization, especially in those with a negative or biphasic P-wave in lead III of the body surface electrocardiogram, PLSVC should be considered. CVAD insertion in patients with type I PLSVC is safe under certain conditions, with the proper tip position in the middle to lower part of PLSVC.

Parameter variations in personalized electrophysiological models of human heart ventricles.

The objectives of this study were to evaluate the accuracy of personalized numerical simulations of the electrical activity in human ventricles by comparing simulated electrocardiograms (ECGs) with real patients' ECGs and analyzing the sensitivity of the model output to variations in the model parameters. We used standard 12-lead ECGs and up to 224 unipolar body-surface ECGs to record three patients with cardiac resynchronization therapy devices and three patients with focal ventricular tachycardia. Patient-tailored geometrical models of the ventricles, atria, large vessels, liver, and spine were created using computed tomography data. Ten cases of focal ventricular activation were simulated using the bidomain model and the TNNP 2006 cellular model. The population-based values of electrical conductivities and other model parameters were used for accuracy analysis, and their variations were used for sensitivity analysis. The mean correlation coefficient between the simulated and real ECGs varied significantly (from r = 0.29 to r = 0.86) among the simulated cases. A strong mean correlation (r > 0.7) was found in eight of the ten model cases. The accuracy of the ECG simulation varied widely in the same patient depending on the localization of the excitation origin. The sensitivity analysis revealed that variations in the anisotropy ratio, blood conductivity, and cellular apicobasal heterogeneity had the strongest influence on transmembrane potential, while variation in lung conductivity had the greatest influence on body-surface ECGs. Futhermore, the anisotropy ratio predominantly affected the latest activation time and repolarization time dispersion, while the cellular apicobasal heterogeneity mainly affected the dispersion of action potential duration, and variation in lung conductivity mainly led to changes in the amplitudes of ECGs and cardiac electrograms. We also found that the effects of certain parameter variations had specific regional patterns on the cardiac and body surfaces. These observations are useful for further developing personalized cardiac models.

QT interval measurement with portable device during COVID-19 outbreak.

Coronavirus Disease 2019 continues to spread and to date, no definitive treatment is available. Overcrowded and under-resourced healthcare centres have had to design different strategies to treat these patients, what includes the control of the electrocardiogram (ECG), as some drugs that have been used to treat this disease mayprolong the QT interval as a side effect. During the COVID-19 outbreak, we designed a protocol for monitoring the QT interval using a portable device with Bluetooth connectivity. After a validation study with 50 patients, we found a very good correlation between the QT interval measured both with this device and with the conventional body surface ECG. In this article, we provide a brief overview of the protocol and then analyse the QT changes observed in a group of patients during their hospitalization and treatment for SARS-CoV-2 infection. 81 patients with confirmed SARS-CoV-2 infection were enrolled in the protocol (age 63.4 SD 17.2years; 70.3% men), while being treated with lopinavir/ritonavir, azithromycin and hydroxychloroquine, both individually or combined. Ten patients developed long drug-related QT interval, and the QT prolongation was statically significant for all treatment schemes. All patients with drug induced QT prolongation corrected the QT interval following the indications of the protocol, and no patients died of arrhythmic causes after its implementation. In our experience, a protocol for the electrocardiographic monitoring of these patients minimizes the risk of iatrogenic QT interval prolongation and consequently reduces sudden death events, and for that purpose, portable devices like the one used in this protocol may constitute a useful tool to minimize the contact with such patients.

Identification of healthy and pathological heartbeat dynamics based on ECG-waveform using multifractal spectrum

Human body surface electrocardiogram (ECG) is non-stationary and frequency-varying by nature that belongs to a typical nonlinear signal. Therefore, traditional linear and time–frequency​ analysis methods cannot fully disclose its nonlinear nature. Meanwhile, physiological complexity of heartbeat signal may vary with age, diseases, drug administrations, or even behavioral modifiers. To test the intrinsic relationships among them, we first put forward a theoretical model for nonlinear time series analysis and then took the generally accepted multifractal sets to verify it. Upon that, we then investigated the multifractal singularity spectrum areas of synchronous 12-lead ECG signals taken from crowds with different age stages, healthy conditions and drug medications. Our results suggested that aging and diseases can not only decrease multifractal complexity of the signals, but also increase inhomogeneity of it. With aging and deepening lesion, fractal-like structure of the heartbeat system is damaged or even structurally changed, which lead to the declination of physiological complexity and at the same time the increasement of irregularity and anisotropy of ECG signal’s propagation. In addition, the mean value of multifractal spectrum area of human 12-lead ECG signals also reflect the self-discipline regulation of human autonomic nervous system. The value descends with age growing or drug intervention to restrain sympathetic nerves. That suggest self-discipline control function weakens when people are getting old, or they are under repressed heartbeat activities with lower heart rate and lower blood pressure. Then, complexity of heartbeat signal declines and even tends to turn from multifractality to monofractality, which means drops off of human individual adaptability.

A New Diagnosis Method of ECG Diseases Based on Deep Learning

The research of ECG diagnosis based on deep learning is a hot topic at present. ECG signals are collected from human body surface electrodes and electrocardiograms are obtained. The research process is to integrate engineering technology into the medical field, reflecting the new direction of interdisciplinary combination. This paper introduces the basic principle of ECG signal and the basic analysis method of ECG. The experimental results show that the application of one-dimensional convolutional neural network is more effective and accurate than the traditional methods. The design of the theoretical method has provided the technical support and theoretical basis for the further study of electrophysiological signals and the clinical diagnosis.

In silico validation of electrocardiographic imaging to reconstruct the endocardial and epicardial repolarization pattern using the equivalent dipole layer source model

The solution of the inverse problem of electrocardiology allows the reconstruction of the spatial distribution of the electrical activity of the heart from the body surface electrocardiogram (electrocardiographic imaging, ECGI). ECGI using the equivalent dipole layer (EDL) model has shown to be accurate for cardiac activation times. However, validation of this method to determine repolarization times is lacking. In the present study, we determined the accuracy of the EDL model in reconstructing cardiac repolarization times, and assessed the robustness of the method under less ideal conditions (addition of noise and errors in tissue conductivity). A monodomain model was used to determine the transmembrane potentials in three different excitation-repolarization patterns (sinus beat and ventricular ectopic beats) as the gold standard. These were used to calculate the body surface ECGs using a finite element model. The resulting body surface electrograms (ECGs) were used as input for the EDL-based inverse reconstruction of repolarization times. The reconstructed repolarization times correlated well (COR > 0.85) with the gold standard, with almost no decrease in correlation after adding errors in tissue conductivity of the model or noise to the body surface ECG. Therefore, ECGI using the EDL model allows adequate reconstruction of cardiac repolarization times.Graphical abstractValidation of electrocardiographic imaging for repolarization using forward calculated body surface ECGs from simulated activation-repolarization sequences.

Association of T-wave amplitude on electrocardiogram with left ventricular ejection fraction in children with dilated cardiomyopathy

To study the association of T-wave amplitude on electrocardiogram (ECG) with left ventricular ejection fraction (LVEF) in children with dilated cardiomyopathy. A retrospective analysis was performed for the clinical data of 44 children who were diagnosed with dilated cardiomyopathy from May 2009 to June 2018. According to LVEF, they were divided into two groups: LVEF ≥50% group (n=26) and LVEF <50% group (n=18). After treatment, 25 children were followed up for 3-42 months (mean 14±9 months). The Guangdong Zhongshan SR-1000A ECG Automatic Analyzer was used to obtain the 12-lead body surface ECG results in the supine position. T-wave amplitude on ECG was evaluated by software and manual measurement. Compared with the LVEF ≥50% group, the LVEF <50% group had a significant reduction in the T-wave amplitude in leads II, V4, V5 and V6 (P<0.05). The increased-LVEF group (an increase in LVEF > 5% after treatment) had a significant increase in the T-wave amplitude in leads aVR, V5, and V6 after treatment (P<0.05), while the unchanged-LVEF group (an increase in LVEF ≤ 5% after treatment) had a significant reduction in the T-wave amplitude in lead aVR after treatment (P<0.05). The receiver operating characteristic curve analysis showed that the T-wave amplitude in leads II, V4, V5 and V6 had a certain value in predicting LVEF <50% in children with dilated cardiomyopathy (P<0.05). A combination of T-wave amplitude of ≤0.20 mV in lead II, ≤0.40 mV in lead V4, and ≤0.30 mV in leads V5 and V6 had a sensitivity of 88.2% and specificity of 76.0% in the predication of LVEF <50% in children with dilated cardiomyopathy. T-wave amplitude on ECG can be used as the indexes for the evaluation of the left ventricular systolic function in children with dilated cardiomyopathy.