Cardiac atrial pathology in Duchenne muscular dystrophy

INTRODUCTION/AIMS

Duchenne muscular dystrophy (DMD) is characterized by fibrofatty replacement of muscle. This has been documented in the ventricular myocardium of DMD patients, but there is limited description of atrial involvement. The purpose of this study is to examine the arrhythmia and ectopy burden in patients with DMD and non-DMD dilated cardiomyopathy (DCM) and to characterize the cardiac histopathologic changes in DMD patients across the disease spectrum.

METHODS

This was a retrospective analysis of age-matched patients with DMD and non-DMD DCM who received a Holter monitor and cardiac imaging within 100 days of each other between 2010 and 2020. Twenty-four-hour Holter monitors were classified based on the most recent left ventricular ejection fraction at the time of monitoring. Cardiac histopathologic specimens from whole-heart examinations at the time of autopsy from three DMD patients and one DCM patient were reviewed.

RESULTS

A total of 367 patients with 1299 Holter monitor recordings were included over the study period, with 94% representing DMD patients and 6% non-DMD DCM. Patients with DMD had more atrial ectopy across the cardiac function spectrum (p < 0.05). There was no difference in ventricular ectopy. Four DMD patients developed symptomatic atrial arrhythmias. Autopsy specimens from DMD patients demonstrated fibrofatty infiltration of both atrial and ventricular myocardium.

DISCUSSION

The atrial myocardium in patients with DMD is unique. Autopsy specimens reveal fibofatty replacement of the atrial myocardium, which may be a nidus for both ectopy and arrhythmias in DMD patients.

Variable clinical expression of a novel FLNC truncating variant in a large family

BACKGROUND

Variants in Filamin-C (FLNC) have been associated with various hereditary cardiomyopathies. Recent literature reports a prevalence of sudden cardiac death (SCD) of 13-25% among carriers of truncating-variants, with mean age of 42±15 years for first SCD event. This study reports two familial cases of SCD and the results of cascade screening of their large family.

METHODS

Molecular-autopsy of the SCD victims revealed a novel truncating-variant in the FLNC gene (chr 7:128496880 [hg19]; NM_001458.5; c.7467_7474del; p.(Ser2490fs)). We screened thirty-two family members following genetic counseling, and variant carriers underwent a comprehensive workup followed by consultation with a cardiologist with expertise in the genetics of cardiac diseases.

RESULTS

Seventeen variant carriers were identified: ages between 9 and 85 (mean 47±26). Fifteen underwent clinical evaluation. To date, none of the identified carriers has had major adverse events. In evaluated patients, ECG showed right-axis deviation in 60% (n = 9). Holter recorded frequent premature ventricular contractions (PVCs) (991±2030 per 24 h) in 33% (n = 5) with 4 patients having polymorphic PVC morphology. Three carriers had echocardiographic evidence of mild left-ventricular (LV) systolic dysfunction and another with mild LV dilatation. Cardiac magnetic-resonance (CMR) exhibited late‑gadolinium-enhancement in 10 out of 11 exams, mainly in the mid-myocardium and sub-epicardium, frequently involving the septum and the inferior-lateral wall.

CONCLUSION

This large FLNC truncating variant carrier family exhibits high cardiomyopathy penetrance, best diagnosed by CMR, with variable clinical expressions. These findings present a challenge in SCD prevention management and underscoring the imperative for better risk stratification measures.

A multi-module algorithm for heartbeat classification based on unsupervised learning and adaptive feature transfer

The scarcity of annotated data is a common issue in the realm of heartbeat classification based on deep learning. Transfer learning (TL) has emerged as an effective strategy for addressing this issue. However, current TL techniques in this realm overlook the probability distribution differences between the source domain (SD) and target domain (TD) databases. The motivation of this paper is to address the challenge of labeled data scarcity at the model level while exploring an effective method to eliminate domain discrepancy between SD and TD databases, especially when SD and TD are derived from inconsistent tasks. This study proposes a multi-module heartbeat classification algorithm. Initially, unsupervised feature extractors are designed to extract rich features from unlabeled SD and TD data. Subsequently, a novel adaptive transfer method is proposed to effectively eliminate domain discrepancy between features of SD for pre-training (PTF-SD) and features of TD for fine-tuning (FTF-TD). Finally, the adapted PTF-SD is employed to pre-train a designed classifier, and FTF-TD is used for classifier fine-tuning, with the objective of evaluating the algorithm's performance on the TD task. In our experiments, MNIST-DB serves as the SD database for handwritten digit image classification task, MIT-DB as the TD database for heartbeat classification task. The overall accuracy of classifying heartbeats into normal heartbeats, supraventricular ectopic beats (SVEBs), and ventricular ectopic beats (VEBs) reaches 96.7 %. Specifically, the sensitivity (Sen), positive predictive value (PPV), and F1 score for SVEBs are 0.802, 0.701, and 0.748, respectively. For VEBs, Sen, PPV, and F1 score are 0.976, 0.840, and 0.903, respectively. The results indicate that the proposed multi-module algorithm effectively addresses the challenge labeled data scarcity in heartbeat classification through unsupervised learning and adaptive feature transfer methods.

Enhancing the efficiency and cost-effectiveness of magnetocardiography by optimal channel selection for cardiac diagnosis

Background. Magnetocardiography (MCG) is a non-invasive and non-contact technique that measures weak magnetic fields generated by the heart. It is highly effective in the diagnosis of heart abnormalities. Multichannel MCG provides detailed spatio-temporal information of the measured magnetic fields. While multichannel MCG systems are costly, usage of the optimal number of measurement channels to characterize cardiac magnetic fields without any appreciable loss of signal information would be economically beneficial and promote the widespread use of MCG technology.Methods. An optimization method based on the sequential selection approach is used to choose channels containing the maximum signal information while avoiding redundancy. The study comprised 40 healthy individuals, along with two subjects having ischemic heart disease and one subject with premature ventricular contraction. MCG measured using a 37 channel MCG system. After revisiting the existing methods of optimization, the mean error and correlation of the optimal set of measurement channels with those of all 37 channels are evaluated for different sets, and it has been found that 18 channels are adequate.Results. The chosen 18 optimal channels exhibited a strong correlation (0.99 ± 0.006) between the original and reconstructed magnetic field maps for a cardiac cycle in healthy subjects. The root mean square error is 0.295 pT, indicating minimal deviation.Conclusion. This selection method provides an efficient approach for choosing MCG, which could be used for minimizing the number of channels as well as in practical unforeseen measurement conditions where few channels are noisy during the measurement.

A rare etiology of frequent ventricular ectopy: Embolic complication of a venous iliac stent to the right ventricle

Peripheral venous stent migration is an exceedingly rare complication of endovascular stenting. In this clinical vignette, we present a case of a 74-year-old male with a history of endo-venous laser ablation therapy of the right greater saphenous vein complicated with an occlusion requiring a left iliac vein stent. The patient presented to the clinic months after the procedure with complaints of palpitations. Multimodality imaging revealed a stent that had become dislodged and was now located in the right ventricle, trapped within the tricuspid valve apparatus.

Bilateral Cardiac Sympathetic Denervation for Refractory Multifocal Premature Ventricular Contractions in Patients With Nonischemic Cardiomyopathy

BACKGROUND

Bilateral cardiac sympathetic denervation (BCSD) for refractory life-threatening ventricular arrhythmias is a neuromodulatory intervention targeting sympathetically driven focal or re-entrant ventricular arrhythmias.

OBJECTIVES

This study sought to provide a more complete and successful option for intervention in patients in whom premature ventricular contraction (PVC) ablation is not feasible or has been unsuccessful.

METHODS

A total of 43 patients with >5% PVC burden and concomitant nonischemic cardiomyopathy (NICM) who previously failed medical and ablation therapies were referred for BCSD. All patients underwent bilateral video-assisted thoracoscopic surgical approach with T1-T4 sympathectomy. Primary effectiveness endpoints were postprocedural PVC burden resolution, improvement in left ventricular ejection fraction (LVEF), and cessation of antiarrhythmic drugs (AADs). Safety endpoints included peri- and postprocedural complications. Outcomes were assessed over a 1-year follow-up period.

RESULTS

Among the 43 patients who underwent BCSD, the mean age was 52.3 ± 14.7 years, 69.8% of whom were male patients. Presenting mean LVEF was 38.7% ± 7.8%, and PVC burden was 23.7% ± 9.9%. There were significant reductions in PVC burden postprocedurally (1.3% ± 1.1% post-BCSD, compared with 23.7% ± 9.9% pre-BCSD, P < 0.001) and improvements in LVEF (46.3% ± 9.5% post-BCSD, compared with 38.7% ± 7.8% pre-BCSD, P < 0.001). The rate of ICD therapies decreased from 81.4% (n = 35) to 11.6% (n = 5) (P < 0.001), leading to a significant reduction in use of AADs (100.0% to 11.6%, P < 0.001) and improvement in mean NYHA functional class (2.5 ± 0.5 to 1.4 ± 0.2, P < 0.001). Major intraoperative complications were seen in 4.7% of patients (hemothorax and chylothorax). Of the patients, 81.4% (n = 35) experienced no mortality or major complications over a 1-year follow-up period, with the remaining still within their first year postprocedure.

CONCLUSIONS

BCSD is effective for the management of refractory PVCs and ventricular tachycardia who have failed previous ablation therapy.

Role of dofetilide in patients with ventricular arrhythmias

BACKGROUND OR PURPOSE

To assess effectiveness of dofetilide in reducing the burden of ventricular arrhythmias (VAs).

BACKGROUND

Prior small sample studies show that dofetilide has benefit in reducing VA. However, large sample investigations with long-term follow-up are lacking.

METHODS

Two hundred seventeen consecutive patients admitted between January 2015 and December 2021 for dofetilide initiation for control of VA were assessed. Dofetilide was successfully started in 176 patients (81%) and had to be discontinued in the remaining 41 patients (19%). Dofetilide was initiated for control of ventricular tachycardia (VT) in 136 patients (77%), whereas 40 (23%) patients were initiated on dofetilide for reducing the burden of premature ventricular complexes (PVCs).

RESULTS

The mean follow-up was 24 ± 7 months. In total, among the 136 VT patients, 33 (24%) died, 11 (8%) received a left ventricular assist device (LVAD), and 3 (2%) received a heart transplant during follow-up. Dofetilide was discontinued in 117 (86%) patients due to lack of sustained effectiveness during follow-up. Dofetilide use was associated with similar odds of the composite outcome of all-cause mortality/LVAD/heart transplant (OR: 0.97, 0.55-4.23) in patients with ischemic cardiomyopathy (ICM) compared to those with non-ischemic cardiomyopathy (NICM). Dofetilide did not reduce PVC burden during follow-up in the 40 patients with PVCs (mean baseline PVC burden: 15%, at 1-year follow-up: 14%).

CONCLUSIONS

Dofetilide use was less effective in reducing VA burden in our cohort of patients. Randomized controlled studies are needed to confirm our findings.

Deep learning-based regional ECG diagnosis platform

OBJECTIVE

To enable the intelligent diagnosis of a variety of common Electrocardiogram (ECG), we investigate the deep learning-based ECG diagnosis system.

METHODS

From January 2015 to December 2019, four consecutive years of 100,120 conventional 12-lead ECG data were collected in our hospital. Utilizing this dataset, we constructed a deep learning model designed to intelligently diagnose prevalent ECG anomalies by employing a multi-task learning framework. The system performance was evaluated using various metrics, including sensitivity, specificity, negative predictive value, positive predictive value, and so forth. Additionally, we employed an ECG intelligent diagnostic platform for clinical application to undertake real-time online analysis of 2500 conventional 12-lead ECG samples in June 2020, aiming to validate our model. At this stage, we compared the performance of our model against the traditional manual identification method.

RESULTS

The efficacy of the ECG intelligent diagnostic model was notably high for common and straightforward ECG patterns, such as sinus rhythm (F1 = 98.01%), sinus tachycardia (F1 = 96.26%), sinus bradycardia (F1 = 94.88%), and a normal electrocardiogram (F1 = 91.71%), as well as for Premature Ventricular Contractions (F1 = 91.62%). Nevertheless, when diagnosing rarer and more intricate ECG anomalies, the system requires an increased number of samples to refine the deep learning models. During the validation stage, our model exhibited better efficiency in terms of accuracy, labor time and labor cost when compared to the manual identification approach.

CONCLUSIONS

Our deep learning-driven intelligent ECG diagnostic model clearly demonstrates significant clinical utility. The integrated artificial intelligence diagnosis system not only has the potential to augment physicians in their diagnostic processes but also offers a viable avenue to reduce associated labor costs.

Effectiveness of the risk stratification proposed by the 2022 European Heart Rhythm Association Expert Consensus statement on arrhythmic mitral valve prolapse

BACKGROUND

Recently, an expert consensus statement proposed indications where implantation of a primary prevention implantable cardioverter-defibrillator (ICD) may be reasonable in patients with mitral valve prolapse (MVP). The objective was to evaluate the proposed risk stratification by the expert consensus statement.

METHODS

Consecutive patients with MVP without alternative arrhythmic substrates with cardiac magnetic resonance imaging (CMR) were included in a single-center retrospective registry. Arrhythmic MVP (AMVP) was defined as a total premature ventricular complex burden ≥5%, non-sustained ventricular tachycardia (VT), VT, or ventricular fibrillation. The end point was a composite of SCD, VT, inducible VT, and appropriate ICD shocks.

RESULTS

In total, 169 patients (52.1% male, median age 51.4 years) were included and 99 (58.6%) were classified as AMVP. Multivariate logistic regression identified the presence of late gadolinium enhancement (OR 2.82, 95%CI 1.45-5.50) and mitral annular disjunction (OR 1.98, 95%CI 1.02-3.86) as only predictors of AMVP. According to the EHRA risk stratification, 5 patients with AMVP (5.1%) had a secondary prevention ICD indication, while in 69 patients (69.7%) the implantation of an ICD may be reasonable. During a median follow-up of 8.0 years (IQR 5.0-15.6), the incidence rate for the composite arrhythmic end point was 0.3%/year (95%CI 0.1-0.8).

CONCLUSION

More than half of MVP patients referred for CMR met the AMVP diagnostic criteria. Despite low long-term event rates, in 70% of patients with AMVP the implantation of an ICD may be reasonable. Risk stratification of SCD in MVP remains an important knowledge gap and requires urgent investigation.

An intelligent diagnostic method of ECG signal based on Markov transition field and a ResNet

BACKGROUND AND OBJECTIVE

Heart disease seriously threatens human life and health. It has the character of abruptness and is necessary to accurately monitor and intelligently diagnose electrocardiograph signals in real-time. As part of the automation of heart monitoring, the electrocardiogram (ECG) intelligent diagnosis method based on deep learning not only meets the needs of real-time and accurate but also can abandon relevant professional knowledge, which makes it possible to be promoted in the general population.

METHODS

This paper presents an intelligent diagnosis method based on a ResNet. Firstly, ECG signals from MIT-BIH Database are converted into 2-dim matrices by Markov Transition Field. Secondly, the matrices are used as the input of a ResNet. Then, the ResNet is able to extract high abstract features of various diseases and realize intelligent identification of five heartbeat types, including Normal Beat, Left Bundle Branch Block Beat, Right Bundle Branch Block Beat, Premature Ventricular Contraction Beat, and Atrial Premature Contraction Beat. Eventually, the proposed model is used to identify Normal Beat and Atrial Fibrillation(AF) based on the PAF Prediction Challenge Database(the PAFPC Database) to verify its generalization ability.

RESULTS

The experiment result shows that the intelligent diagnosis method can reach a high F1-score of 97.7% and a high accuracy upon to 99.2% on MIT-BIH Database, which are higher than the models proposed by other researchers. Its mean sensitivity and mean specificity are 97.42% and 99.54%, respectively. Moreover, the accuracy of the generalization ability verification experiment is 94.57% on the PAFPC Database, which is also higher than the results of other studies.

CONCLUSION

The research results show that the method proposed in this paper still achieves higher accuracy and higher F1-score than other methods without any data preprocessing. This method has better classification performance than traditional machine learning methods and other deep learning methods. That is, the method based on Markov Transition Field and a ResNet has good application prospects. At the same time, it has been verified that the model proposed in this paper also has excellent generalization ability.

Autonomic denervation, myocardial hypoperfusion and fibrosis may predict ventricular arrhythmia in the early stages of Chagas cardiomyopathy

BACKGROUND

Sudden cardiac death (SCD) can be the first clinical event of Chagas heart disease (CHD). However, current guidelines contain no clear recommendation for early cardioverter-defibrillator implantation. Using imaging modalities, we evaluated associations among autonomic denervation, myocardial hypoperfusion, fibrosis and ventricular arrhythmia in CHD.

METHODS AND RESULTS

Twenty-nine patients with CHD and preserved left ventricular function underwent 123I-metaiodobenzylguanidine (MIBG) scintigraphy, 99mTc-methoxyisobutylisonitrile (MIBI) myocardial perfusion and cardiac magnetic resonance imaging (MRI). They were divided into arrhythmic (≥ 6 ventricular premature complexes/h and/or non-sustained ventricular tachycardia on 24-hour Holter, n = 15) and non-arrhythmic (< 6 ventricular premature complexes/h and no ventricular tachycardia; n = 14) groups. The arrhythmic group had higher denervation scores from MIBG imaging (23.2 ± 18.7 vs 5.6 ± 4.9; P < .01), hypoperfusion scores from MIBI SPECT (4.7 ± 6.8 vs 0.29 ± 0.6: P = .02), innervation/perfusion mismatch scores (18.5 ± 17.5 vs 5.4 ± 4.8; P = .01) and fibrosis by late gadolinium enhancement on MRI (14.3% ± 13.5% vs 4.0% ± 2.9%; P = .04) than the non-arrhythmic group.

CONCLUSION

These imaging parameters were associated with ventricular arrhythmia in early CHD and may enable risk stratification and the implementation of primary preventive strategies for SCD.

Mechanism of Nardostachyos Radix et Rhizoma-Salidroside in the treatment of premature ventricular beats based on network pharmacology and molecular docking

To analyse the mechanism of Nardostachyos Radix et Rhizoma-Salidroside in the treatment of Premature Ventricular Brats by using network pharmacology and molecular docking and to provide the basis for developing the use of experimental and clinical traditional Chinese medicine. The chemical compositions of Nardostachyos Radix et Rhizoma and Salidroside were determined, and their related targets were predicted. The disease-related targets were obtained by searching the common disease databases Genecards, OMIM, Drugbank and DisGeNET, and the intersection between the predicted targets and the disease targets was determined. Then using the STRING database to set up the protein‒protein interactions (PPIs) network between Nardostachyos Radix et Rhizoma-Salidroside and the common targets of PVB. An "herb-ingredient-target" network was constructed and analyzed by Cytoscape3.7.2 software. Using the metascape database to analysis the predicted therapeutic targets based on the GO and KEGG. Finally, molecular docking technology was used toconfirm the capacity of the primary active ingredients of the 2 herbs to bind to central targets using the online CB-Dock2 database. 41 active components of Nardostachyos Radix et Rhizoma-Salidroside were detected, with 420 potential targets of action, with a total of 1688 PVB targets, and the top 10 core targets of herb-disease degree values were AKT1, TNF, GAPDH, SRC, PPARG, EGFR, PTGS2, ESR1, MMP9, and STAT3. KEGG analysis indicated that its mechanism may be related to the calcium signalling pathway, cancer signalling pathway, AGE-RAGE signalling pathway and other pathways. Molecular docking suggested that main of the active ingredients of the Nardostachyos Radix et Rhizoma-Salidroside pairs were well bound to the core targets. Based on novel network pharmacology and molecular docking validation research methods, we revealed for the first time the potential mechanism of Nardostachyos Radix et Rhizoma-Salidroside in PVB therapy.

Lead I R-wave indexes: A novel electrocardiographic criterion for distinguishing the origin of idiopathic premature ventricular contractions from the three subregions of the aortic sinus cusps

PURPOSE

The current study was conducted to investigate the electrocardiographic (ECG) characteristics of idiopathic premature ventricular contractions (PVCs) originating from the aortic sinus cusp (ASC) and establish a novel ECG criterion to discriminate PVCs originating from the right coronary cusp (RCC), left coronary cusp (LCC), and the left and right coronary cusp junction (LRJ).

METHODS

A retrospective analysis was performed on a total of 133 patients with idiopathic PVCs who underwent successful mapping and ablation. The sites of origin (SOO) were confirmed using fluoroscopy and a three-dimensional mapping system during radiofrequency catheter ablation (RFCA). Among the patients, 69 had PVCs originating from the LCC, 39 from the RCC, and 25 from the LRJ. Characteristics of surface 12‑lead electrocardiograms (ECGs) recorded during PVCs were analyzed. Q-, R-, S, and R'-wave amplitudes were measured in lead I, and the lead I R-wave indexes (IRa, IRb, IRc, IRd, and IRe) were derived by employing multiplication, subtraction, sum, and division operations on these ECG measurements. Notably, IRb and IRe demonstrated usefulness as ECG indexes for discriminating PVCs originating from RCC, LCC, and LRJ in the ASC.

RESULTS

The R- and S-wave amplitudes in lead I exhibited statistically significant differences among the three groups (P < 0.001 and P < 0.001, respectively). In discriminating PVCs originating from the RCC from the other two groups, IRb showed the largest area under the curve (AUC) of 0.813, as assessed by receiver operating characteristic (ROC) analysis, with a cutoff value of ≤0.5 indicating PVCs of RCC origin. The sensitivity and specificity were 80.3% and 78.7%, respectively. For discriminating PVCs arising from the LCC from those in the LRJ group, IRe exhibited the largest AUC of 0.801, with an optimal cutoff value of 0. An IRe value >0 indicated PVCs originating from the LRJ, while an IRe value ≤0 indicated PVCs originating from the LCC. The sensitivity and specificity of the IRe index were 84.0% and 70.7%, respectively.

CONCLUSION

Lead I R-wave indexes provided simple and useful ECG criteria for discriminating PVCs originating from the LCC, RCC, and LRJ in the left ventricular outflow tract (LVOT).

Therapy Efficacy of Idiopathic Ventricular Extrasystoles: A Real Life Study

Background

Ventricular extrasystoles (VESs) are common and often harmless in a healthy heart, but they can significantly affect the quality of life. If changes in lifestyle and antiarrhythmic medication are not enough, invasive and often curative catheter ablation can be considered. Better understanding of the conformation of VESs with a 12-lead ECG, as well as their precise localization, have increased their treatment with catheter ablation. Our goal was to determine whether the anatomical site of VES had an effect on procedure success. We also analyzed the safety of the procedure and patient-related factors affecting the results.

Materials and Methods

In this retrospective study, we analyzed the medical records of 63 consecutive patients with multiple idiopathic VESs treated by catheter ablation at Heart Hospital, Tampere University Hospital, during 2017 and 2018. Patients with structural heart disease were excluded. Ablation success was estimated with two endpoints, primary and follow-up success.

Results

The majority of the patients received treatment on the right ventricular outflow tract (66.7%), others on the left ventricle (17.5%), or the aortic cusp (9.5%). The site of origin remained unknown in four procedures (6.3% of patients). Primary success was observed in 48 procedures (76.2%). During the follow-up period of three months, the procedure was successful in 70.3% of the cases. The anatomical site of VES had no significant effect on either primary or follow-up success. Those with a successful follow-up result had a lower body mass index (BMI = 26.4) than those who had an unsuccessful result (BMI = 28.7; p=0.069); this did not reach statistical significance, potentially due to the small study population size. Complications were observed in three patients (4.5%). All of them were related to the catheter insertion site.

Conclusions

For a symptomatic patient, catheter ablation is an effective and often fully curative treatment. The success rate was similar regardless of the site of VESs. This suggests that catheter ablation should also be assessed early on for other cases besides classic right ventricular outflow tract VESs. A high BMI was the only factor associated with a poor procedure success rate. The procedure itself is safe, and adverse effects are rare. The radiation dose is also low partly due to the current magnetic navigation method.

Electrocardiographic changes during sustained normobaric hypoxia in patients after myocardial infarction

The safety of prolonged high-altitude stays and exercise for physically fit post-myocardial infarction (MI) patients is unclear. Myocardial tissue hypoxia and pulmonary hypertension can affect cardiac function and electrophysiology, possibly contributing to arrhythmias. We included four non-professional male athletes, clinically stable after left ventricular MI (three with ST-segment elevation MI and one with non-ST-segment elevation MI) treated with drug-eluting stents for single-vessel coronary artery disease. Oxygen levels were reduced to a minimum of 11.8%, then restored to 20.9%. We conducted electrocardiography (ECG), ergometry, and echocardiography assessments in normoxic and hypoxic conditions. With an average age of 57.8 ± 3.3 years and MI history 37 to 104 months prior, participants experienced a significant increase in QTc intervals during hypoxia using Bazett's (from 402 ± 13 to 417 ± 25 ms), Fridericia's (from 409 ± 12 to 419 ± 19 ms), and Holzmann's formulas (from 103 ± 4 to 107 ± 6%) compared to normoxia. This effect partially reversed during recovery. Echocardiographic signs of pulmonary hypertension during normobaric hypoxia correlated significantly with altered QTc intervals (p < 0.001). Despite good health and complete revascularization following MI, susceptibility to hypoxia-induced QTc prolongation and ventricular ectopic beats persists, especially during physical activity. MI survivors planning high-altitude activities should consult cardiovascular specialists with high-altitude medicine expertise.

Purkinje-Related Ventricular Tachycardia and Ventricular Fibrillation: Solved and Unsolved Questions

Of the monomorphic ventricular tachycardias, there are 4 specific tachycardias related to the Purkinje system: 1) idiopathic verapamil-sensitive fascicular ventricular tachycardia (FVT); 2) non-re-entrant FVT; 3) bundle branch re-entry and interfascicular re-entry; and 4) Purkinje-mediated VT in structural heart disease. Verapamil-sensitive FVT is classified into 4 types according to the location of the circuit: 1) left posterior type; 2) left anterior type; 3) left upper septal type;and 4) reverse type. And, in the left anterior and posterior types, there are septal and papillary muscle subtypes. Although macro-re-entry has been reported to be the mechanism underlying verapamil-sensitive FVT, recording the entire circuit is challenging. One possible reason is that the Purkinje-muscle junction may penetrate the myocardial layer as a part of the circuit. The Purkinje network may thus play an important role in the initiation and maintenance of ventricular fibrillation. Further, it has been reported that the development and the abnormalities of the Purkinje system are associated with the arrhythmogenesis of ventricular fibrillation. Furthermore, it has been reported that catheter ablation of trigger ventricular premature complexes, and/or "de-networking" of the Purkinje system, can be used as electrical bailout therapy. There is a hypothesis that the intramural Purkinje system is involved in the generation of J waves. Nevertheless, as there are still unresolved issues that must be debated and accurately analyzed, this review aims to discuss the solved and unsolved questions related to Purkinje-related arrhythmias.

Identification of non-ischaemic fibrosis in male veteran endurance athletes, mechanisms and association with premature ventricular beats

Left ventricular fibrosis can be identified by late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) in some veteran athletes. We aimed to investigate prevalence of ventricular fibrosis in veteran athletes and associations with cardiac arrhythmia. 50 asymptomatic male endurance athletes were recruited. They underwent CMR imaging including volumetric analysis, bright blood (BB) and dark blood (DB) LGE, motion corrected (MOCO) quantitative stress and rest perfusion and T1/T2/extracellular volume mapping. Athletes underwent 12-lead electrocardiogram (ECG) and 24-h ECG. Myocardial fibrosis was identified in 24/50 (48%) athletes. All fibrosis was mid-myocardial in the basal-lateral left ventricular wall. Blood pressure was reduced in athletes without fibrosis compared to controls, but not athletes with fibrosis. Fibrotic areas had longer T2 time (44 ± 4 vs. 40 ± 2 ms, p < 0.0001) and lower rest myocardial blood flow (MBF, 0.5 ± 0.1 vs. 0.6 ± 0.1 ml/g/min, p < 0.0001). On 24-h ECG, athletes with fibrosis had greater burden of premature ventricular beats (0.3 ± 0.6 vs. 0.05 ± 0.2%, p = 0.03), with higher prevalence of ventricular couplets and triplets (33 vs. 8%, p = 0.02). In veteran endurance athletes, myocardial fibrosis is common and associated with an increased burden of ventricular ectopy. Possible mechanisms include inflammation and blood pressure. Further studies are needed to establish whether fibrosis increases risk of malignant arrhythmic events.

The yield of 24-hour ambulatory electrocardiography in the assessment of symptomatic school-age children

INTRODUCTION

Lightheadedness, syncope, dyspnea, chest pain, palpitation, loss of consciousness (LOC) or malaise are a frequent reason for consultation in school-age children (SAC). The yield of holter monitoring (HM) in the investigation of these symptoms in SAC is still controversial given the scarce studies.

AIM

To determine the prevalence of baseline ECG abnormalities and those detected in HM in SAC, and to assess the predictive factors of abnormal HM test.

METHODS

We conducted a retrospective descriptive study in which we had included all the consecutive SAC (6 to 12 years) presenting: syncope or lightheadedness or palpitation, dyspnea, or chest pain, malaise, LOC and referred to our department for HM.

RESULTS

We included 366 children. Mean age was 9.7 ± 1.88 years. The major symptoms experienced by the patients were: palpitation (50.7%), chest pain (16.9%), lightheadedness (11.9%), syncope (6.9%), LOC (5.3%), ECG was abnormal in 8.7%. The two most common baseline ECG abnormalities were premature ventricular contractions (PVCs) (1.8%) and right bundle branch block (1.6%). HM was positive in 101 (26.6%) patients. The most common abnormalities in HM were vagal hypertonia in 61 patients (16.1%), PVCs in 29 patients (7.7%), Malignant abnormalities were encountered in 16 patients (13.9%) whereas benign abnormalities in 99 patients (86.1%). In the multivariate analysis: Age≥10 years, mean heart rate <94 beats per minute, the presence of syncope, LOC, malaise and the presence of PVCs in baseline ECG were identified as independent risk factors of abnormal holter.

CONCLUSION

HM has an important diagnostic value in symptomatic SAC. It allows the identification of benign as well as malignant arrhythmias, which require urgent management.

Worldwide Experience With an Irrigated Needle Catheter for Ablation of Refractory Ventricular Arrhythmias: Final Report

BACKGROUND

We previously reported feasibility of irrigated needle ablation (INA) with a retractable 27-G end-hole needle catheter to treat nonendocardial ventricular arrhythmia substrate, an important cause of ablation failure.

OBJECTIVES

The purpose of this study was to report outcomes and complications in our entire INA-treated population.

METHODS

Patients with recurrent sustained monomorphic ventricular tachycardia (VT) or high-density premature ventricular contractions (PVCs) despite radiofrequency ablation were prospectively enrolled at 4 centers. Endpoints included a 70% decrease in VT frequency or PVC burden decrease to <5,000/24 h at 6 months.

RESULTS

INA was performed in 111 patients (median: 2 failed prior ablations, 71% nonischemic heart disease, and left ventricular ejection fraction 36% ± 14%). INA acutely abolished targeted PVCs in 33 of 37 patients (89%), and PVCs were reduced to <5,000/day in 29 patients (78%). During 6-month follow-up, freedom from hospitalization was observed in 50 of 72 patients with VT (69%), and improvement or abolition of VT occurred in 47%. All patients received multiple INA applications, with more in the VT group than in the PVC group (median: 12 [IQR: 7-19] vs 7 [5-15]; P < 0.01). After INA, additional endocardial standard radiofrequency ablation was required in 23% of patients. Adverse events included 4 pericardial effusions (3.5%), 3 cases of (anticipated) atrioventricular block (2.6%), and 3 heart failure exacerbations (2.6%). During 6-month follow-up, 5 deaths occurred; none were procedure-related.

CONCLUSIONS

INA achieves improved arrhythmia control in 78% of patients with PVCs and avoids hospitalization in 69% of patients with VT refractory to standard ablation at 6-month follow-up. Procedural risks are acceptable. (Intramural Needle Ablation for Ablation of Recurrent Ventricular Tachycardia, NCT01791543; Intramural Needle Ablation for the Treatment of Refractory Ventricular Arrhythmias, NCT03204981).

Arrhythmic Mitral Valve Prolapse With Only Mild or Moderate Mitral Regurgitation: Characterization of Myocardial Substrate

BACKGROUND

Sustained ventricular tachycardia and sudden cardiac death due to degenerative mitral valve prolapse (MVP) can occur in the absence of severe mitral regurgitation (MR). A significant percentage of patients with MVP-related sudden death do not have any evidence of replacement fibrosis, suggesting other unrecognized proarrhythmic factors may place these patients at risk.

OBJECTIVES

This study aims to characterize myocardial fibrosis/inflammation and ventricular arrhythmia complexity in patients with MVP and only mild or moderate MR.

METHODS

Prospective observational study of patients with MVP and only mild or moderate MR underwent ventricular arrhythmia characterization and hybrid positron emission tomography (PET)/magnetic resonance imaging (MRI). Coregistered hybrid 18F-fluorodeoxyglucose (18F-FDG)-PET and MRI late gadolinium enhancement images were assessed and categorized. Recruitment occurred in the cardiac electrophysiology clinic.

RESULTS

In 12 patients with degenerative MVP with only mild or moderate MR, of which a majority had complex ventricular ectopy (n = 10, 83%), focal (or focal-on-diffuse) uptake of 18F-FDG (PET-positive) was detected in 83% (n = 10) of patients. Three-quarters of the patients (n = 9, 75%) had FDG uptake that coexisted with areas of late gadolinium enhancement (PET/MRI-positive). Abnormal T1, T2 and extracellular volume (ECV) values were observed in 58% (n = 7), 25% (n = 3), and 16% (n = 2), respectively.

CONCLUSIONS

Most patients with degenerative MVP, ventricular ectopy, and mild or moderate MR show myocardial inflammation that is concordant with myocardial scar. Further study is needed to determine whether these findings contribute to the observation that most MVP-related sudden deaths occur in patients with less than severe MR.

Electrocardiographic characteristics of right-bundle-branch-block premature ventricular complexes predicting absence of left ventricular scar in athletes with apparently structural normal heart

AIMS

Left ventricular scar is an arrhythmic substrate that may be missed by echocardiography and diagnosed only by cardiac magnetic resonance (CMR), which is a time-consuming and expensive imaging modality. Premature ventricular complexes (PVCs) with a right-bundle-branch-block (RBBB) pattern are independent predictors of late gadolinium enhancement (LGE) but their positive predictive value is low. We studied which electrocardiographic features of PVCs with an RBBB pattern are associated with a higher probability of the absence of an underlying LGE.

METHODS

The study included 121 athletes (36 ± 16 years; 48.8% men) with monomorphic PVCs with an RBBB configuration and normal standard clinical investigations who underwent CMR. LGE was identified in 35 patients (29%), predominantly in those with PVCs with a superior/intermediate axis (SA-IntA) compared to inferior axis (IA) (38% vs. 10%, P = 0.002). Among patients with SA-IntA morphology, the contemporary presence of qR pattern in lead aVR and V1 was exclusively found in patients without LGE at CMR (51.0% vs. 0%, P < 0.0001). Among patients with IA, the absence of LGE correlated to a narrow ectopic QRS (145 ± 16 vs. 184 ± 27 msec, P < 0.001).

CONCLUSIONS

Among athletes with apparently idiopathic PVCs with a RBBB configuration, the presence of a concealed LGE at CMR was documented in 29% of cases, mostly in those with a SA-IntA. In our experience, the contemporary presence of qR pattern in lead aVR and V1 in PVCs with RBBB/SA-IntA morphology or, on the other hand, a relatively narrow QRS in PVCs with an IA, predicted absence of LGE.

WavelNet: A novel convolutional neural network architecture for arrhythmia classification from electrocardiograms

BACKGROUND AND OBJECTIVE

Automated detection of arrhythmias from electrocardiograms (ECGs) can be of considerable assistance to medical professionals in providing efficient treatment for patients with cardiovascular diseases. In recent times, convolutional neural network (CNN)-based arrhythmia classification models have been introduced, but their decision-making processes remain unclear and their performances are not reproducible. This paper proposes an accurate, interpretable, and reproducible end-to-end arrhythmia classification model based on a novel CNN architecture named WavelNet, which is interpretable and optimal for dealing with ECGs.

METHODS

Inspired by SincNet, which is capable of band-pass filtering-based spectral analysis, WavelNet was devised to achieve wavelet transform-based spectral analysis. WavelNet was trained using a subject-oriented five-class ECG arrhythmia dataset generated from the MIT-BIH Arrhythmia Database while following a benchmark scheme. By adopting various mother wavelets, multiple WavelNet-based arrhythmia classification models were implemented. To investigate whether our wavelet transform-based approach outperforms original end-to-end and band-pass filtering-based approaches, our proposed models were compared with vanilla CNN- and SincNet-based models. Model implementation and evaluation processes were repeated ten times in a Google Colab Pro+ environment. Furthermore, our most successful model was compared with state-of-the-art arrhythmia classification models for performance evaluation.

RESULTS

The proposed WavelNet-based models showed excellent performance on classifying non-ectopic, supraventricular ectopic, and ventricular ectopic beats because of their ability to perform adaptive spectral analysis while preserving temporal ECG information compared with vanilla CNN- and SincNet-based models. In particular, a Symlet 4 wavelet-adopting WavelNet-based model achieved the best performance with nearly 90% overall accuracy as well as the highest levels of sensitivity in classifying each arrhythmia class: 91.4%, 49.3%, and 91.4% for non-ectopic, supraventricular ectopic, and ventricular ectopic beat classifications, respectively. These results were comparable to those of state-of-the-art models. In addition, the results are reproducible, which differentiates our study from previous studies.

CONCLUSIONS

Our proposed WavelNet-based arrhythmia classification model achieved remarkable performance based on a reasonable decision-making process, in comparison with other models. As its noteworthy performance is clinically reasonable and reproducible, our proposed model can contribute toward implementing a real-world precision healthcare system for patients with cardiovascular diseases.

Initiation of ventricular arrhythmia in the acquired long QT syndrome

AIMS

Long QT syndrome (LQTS) carries a risk of life-threatening polymorphic ventricular tachycardia (Torsades de Pointes, TdP) and is a major cause of premature sudden cardiac death. TdP is induced by R-on-T premature ventricular complexes (PVCs), thought to be generated by cellular early-afterdepolarisations (EADs). However, EADs in tissue require cellular synchronisation, and their role in TdP induction remains unclear. We aimed to determine the mechanism of TdP induction in rabbit hearts with acquired LQTS (aLQTS).

METHODS AND RESULTS

Optical mapping of action potentials (APs) and intracellular Ca2+ was performed in Langendorff-perfused rabbit hearts (n = 17). TdP induced by R-on-T PVCs was observed during aLQTS (50% K+/Mg++ & E4031) conditions in all hearts (P < 0.0001 vs. control). Islands of AP prolongation bounded by steep voltage gradients (VGs) were consistently observed before arrhythmia and peak VGs were more closely related to the PVC upstroke than EADs, both temporally (7 ± 5 ms vs. 44 ± 27 ms, P < 0.0001) and spatially (1.0 ± 0.7 vs. 3.6 ± 0.9 mm, P < 0.0001). PVCs were initiated at estimated voltages of ∼ -40 mV and had upstroke dF/dtmax and Vm-Ca2+ dynamics compatible with ICaL activation. Computational simulations demonstrated that PVCs could arise directly from VGs, through electrotonic triggering of ICaL. In experiments and the model, sub-maximal L-type Ca2+ channel (LTCC) block (200 nM nifedipine and 90% gCaL, respectively) abolished both PVCs and TdP in the continued presence of aLQTS.

CONCLUSION

These data demonstrate that ICaL activation at sites displaying steep VGs generates the PVCs which induce TdP, providing a mechanism and rationale for LTCC blockers as a novel therapeutic approach in LQTS.

Temporal and Spatial Changes of Proarrhythmic Substrate in Premature Ventricular Contraction-Induced Cardiomyopathy

BACKGROUND

The changes in proarrhythmic substrates and malignant ventricular arrhythmia mechanisms caused by premature ventricular contraction-induced cardiomyopathy (PVCCM) remain unclear.

OBJECTIVES

The goal of this study was to establish the electrophysiological mechanism of how high-load PVC causes malignant arrhythmia.

METHODS

Thirteen swine were exposed to 50% paced PVC from the right ventricular apex for 12 weeks (PVCCM, n = 6) and no pacing for 12 weeks (control, n = 7). Cardiac function was quantified biweekly with echocardiography. Computed tomography scans and electrophysiological examinations were performed monthly to dynamically evaluate the changes in the cardiac structure and the arrhythmogenic substrate.

RESULTS

The decreases in the cardiac function and ventricular enlargement in the PVCCM group were significant after 12 weeks of PVC stimulation compared with the control group (P < 0.001). Electrophysiological examination found that the ventricular effective refractory period dispersion (0.071 ± 0.008), area of the low-voltage zone (9.41 ± 1.55 cm2), and malignant ventricular arrhythmia inducibility (33.3%) of the PVCCM group increased significantly at week 8 after pacing (P < 0.001 vs the control group); these changes slowed down after 8 weeks. Moreover, the distribution of the low-voltage zone presented obvious spatial heterogeneity, especially in the anterior wall of the right ventricle, accompanied by delayed activation in the sinus rhythm (67 ± 13 milliseconds). Consistently, the proportion of ventricular fibrosis- and expression-related proteins were significantly increased in the PVCCM group (P < 0.001), especially in the right ventricle. Moreover, proteomic analysis confirmed the spatial profile of these fibrotic changes in the PVCCM group.

CONCLUSIONS

High-burden PVC can cause significant temporal and spatial heterogeneity changes in proarrhythmic substrates, which are potentially related to the upregulation of calcium signaling caused by asynchronous activation.

The Relationship between the Frontal QRS-T Angle on ECG and Physical Activity Level in Young Adults

BACKGROUND

The heart's electrical activity has been the subject of numerous research concerning various physiological parameters. The frontal QRS-T angle (FQRST) is an advanced ECG variable with clinical epidemiological utility. This study aimed to determine the relationship between FQRST and physical activity exposure among young adults.

METHODS

We recorded the ECG with 12 leads of 124 participants (mean age 20.28 ± 2.23 years, age range 18-27 years). Next, we measured their physical activity level (PAL) with the International Physical Activity Questionnaire-Short Form (IPAQ), which categorizes activity into three classes: low, moderate, or high.

RESULTS

An inferential analysis, based on the Kruskal-Wallis H test and Mann-Whitney U test, revealed a statistically significant difference in FQRST between the three groups of subjects, classified by their PAL (p < 0.001). We also identified a significant regression model between the body mass index (BMI) and the FQRST (p < 0.001).

CONCLUSIONS

The physical activity regime of young adults significantly influences the concordance between ventricular depolarization and repolarization, reflected in the FQRST's width. Also, we found a regression model between FQRST and BMI with statistical significance.

Heart rate turbulence assessed through ergometry after myocardial infarction: a feasibility study

BACKGROUND

Coronary artery disease is an important cause of morbidity and mortality. The impact of ventricular arrhythmias with impaired cardiac vagal activity is one of the most recently studied prognostic factors. However, there are no studies evaluating the phenomenon of heart rate turbulence (HRT) during physical exertion.

OBJECTIVE

To study the behavior of HRT during exercise testing, among individuals after myocardial infarction.

DESIGN AND SETTING

Feasibility study conducted in a university hospital among individuals 4-6 weeks after myocardial infarction.

METHODS

All subjects underwent 24-hour Holter monitoring and ergometric stress testing. We considered that abnormal HRT was present if the turbulence onset was ≥ 0% or turbulence slope was ≤ 2.5 mm/relative risk interval.

RESULTS

All 32 subjects were asymptomatic. Their median age was 58 years (interquartile range 12.8) and 70% were male. Abnormal HRT was associated with ventricular dysfunction in this population. We found no differences regarding the behavior of HRT, in relation to age, gender, smoking, systemic arterial hypertension, diabetes mellitus or dyslipidemia. Ergometric stress testing detected premature ventricular beats (PVB) in approximately 44% of the examinations, and these occurred both during the active phase of effort and in the recovery period. The low occurrence of several isolated PVB in beta-blocked subjects made it difficult to perform statistical analysis to correlate HRT between ergometric and Holter testing.

CONCLUSION

The data obtained in this study do not support performing HRT through ergometric stress testing among patients who remain on beta-blockers post-myocardial infarction, for the purpose of assessing cardiac vagal activity.

A meta-analysis of wenxin granule and metoprolol for the treatment of coronary heart disease and arrhythmia

BACKGROUND

This meta-analysis aimed to systematically and comprehensively assess the effectiveness and safety of wenxin granule (WXG) and metoprolol in the treatment of elderly patients with coronary heart disease (CHD) and arrhythmia.

METHODS

We searched the electronic databases of the Cochrane Library, PUBMED, EMBASE, CNKI, Wangfang, and CBM from initiation to May 1, 2022, and selected a set of clinical indicators for WXG and metoprolol for CHD and arrhythmia. The methodological quality of the included studies was analyzed using the Cochrane risk-of-bias tool. Data were pooled using a fixed-effects or random-effects model, and a meta-analysis was conducted.

RESULTS

Eight randomized controlled trials involving 722 patients with CHD and arrhythmia were included. Our findings showed that WXG and metoprolol showed better effects than metoprolol alone on electrocardiogram change (odds ratio [OR] = 7.21, 95% confidence interval [CI] [1.48, 35.07]), clinical symptom improvement (OR = 5.83, 95% CI [1.52, 22.35]), overall clinical effect (OR = 5.51, 95% CI [2.65, 11.44], P < .001), atrial premature beat (mean difference [MD] = -109.85, 95% CI [-171.25, -48.46], P < .001), ventricular premature beat (MD = -195.43, 95% CI [-334.09, -56.77], P < .001), borderline premature beat (MD = -42.92, 95% CI [-77.18, -8.67], P = .01), short-burst ventricular tachycardia (MD = -35.98, 95% CI [-39.66, -32.30], P < .001), ST segment reduction (MD = -0.47, 95% CI [-0.54, -0.40], P < .001), ST segment decrease duration (MD = -0.76, 95% CI [-0.95, -0.57], P < .001). However, no significant differences were observed in adverse reactions (OR = 0.54, 95% CI [0.27, 1.09], P = .09).

CONCLUSION

Compared to metoprolol alone, WXG and metoprolol can more effectively manage patients with CHD and arrhythmia. However, additional large-scale, multicenter, rigorous, and high-quality randomized controlled trials are warranted to verify the present findings.

Forward-Solution Noninvasive Computational Arrhythmia Mapping: The VMAP Study

BACKGROUND

The accuracy of noninvasive arrhythmia source localization using a forward-solution computational mapping system has not yet been evaluated in blinded, multicenter analysis. This study tested the hypothesis that a computational mapping system incorporating a comprehensive arrhythmia simulation library would provide accurate localization of the site-of-origin for atrial and ventricular arrhythmias and pacing using 12-lead ECG data when compared with the gold standard of invasive electrophysiology study and ablation.

METHODS

The VMAP study (Vectorcardiographic Mapping of Arrhythmogenic Probability) was a blinded, multicenter evaluation with final data analysis performed by an independent core laboratory. Eligible episodes included atrial and ventricular: tachycardia, fibrillation, pacing, premature atrial and ventricular complexes, and orthodromic atrioventricular reentrant tachycardia. Mapping system results were compared with the gold standard site of successful ablation or pacing during electrophysiology study and ablation. Mapping time was assessed from time-stamped logs. Prespecified performance goals were used for statistical comparisons.

RESULTS

A total of 255 episodes from 225 patients were enrolled from 4 centers. Regional accuracy for ventricular tachycardia and premature ventricular complexes in patients without significant structural heart disease (n=75, primary end point) was 98.7% (95% CI, 96.0%-100%; P<0.001 to reject predefined H0 <0.80). Regional accuracy for all episodes (secondary end point 1) was 96.9% (95% CI, 94.7%-99.0%; P<0.001 to reject predefined H0 <0.75). Accuracy for the exact or neighboring segment for all episodes (secondary end point 2) was 97.3% (95% CI, 95.2%-99.3%; P<0.001 to reject predefined H0 <0.70). Median spatial accuracy was 15 mm (n=255, interquartile range, 7-25 mm). The mapping process was completed in a median of 0.8 minutes (interquartile range, 0.4-1.4 minutes).

CONCLUSIONS

Computational ECG mapping using a forward-solution approach exceeded prespecified accuracy goals for arrhythmia and pacing localization. Spatial accuracy analysis demonstrated clinically actionable results. This rapid, noninvasive mapping technology may facilitate catheter-based and noninvasive targeted arrhythmia therapies.

REGISTRATION

URL: https://www.

CLINICALTRIALS

gov; Unique identifier: NCT04559061.

Global ECG Classification by Self-Operational Neural Networks with Feature Injection

OBJECTIVE

Global (inter-patient) ECG classification for arrhythmia detection over Electrocardiogram (ECG) signal is a challenging task for both humans and machines. Automating this process with utmost accuracy is, therefore, highly desirable due to the advent of wearable ECG sensors. However, even with numerous deep learning approaches proposed recently, there is still a notable gap in the performance of global and patient-specific ECG classification performance.  Methods: In this study, we propose a novel approach for inter-patient ECG classification using a compact 1D Self-ONN by exploiting morphological and timing information in heart cycles. We used 1D Self-ONN layers to automatically learn morphological representations from ECG data, enabling us to capture the shape of the ECG waveform around the R peaks. We further inject temporal features based on RR interval for timing characterization. The classification layers can thus benefit from both temporal and learned features for the final arrhythmia classification.

RESULTS

 Using the MIT-BIH arrhythmia benchmark database, the proposed method achieves the highest classification performance ever achieved, i.e., 99.21% precision, 99.10% recall, and 99.15% F1-score for normal (N) segments; 82.19% precision, 82.50% recall, and 82.34% F1-score for the supra-ventricular ectopic beat (SVEBs); and finally, 94.41% precision, 96.10% recall, and 95.2% F1-score for the ventricular-ectopic beats (VEBs). Significance:  As a pioneer application, the results show that compact and shallow 1D Self-ONNs with the feature injection can surpass all state-of-the-art deep models with a significant margin and with minimal computational complexity.

CONCLUSION

 This study has demonstrated that using a compact and superior network model, a global ECG classification can still be achieved with an elegant performance level even when no patient-specific information is used.

Multiclass Convolutional Neural Networks for Atrial Fibrillation Classification

Atrial fibrillation (AF) is a common supraventricular arrhythmia. Its automatic identification by standard 12-lead electrocardiography (ECG) is still challenging. Recently, deep learning provided new instruments able to mimic the diagnostic ability of clinicians but only in case of binary classification (AF vs. normal sinus rhythm-NSR). However, binary classification is far from the real scenarios, where AF has to be discriminated also from several other physiological and pathological conditions. The aim of this work is to present a new AF multiclass classifier based on a convolutional neural network (CNN), able to discriminate AF from NSR, premature atrial contraction (PAC) and premature ventricular contraction (PVC). Overall, 2796 12-lead ECG recordings were selected from the open-source "PhysioNet/Computing in Cardiology Challenge 2021" database, to construct a dataset constituted by four balanced classes, namely AF class, PAC class, PVC class, and NSR class. Each lead of each ECG recording was decomposed into spectrogram by continuous wavelet transform and saved as 2D grayscale images, used to feed a 6-layers CNN. Considering the same CNN architecture, a multiclass classifiers (all classes) and three binary classifiers (AF class, PAC class, and PVC class vs. NSR class) were created and validated by a stratified shuffle split cross-validation of 10 splits. Performance was quantified in terms of area under the curve (AUC) of the receiver operating characteristic. Multiclass classifier performance was high (AF class: 96.6%; PAC class: 95.3%; PVC class: 92.8%; NSR class: 97.4%) and preferable to binary classifiers. Thus, our CNN AF multiclass classifier proved to be an efficient tool for AF discrimination from physiological and pathological confounders. Clinical Relevance-Our CNN AF multiclass classifier proved to be suitable for AF discrimination in real scenarios.

Manifold Approximating Graph Interpolation of Cardiac Local Activation Time

Objective:

Local activation time (LAT) mapping of cardiac chambers is vital for targeted treatment of cardiac arrhythmias in catheter ablation procedures. Current methods require too many LAT observations for an accurate interpolation of the necessarily sparse LAT signal extracted from intracardiac electrograms (EGMs). Additionally, conventional performance metrics for LAT interpolation algorithms do not accurately measure the quality of interpolated maps. We propose, first, a novel method for spatial interpolation of the LAT signal which requires relatively few observations; second, a realistic sub-sampling protocol for LAT interpolation testing; and third, a new color-based metric for evaluation of interpolation quality that quantifies perceived differences in LAT maps.

Methods:

We utilize a graph signal processing framework to reformulate the irregular spatial interpolation problem into a semi-supervised learning problem on the manifold with a closed-form solution. The metric proposed uses a color difference equation and color theory to quantify visual differences in generated LAT maps.

Results:

We evaluate our approach on a dataset consisting of seven LAT maps from four patients obtained by the CARTO electroanatomic mapping system during premature ventricular complex (PVC) ablation procedures. Random sub-sampling and re-interpolation of the LAT observations show excellent accuracy for relatively few observations, achieving on average 6% lower error than state-of-the-art techniques for only 100 observations.

Conclusion:

Our study suggests that graph signal processing methods can improve LAT mapping for cardiac ablation procedures.

Significance:

The proposed method can reduce patient time in surgery by decreasing the number of LAT observations needed for an accurate LAT map.

An Energy Efficient ECG Ventricular Ectopic Beat Classifier Using Binarized CNN for Edge AI Devices

Wearable Artificial Intelligence-of-Things (AIoT) requires edge devices to be resource and energy-efficient. In this paper, we design and implement an efficient binary convolutional neural network (bCNN) algorithm utilizing function-merging and block-reuse techniques to classify between Ventricular and non-Ventricular Ectopic Beat images. We deploy our model into a low-resource low-power field programmable gate array (FPGA) fabric. Our model achieves a classification accuracy of 97.3%, sensitivity of 91.3%, specificity of 98.1%, precision of 86.7%, and F1-score of 88.9%, along with dynamic power dissipation of only 10.5-μW.

Electrophysiology and Arrhythmogenesis in the Human Right Ventricular Outflow Tract

BACKGROUND

Right ventricular outflow tract (RVOT) is a common source of ventricular tachycardia, which often requires ablation. However, the mechanisms underlying the RVOT's unique arrhythmia susceptibility remain poorly understood due to lack of detailed electrophysiological and molecular studies of the human RVOT.

METHODS

We conducted optical mapping studies in 16 nondiseased donor human RVOT preparations subjected to pharmacologically induced adrenergic and cholinergic stimulation to evaluate susceptibility to arrhythmias and characterize arrhythmia dynamics.

RESULTS

We found that under control conditions, RVOT has shorter action potential duration at 80% repolarization relative to the right ventricular apical region. Treatment with isoproterenol (100 nM) shortened action potential duration at 80% repolarization and increased incidence of premature ventricular contractions (P=0.003), whereas acetylcholine (100 μM) stimulation alone had no effect on action potential duration at 80% repolarization or premature ventricular contractions. However, acetylcholine treatment after isoproterenol stimulation reduced the incidence of premature ventricular contractions (P=0.034) and partially reversed action potential duration at 80% repolarization shortening (P=0.029). Immunolabeling of RVOT (n=4) confirmed the presence of cholinergic marker VAChT (vesicular acetylcholine transporter) in the region. Rapid pacing revealed RVOT susceptibility to both concordant and discordant alternans. Investigation into transmural arrhythmia dynamics showed that arrhythmia wave fronts and phase singularities (rotors) were relatively more organized in the endocardium than in the epicardium (P=0.006). Moreover, there was a weak but positive spatiotemporal autocorrelation between epicardial and endocardial arrhythmic wave fronts and rotors. Transcriptome analysis (n=10 hearts) suggests a trend that MAPK (mitogen-activated protein kinase) signaling, calcium signaling, and cGMP-PKG (protein kinase G) signaling are among the pathways that may be enriched in the male RVOT, whereas pathways of neurodegeneration may be enriched in the female RVOT.

CONCLUSIONS

Human RVOT electrophysiology is characterized by shorter action potential duration relative to the right ventricular apical region. Cholinergic right ventricular stimulation attenuates the arrhythmogenic effects of adrenergic stimulation, including increase in frequency of premature ventricular contractions and shortening of wavelength. Right ventricular arrhythmia is characterized by positive spatial-temporal autocorrelation between epicardial-endocardial arrhythmic wave fronts and rotors that are relatively more organized in the endocardium.

Parathyroid carcinoma presenting with ventricular bigeminy in pregnancy

Parathyroid carcinoma is very rare in pregnancy. Clinical features are similar to primary hyperparathyroidism. A 38-year-old pregnant woman had repeated hospital admissions for palpitations, headaches, dizziness and polydipsia. Blood investigations showed severe hypercalcaemia with raised parathyroid hormone and 24-hour ECG showed ventricular bigeminy and premature ventricular contractions. Neck ultrasound showed a lesion in the right thyroid lobe. Consequently, she underwent an en bloc resection of the right parathyroid and thyroid lobe at 23 weeks gestation. Histology results confirmed parathyroid cancer. This case highlights the complexities of identifying hypercalcaemia in pregnancy due to the overlapping features with common disorders of pregnancy. Early recognition and timely surgical management can prevent maternal and fetal complications. Also, the case demonstrates the value of interprofessional collaboration between different specialities in providing quality care and improving outcomes. An abridged version of this case was presented at European Congress of Endocrinology 2021.

Inter-patient arrhythmia classification with improved deep residual convolutional neural network

BACKGROUND AND OBJECTIVE

Early detection of arrhythmias has become critical due to the increased mortality from cardiovascular disease, and ECG is an effective tool for diagnosing cardiovascular disease and detecting arrhythmias. Classification based on ECG signal segments is more suitable for clinical application.

METHODS

An improved deep residual convolutional neural network is proposed to classify arrhythmias automatically. Firstly, the overlapping segmentation method is used to segment the ECG signals in the MIT-BIH database into segments of 5 seconds in length to overcome the imbalance between classes, and these segments of the ECG signals are re-labeled. Then the discrete wavelet transform (DWT) is used to denoise these segments and the improved deep residual convolutional neural network is used for arrhythmia classification. In addition, the focal loss function is used to overcome the imbalanced classification difficulty between classes.

RESULTS

The proposed method gives 94.54% sensitivity, 93.33% positive predictivity, and 80.80% specificity for normal segments. For the supraventricular ectopic segment, the proposed method gives 35.22% sensitivity, 65.88% positive predictivity, and 98.83% specificity. For the ventricular ectopic segment, the proposed method gives 88.35% sensitivity, 79.86% positive predictivity, and 94.92% specificity.

CONCLUSION

The results of this study indicate that the proposed improved deep residual convolutional neural network model trained by the training set obtained by using the overlapping segmentation method is comparable to a classical method and three state-of-art methods. In addition, the classification performance of the network model trained by focal loss as the loss function is further improved.

Real-Time Patient-Specific ECG Classification by 1D Self-Operational Neural Networks

Despite the proliferation of numerous deep learning methods proposed for generic ECG classification and arrhythmia detection, compact systems with the real-time ability and high accuracy for classifying patient-specific ECG are still few. Particularly, the scarcity of patient-specific data poses an ultimate challenge to any classifier. Recently, compact 1D Convolutional Neural Networks (CNNs) have achieved the state-of-the-art performance level for the accurate classification of ventricular and supraventricular ectopic beats. However, several studies have demonstrated the fact that the learning performance of the conventional CNNs is limited because they are homogenous networks with a basic (linear) neuron model. In order to address this deficiency and further boost the patient-specific ECG classification performance, in this study, we propose 1D Self-organized Operational Neural Networks (1D Self-ONNs). Due to its self-organization capability, Self-ONNs have the utmost advantage and superiority over conventional ONNs where the prior operator search within the operator set library to find the best possible set of operators is entirely avoided. As the first study where 1D Self-ONNs are ever proposed for a classification task, our results over the MIT-BIH arrhythmia benchmark database demonstrate that 1D Self-ONNs can surpass 1D CNNs with a significant margin while having a similar computational complexity. Under AAMI recommendations and with minimal common training data used, over the entire MIT-BIH dataset 1D Self-ONNs have achieved 98% and 99.04% average accuracies, 76.6% and 93.7% average F1 scores on supra-ventricular and ventricular ectopic beat (VEB) classifications, respectively, which is the highest performance level ever reported.

Increased Risks of Re-identification For Patients Posed by Deep Learning-Based ECG Identification Algorithms

ECGs analysis is an important tool in cardiac diagnosis. ECG data also have the potential to be used as a biometric source that allows precise person identification similar to the widely used fingerprint and iris recognition techniques. However, this phenomenon also raises serious privacy concerns. In this study, we provide a complete, multi-step ECG identification algorithm using a private database of ECG recordings. We train and validate our AI model on approximately 40k patients which makes this study by far the largest research project in this field. Moreover, our best model attained an exceptionally high accuracy of 94.56%. In addition to discussing the general implications of the deployment of such systems related to privacy, for the first time, we also assess the accuracy of ECG-based identification for distinct heart condition groups (and combinations of such conditions) and the corresponding privacy implications. For instance, we discovered that in contrast to initial expectation that identification accuracy for healthy normal sinus rhythm should be the highest, the identification accuracy is higher for patients with sinus tachycardia or patients who are diagnosed with both ST changes and supraventricular tachycardia. This puts these patients at a higher risk of privacy issues due to re-identification. On the other hand, we observed that patients with premature ventricular contractions have an identification accuracy as low as 78.54%. The identification rate for patients with a pacemaker is 80.2%.Clinical relevance-While ECG as a biometric can be a potentially useful technology, it also raises serious concerns regarding the privacy of cardiac patients. Especially, the ECG Identification algorithms empowered by deep learning can increase the risk of re-identification.

An ECG Heartbeat Classification Method Based on Deep Convolutional Neural Network

The electrocardiogram (ECG) is one of the most powerful tools used in hospitals to analyze the cardiovascular status and check health, a standard for detecting and diagnosing abnormal heart rhythms. In recent years, cardiovascular health has attracted much attention. However, traditional doctors' consultations have disadvantages such as delayed diagnosis and high misdiagnosis rate, while cardiovascular diseases have the characteristics of early diagnosis, early treatment, and early recovery. Therefore, it is essential to reduce the misdiagnosis rate of heart disease. Our work is based on five different types of ECG arrhythmia classified according to the AAMI EC57 standard, namely, nonectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beat. This paper proposed a high-accuracy ECG arrhythmia classification method based on convolutional neural network (CNN), which could accurately classify ECG signals. We evaluated the classification effect of this classification method on the supraventricular ectopic beat (SVEB) and ventricular ectopic beat (VEB) based on the MIT-BIH arrhythmia database. According to the results, the proposed method achieved 99.8% accuracy, 98.4% sensitivity, 99.9% specificity, and 98.5% positive prediction rate for detecting VEB. Detection of SVEB achieved 99.7% accuracy, 92.1% sensitivity, 99.9% specificity, and 96.8% positive prediction rate.

Temporary atrial overdrive pacing during a drug-refractory electrical storm in acute myocardial infarction

A 64-year-old man with diabetes mellitus was diagnosed with a non-ST-segment elevation myocardial infarction and was treated with stent implantation. Four days later, he developed an electrical storm (ES) that persisted despite antiarrhythmic drugs and sedation. External defibrillation was performed more than 100 times over 2 hours. After ruling out the common causes of polymorphic ventricular tachycardia, an ES was considered because of brady-dependent R-on-T phenomenon, presumably precipitated by antiarrhythmic drugs. Temporary transvenous atrial overdrive pacing allowed complete suppression of premature ventricular complexes and ventricular fibrillation.

[An arrhythmia classification method based on deep learning parallel network model]

OBJECTIVE

We propose a parallel neural network classification method to improve the performance of classification of 4 types of arrhythmias: normal beat, supraventricular ectopic beat, ventricular ectopic beat and fused beat.

METHODS

Preprocessing was performed including denoising of ECG signal, segmentation of small-scale heartbeat and large-scale heartbeat and data enhancement. Based on deep learning theory, densely connected convolutional network was applied to improve the limitation of waveform feature extraction, and bidirectional long short-term memory network and efficient channel attention network were combined to enhance the function of time series features and important features of the waveform. The parallel network structure was adopted, and the waveform features of small- scale heartbeat and large-scale heartbeat were input to improve the accuracy of arrhythmia classification at the same time. Softmax was used to carry out the 4 classification tasks of arrhythmia by the parallel network model.

RESULTS

The proposed method was verified using MIT-BIH Arrhythmia Database and 3 groups of experiments. The experiments for comparing the classification performance of multiple parallel network models and that of each classification model under different heartbeat input methods showed that the proposed classification model had an overall accuracy, average sensitivity and average specificity of 99.36%, 96.08% and 99.41%, respectively. Convergence performance analysis of the parallel network classification model showed that the training time of the classification model was 41 s.

CONCLUSION

The parallel multi-network classification method can improve the average sensitivity, specificity and training time while maintaining a high overall accuracy, and may thus provide a new technical solution for clinical diagnosis of arrhythmia.

Intracardiac Echocardiography to Guide the Ablation of Parahisian Arrhythmias

Arrhythmias from the perinodal region have been described for accessory pathways (APs), atrial tachycardias (AT), premature ventricular complexes (PVCs), and ventricular tachycardia (VT). The parahisian (PH) region encompasses anatomic structures that include the atrioventricular (AV) node and His-Bundle (HB). These locations are at high-risk for inducing AV block during catheter ablation in the electrophysiology laboratory. PH arrhythmias were initially defined as having sites of origin within 10 mm of the largest HB potential (>0.1 mV) recording site, but more recent definitions have included any site that has an HB potential at the ablation site. Intracardiac echocardiography (ICE) use offers real-time visualization of the catheter tip-to-tissue contact and can monitor for acute complications during atrial and ventricular procedures. ICE also enables a broad appreciation of real-time cardiac structures, which is invaluable in navigating the complex anatomy of the PH region.