ECG autoencoder based on low-rank attention.

The prevalence of cardiovascular disease (CVD) has surged in recent years, making it the foremost cause of mortality among humans. The Electrocardiogram (ECG), being one of the pivotal diagnostic tools for cardiovascular diseases, is increasingly gaining prominence in the field of machine learning. However, prevailing neural network models frequently disregard the spatial dimension features inherent in ECG signals. In this paper, we propose an ECG autoencoder network architecture incorporating low-rank attention (LRA-autoencoder). It is designed to capture potential spatial features of ECG signals by interpreting the signals from a spatial perspective and extracting correlations between different signal points. Additionally, the low-rank attention block (LRA-block) obtains spatial features of electrocardiogram signals through singular value decomposition, and then assigns these spatial features as weights to the electrocardiogram signals, thereby enhancing the differentiation of features among different categories. Finally, we utilize the ResNet-18 network classifier to assess the performance of the LRA-autoencoder on both the MIT-BIH Arrhythmia and PhysioNet Challenge 2017 datasets. The experimental results reveal that the proposed method demonstrates superior classification performance. The mean accuracy on the MIT-BIH Arrhythmia dataset is as high as 0.997, and the mean accuracy and F 1 -score on the PhysioNet Challenge 2017 dataset are 0.850 and 0.843.

Observational study protocol for an arrhythmia notification feature.

INTRODUCTION: Screening for atrial fibrillation (AF) in the general population may help identify individuals at risk, enabling further assessment of risk factors and institution of appropriate treatment. Algorithms deployed on wearable technologies such as smartwatches and fitness bands may be trained to screen for such arrhythmias. However, their performance needs to be assessed for safety and accuracy prior to wide-scale implementation. METHODS AND ANALYSIS: This study will assess the ability of the WHOOP strap to detect AF using its WHOOP Arrhythmia Notification Feature (WARN) algorithm in an enriched cohort with a 2:1 distribution of previously diagnosed AF (persistent and paroxysmal) and healthy controls. Recruited participants will collect data for 7 days with the WHOOP wrist-strap and BioTel ePatch (electrocardiography gold-standard). Primary outcome will be participant level sensitivity and specificity of the WARN algorithm in detecting AF in analysable windows compared with the ECG gold-standard. Similar analyses will be performed on an available epoch-level basis as well as comparison of these findings in important subgroups. ETHICS AND DISSEMINATION: The study was approved by the ethics board at the study site. Participants will be enrolled after signing an online informed consent document. Updates will be shared via clinicaltrials.gov. The data obtained from the conclusion of this study will be presented in national and international conferences with publication in clinical research journals. TRIAL REGISTRATION NUMBER: NCT05809362.

Pediatric and Familial Genetic Arrhythmia Syndromes: Evaluation of Bidirectional Ventricular Tachycardia-Differential Diagnosis.

Bidirectional ventricular tachycardia is a unique arrhythmia that can herald lethal arrhythmia syndromes. Using cases based on real patient stories, this article examines 3 different presentations to help clinicians learn the differential diagnosis associated with this condition. Each associated genetic disorder will be briefly discussed, and valuable tips for distinguishing them from each other will be provided.

[Rhythm management in patients with heart failure].

INTRODUCTION: Arrhythmias manifest frequently in individuals with heart failure, posing a notable threat of mortality and morbidity. While the prevention of sudden cardiac death through ICD therapy remains pivotal, accurate risk stratification remains a challenging task even in 2024. Recent data underscore the early consideration of catheter ablation for ventricular tachycardias. Although antiarrhythmic drug therapy serves as an ancillary measure for symptomatic patients, it does not confer prognostic advantages. The holistic management of arrhythmias in heart failure necessitates a systematic, multidimensional approach that initiates with evidence-based medical therapy for heart failure and integrates device-based and interventional therapies. Noteworthy clinical studies have illustrated the positive prognostic impact of early rhythm control strategies, particularly catheter ablation, in individuals managing heart failure and atrial fibrillation.

A review regarding the article 'Electrocardiographic abnormalities in patients with metabolic dysfunction-associated Steatotic liver disease: A systematic review and meta-analysis.'

Metabolic-dysfunction-associated Steatotic liver disease (MASLD) is a high-risk condition for both liver fibrosis and cardiovascular disease (CVD). Therefore, therapeutic strategies to prevent both liver fibrosis and atherosclerotic CVD are required for the treatment of MASLD. Metabolic dysfunction-associated steatohepatitis (MASH) is the more severe form of MASLD, is defined histologically by the presence of lobular inflammation and hepatocyte ballooning and is associated with a greater risk of fibrosis progression. While CVD is the leading cause of mortality in patients with MASLD, those with more severe liver fibrosis are at increased risk of liver-related mortality, with the risk increasing exponentially with fibrosis stage. MASH has been found in 63% of patients with MASLD undergoing liver biopsy in an Asian multi-center cohort. Multiple complex pathways are involved in the association between MASLD and CVD. The visceral accumulation of fat around the liver and other organs, including the pericardium, leads to the release of fat-derived metabolites with the activation of several inflammatory pathways Cardiac rhythm abnormalities are prevalent in MASLD, such as prolongation of the QT interval, ventricular arrhythmias, and atrial fibrillation. Therapeutic interventions that improve cardiometabolic risk factors may be beneficial for an improvement in MASLD. The effects of such therapeutic interventions on lipid, lipoprotein and apoprotein accumulation in the liver and on hepatic steatosis and fibrosis still remain unelucidated. Which lipid factor is crucial for developing MASLD also remains largely unknown.

A New Scoring System for the Evaluation of Ibrutinib-Associated Arrhythmias in Chronic Lymphocytic Leukemia: The ACEF Score

Objective: Bruton tyrosine kinase inhibition in cardiac tissue causes inhibition of the PI3K-AKT signaling pathway, which is responsible for protecting cardiac tissue during stress. Therefore, there is an increase in the risk of arrhythmia. This study explores the prediction of that risk with the Age-Creatinine-Ejection Fraction (ACEF) score as a simple scoring system based on the components of age, creatinine, and ejection fraction. Materials and Methods: Patients diagnosed with chronic lymphocytic leukemia (CLL) and receiving ibrutinib treatment for at least 1 year were evaluated with echocardiography and Holter electrocardiography and the results were compared with a control group of CLL patients who had not received treatment. ACEF score was calculated with the formula age/left ventricular ejection fraction+1 (if creatinine >2.0 mg/dL). Results: When the arrhythmia development of the patients was evaluated, no statistically significant difference was found between the control and ibrutinib groups in terms of types of arrhythmias other than paroxysmal atrial fibrillation (PAF). PAF was found to occur at rates of 8% versus 22% (p=0.042) among ibrutinib non-users versus users. For patients using ibrutinib, an ACEF score of >1.21 predicted the development of PAF with 77% sensitivity and 75% specificity (area under the curve: 0.830, 95% confidence interval: 0.698-0.962, p<0.001). Conclusion: The ACEF score can be used as a risk score that predicts the development of PAF in patients diagnosed with CLL who are scheduled to start ibrutinib.

Definition and management of arrhythmia-induced cardiomyopathy: findings from the European Heart Rhythm Association survey.

AIMS: Arrhythmia-induced cardiomyopathy (AiCM) represents a subtype of acute heart failure (HF) in the context of sustained arrhythmia. Clear definitions and management recommendations for AiCM are lacking. The European Heart Rhythm Association Scientific Initiatives Committee (EHRA SIC) conducted a survey to explore the current definitions and management of patients with AiCM among European and non-European electrophysiologists. METHODS AND RESULTS: A 25-item online questionnaire was developed and distributed among EP specialists on the EHRA SIC website and on social media between 4 September and 5 October 2023. Of the 206 respondents, 16% were female and 61% were between 30 and 49 years old. Most of the respondents were EP specialists (81%) working at university hospitals (47%). While most participants (67%) agreed that AiCM should be defined as a left ventricular ejection fraction (LVEF) impairment after new onset of an arrhythmia, only 35% identified a specific LVEF drop to diagnose AiCM with a wide range of values (5-20% LVEF drop). Most respondents considered all available therapies: catheter ablation (93%), electrical cardioversion (83%), antiarrhythmic drugs (76%), and adjuvant HF treatment (76%). A total of 83% of respondents indicated that adjuvant HF treatment should be started at first HF diagnosis prior to antiarrhythmic treatment, and 84% agreed it should be stopped within six months after LVEF normalization. Responses for the optimal time point for the first LVEF reassessment during follow-up varied markedly (1 day-6 months after antiarrhythmic treatment). CONCLUSION: This EHRA Survey reveals varying practices regarding AiCM among physicians, highlighting a lack of consensus and heterogenous care of these patients.

Contemporary Advances in Cardiac Remote Monitoring: A Comprehensive, Updated Mini-Review.

Over the past decade, remote monitoring (RM) has become an increasingly popular way to improve healthcare and health outcomes. Modern cardiac implantable electronic devices (CIEDs) are capable of recording an increasing amount of data related to CIED function, arrhythmias, physiological status and hemodynamic parameters, providing in-depth and updated information on patient cardiovascular function. The extensive use of RM for patients with CIED allows for early diagnosis and rapid assessment of relevant issues, both clinical and technical, as well as replacing outpatient follow-up improving overall management without compromise safety. This approach is recommended by current guidelines for all eligible patients affected by different chronic cardiac conditions including either brady- and tachy-arrhythmias and heart failure. Beyond to clinical advantages, RM has demonstrated cost-effectiveness and is associated with elevated levels of patient satisfaction. Future perspectives include improving security, interoperability and diagnostic power as well as to engage patients with digital health technology. This review aims to update existing data concerning clinical outcomes in patients managed with RM in the wide spectrum of cardiac arrhythmias and Hear Failure (HF), disclosing also about safety, effectiveness, patient satisfaction and cost-saving.

Pediatric and Familial Genetic Arrhythmia Syndromes-Evaluation of Prolonged QTc-Differential Diagnosis and what You Need to Know.

The case series reviews differential diagnosis of a genetic arrhythmia syndrome when evaluating a patient with prolonged QTc. Making the correct diagnosis requires: detailed patient history, family history, and careful review of the electrocardiogram (ECG). Signs and symptoms and ECG characteristics can often help clinicians make the diagnosis before genetic testing results return. These skills can help clinicians make an accurate and timely diagnosis and prevent life-threatening events.

Pediatric and Familial Genetic Arrhythmia Syndromes: SCN5A-Related Disorders When It Is Not Long QT Type 3: Clinical Signs and Symptoms.

The following case series presents three different pediatric patients with SCN5A-related disease. In addition, family members are presented to demonstrate the variable penetrance that is commonly seen. Identifying features of this disease is important, because even in the very young, SCN5A disorders can cause lethal arrhythmias and sudden death.

Discrete Wavelet Transform based ECG classification using gcForest: A deep ensemble method.

BACKGROUND: Cardiovascular diseases (CVDs) are the leading global cause of mortality, necessitating advanced diagnostic tools for early detection. The electrocardiogram (ECG) is pivotal in diagnosing cardiac abnormalities due to its non-invasive nature. OBJECTIVE: This study aims to propose a novel approach for ECG signal classification, addressing the challenges posed by the complexity of ECG signals associated with various diseases. METHODS: Our method integrates Discrete Wavelet Transform (DWT) for feature extraction, capturing salient features of cardiovascular diseases. Subsequently, the gcForest model is employed for efficient classification. The approach is tested on the MIT-BIH Arrhythmia Database. RESULTS: The proposed method demonstrates promising results on the MIT-BIH Arrhythmia Database, achieving a test accuracy of 98.55%, recall of 98.48%, precision of 98.44%, and an F1 score of 98.46%. Additionally, the model exhibits robustness and low sensitivity to hyper-parameters. CONCLUSION: The combined use of DWT and the gcForest model proves effective in ECG signal classification, showcasing high accuracy and reliability. This approach holds potential for improving early detection of cardiovascular diseases, contributing to enhanced cardiac healthcare.

Lack of Prognostic Value of T-Wave Alternans for Implantable Cardioverter-Defibrillator Benefit in Primary Prevention.

BACKGROUND: New methods to identify patients who benefit from a primary prophylactic implantable cardioverter-defibrillator (ICD) are needed. T-wave alternans (TWA) has been shown to associate with arrhythmogenesis of the heart and sudden cardiac death. We hypothesized that TWA might be associated with benefit from ICD implantation in primary prevention. METHODS AND RESULTS: In the EU-CERT-ICD (European Comparative Effectiveness Research to Assess the Use of Primary Prophylactic Implantable Cardioverter-Defibrillators) study, we prospectively enrolled 2327 candidates for primary prophylactic ICD. A 24-hour Holter monitor reading was taken from all recruited patients at enrollment. TWA was assessed from Holter monitoring using the modified moving average method. Study outcomes were all-cause death, appropriate shock, and survival benefit. TWA was assessed both as a contiguous variable and as a dichotomized variable with cutoff points <47 µV and <60 µV. The final cohort included 1734 valid T-wave alternans samples, 1211 patients with ICD, and 523 control patients with conservative treatment, with a mean follow-up time of 2.3 years. TWA ≥60 µV was a predicter for a higher all-cause death in patients with an ICD on the basis of a univariate Cox regression model (hazard ratio, 1.484 [95% CI, 1.024-2.151]; P=0.0374; concordance statistic, 0.51). In multivariable models, TWA was not prognostic of death or appropriate shocks in patients with an ICD. In addition, TWA was not prognostic of death in control patients. In a propensity score-adjusted Cox regression model, TWA was not a predictor of ICD benefit. CONCLUSIONS: T-wave alternans is poorly prognostic in patients with a primary prophylactic ICD. Although it may be prognostic of life-threatening arrhythmias and sudden cardiac death in several patient populations, it does not seem to be useful in assessing benefit from ICD therapy in primary prevention among patients with an ejection fraction of ≤35%.

Management of patients with an electrical storm or clustered ventricular arrhythmias: a clinical consensus statement of the European Heart Rhythm Association of the ESC-endorsed by the Asia-Pacific Heart Rhythm Society, Heart Rhythm Society, and Latin-American Heart Rhythm Society.

Electrical storm (ES) is a state of electrical instability, manifesting as recurrent ventricular arrhythmias (VAs) over a short period of time (three or more episodes of sustained VA within 24 h, separated by at least 5 min, requiring termination by an intervention). The clinical presentation can vary, but ES is usually a cardiac emergency. Electrical storm mainly affects patients with structural or primary electrical heart disease, often with an implantable cardioverter-defibrillator (ICD). Management of ES requires a multi-faceted approach and the involvement of multi-disciplinary teams, but despite advanced treatment and often invasive procedures, it is associated with high morbidity and mortality. With an ageing population, longer survival of heart failure patients, and an increasing number of patients with ICD, the incidence of ES is expected to increase. This European Heart Rhythm Association clinical consensus statement focuses on pathophysiology, clinical presentation, diagnostic evaluation, and acute and long-term management of patients presenting with ES or clustered VA.

Machine learning based detection of T-wave alternans in real ambulatory conditions.

BACKGROUND AND OBJECTIVE: T-wave alternans (TWA) is a fluctuation in the repolarization morphology of the ECG. It is associated with cardiac instability and sudden cardiac death risk. Diverse methods have been proposed for TWA analysis. However, TWA detection in ambulatory settings remains a challenge due to the absence of standardized evaluation metrics and detection thresholds. METHODS: In this work we use traditional TWA analysis signal processing-based methods for feature extraction, and two machine learning (ML) methods, namely, K-nearest-neighbor (KNN) and random forest (RF), for TWA detection, addressing hyper-parameter tuning and feature selection. The final goal is the detection in ambulatory recordings of short, non-sustained and sparse TWA events. RESULTS: We train ML methods to detect a wide variety of alternant voltage from 20 to 100 µV, i.e., ranging from non-visible micro-alternans to TWA of higher amplitudes, to recognize a wide range in concordance to risk stratification. In classification, RF outperforms significantly the recall in comparison with the signal processing methods, at the expense of a small lost in precision. Despite ambulatory detection stands for an imbalanced category context, the trained ML systems always outperform signal processing methods. CONCLUSIONS: We propose a comprehensive integration of multiple variables inspired by TWA signal processing methods to fed learning-based methods. ML models consistently outperform the best signal processing methods, yielding superior recall scores.

Long-term reliability of the phospholamban (PLN) p.(Arg14del) risk model in predicting major ventricular arrhythmia: a landmark study.

AIMS: Recently, a genetic variant-specific prediction model for phospholamban (PLN) p.(Arg14del)-positive individuals was developed to predict individual major ventricular arrhythmia (VA) risk to support decision-making for primary prevention implantable cardioverter defibrillator (ICD) implantation. This model predicts major VA risk from baseline data, but iterative evaluation of major VA risk may be warranted considering that the risk factors for major VA are progressive. Our aim is to evaluate the diagnostic performance of the PLN p.(Arg14del) risk model at 3-year follow-up. METHODS AND RESULTS: We performed a landmark analysis 3 years after presentation and selected only patients with no prior major VA. Data were collected of 268 PLN p.(Arg14del)-positive subjects, aged 43.5 ± 16.3 years, 38.9% male. After the 3 years landmark, subjects had a mean follow-up of 4.0 years (± 3.5 years) and 28 (10%) subjects experienced major VA with an annual event rate of 2.6% [95% confidence interval (CI) 1.6-3.6], defined as sustained VA, appropriate ICD intervention, or (aborted) sudden cardiac death. The PLN p.(Arg14del) risk score yielded good discrimination in the 3 years landmark cohort with a C-statistic of 0.83 (95% CI 0.79-0.87) and calibration slope of 0.97. CONCLUSION: The PLN p.(Arg14del) risk model has sustained good model performance up to 3 years follow-up in PLN p.(Arg14del)-positive subjects with no history of major VA. It may therefore be used to support decision-making for primary prevention ICD implantation not merely at presentation but also up to at least 3 years of follow-up.

Heart patient health monitoring system using invasive and non-invasive measurement.

The abnormal heart conduction, known as arrhythmia, can contribute to cardiac diseases that carry the risk of fatal consequences. Healthcare professionals typically use electrocardiogram (ECG) signals and certain preliminary tests to identify abnormal patterns in a patient's cardiac activity. To assess the overall cardiac health condition, cardiac specialists monitor these activities separately. This procedure may be arduous and time-intensive, potentially impacting the patient's well-being. This study automates and introduces a novel solution for predicting the cardiac health conditions, specifically identifying cardiac morbidity and arrhythmia in patients by using invasive and non-invasive measurements. The experimental analyses conducted in medical studies entail extremely sensitive data and any partial or biased diagnoses in this field are deemed unacceptable. Therefore, this research aims to introduce a new concept of determining the uncertainty level of machine learning algorithms using information entropy. To assess the effectiveness of machine learning algorithms information entropy can be considered as a unique performance evaluator of the machine learning algorithm which is not selected previously any studies within the realm of bio-computational research. This experiment was conducted on arrhythmia and heart disease datasets collected from Massachusetts Institute of Technology-Berth Israel Hospital-arrhythmia (DB-1) and Cleveland Heart Disease (DB-2), respectively. Our framework consists of four significant steps: 1) Data acquisition, 2) Feature preprocessing approach, 3) Implementation of learning algorithms, and 4) Information Entropy. The results demonstrate the average performance in terms of accuracy achieved by the classification algorithms: Neural Network (NN) achieved 99.74%, K-Nearest Neighbor (KNN) 98.98%, Support Vector Machine (SVM) 99.37%, Random Forest (RF) 99.76 % and Naïve Bayes (NB) 98.66% respectively. We believe that this study paves the way for further research, offering a framework for identifying cardiac health conditions through machine learning techniques.