LDCNN: A new arrhythmia detection technique with ECG signals using a linear deep convolutional neural network.

The electrocardiogram (ECG) is a fundamental and widely used tool for diagnosing cardiovascular diseases. It involves recording cardiac electrical signals using electrodes, which illustrate the functioning of cardiac muscles during contraction and relaxation phases. ECG is instrumental in identifying abnormal cardiac activity, heart attacks, and various cardiac conditions. Arrhythmia detection, a critical aspect of ECG analysis, entails accurately classifying heartbeats. However, ECG signal analysis demands a high level of expertise, introducing the possibility of human errors in interpretation. Hence, there is a clear need for robust automated detection techniques. Recently, numerous methods have emerged for arrhythmia detection from ECG signals. In our research, we developed a novel one-dimensional deep neural network technique called linear deep convolutional neural network (LDCNN) to identify arrhythmias from ECG signals. We compare our suggested method with several state-of-the-art algorithms for arrhythmia detection. We evaluate our methodology using benchmark datasets, including the PTB Diagnostic ECG and MIT-BIH Arrhythmia databases. Our proposed method achieves high accuracy rates of 99.24% on the PTB Diagnostic ECG dataset and 99.38% on the MIT-BIH Arrhythmia dataset.

[Early classification and recognition algorithm for sudden cardiac arrest based on limited electrocardiogram data trained with a two-stages convolutional neural network].

Sudden cardiac arrest (SCA) is a lethal cardiac arrhythmia that poses a serious threat to human life and health. However, clinical records of sudden cardiac death (SCD) electrocardiogram (ECG) data are extremely limited. This paper proposes an early prediction and classification algorithm for SCA based on deep transfer learning. With limited ECG data, it extracts heart rate variability features before the onset of SCA and utilizes a lightweight convolutional neural network model for pre-training and fine-tuning in two stages of deep transfer learning. This achieves early classification, recognition and prediction of high-risk ECG signals for SCA by neural network models. Based on 16 788 30-second heart rate feature segments from 20 SCA patients and 18 sinus rhythm patients in the international publicly available ECG database, the algorithm performance evaluation through ten-fold cross-validation shows that the average accuracy (Acc), sensitivity (Sen), and specificity (Spe) for predicting the onset of SCA in the 30 minutes prior to the event are 91.79%, 87.00%, and 96.63%, respectively. The average estimation accuracy for different patients reaches 96.58%. Compared to traditional machine learning algorithms reported in existing literatures, the method proposed in this paper helps address the requirement of large training datasets for deep learning models and enables early and accurate detection and identification of high-risk ECG signs before the onset of SCA.

Genome-Wide Interaction Analyses of Serum Calcium on Ventricular Repolarization Time in 125 393 Participants.

BACKGROUND: Ventricular repolarization time (ECG QT and JT intervals) is associated with malignant arrhythmia. Genome-wide association studies have identified 230 independent loci for QT and JT; however, 50% of their heritability remains unexplained. Previous work supports a causal effect of lower serum calcium concentrations on longer ventricular repolarization time. We hypothesized calcium interactions with QT and JT variant associations could explain a proportion of the missing heritability. METHODS AND RESULTS: We performed genome-wide calcium interaction analyses for QT and JT intervals. Participants were stratified by their calcium level relative to the study distribution (top or bottom 20%). We performed a 2-stage analysis (genome-wide discovery [N=62 532] and replication [N=59 861] of lead variants) and a single-stage genome-wide meta-analysis (N=122 393, [European ancestry N=117 581, African ancestry N=4812]). We also calculated 2-degrees of freedom joint main and interaction and 1-degree of freedom interaction P values. In 2-stage and single-stage analyses, 50 and 98 independent loci, respectively, were associated with either QT or JT intervals (2-degrees of freedom joint main and interaction P value <5×10-8). No lead variant had a significant interaction result after correcting for multiple testing and sensitivity analyses provided similar findings. Two loci in the single-stage meta-analysis were not reported previously (SPPL2B and RFX6). CONCLUSIONS: We have found limited support for an interaction effect of serum calcium on QT and JT variant associations despite sample sizes with suitable power to detect relevant effects. Therefore, such effects are unlikely to explain a meaningful proportion of the heritability of QT and JT, and factors including rare variation and other environmental interactions need to be considered.

Electrophysiological Mechanism of Catestatin Antiarrhythmia: Enhancement of Ito, IK, and IK1 and Inhibition of ICa-L in Rat Ventricular Myocytes.

BACKGROUND: Cardiovascular disease remains one of the leading causes of death globally. Myocardial ischemia and infarction, in particular, frequently cause disturbances in cardiac electrical activity that can trigger ventricular arrhythmias. We aimed to investigate whether catestatin, an endogenous catecholamine-inhibiting peptide, ameliorates myocardial ischemia-induced ventricular arrhythmias in rats and the underlying ionic mechanisms. METHODS AND RESULTS: Adult male Sprague-Dawley rats were randomly divided into control and catestatin groups. Ventricular arrhythmias were induced by ligation of the left anterior descending coronary artery and electrical stimulation. Action potential, transient outward potassium current, delayed rectifier potassium current, inward rectifying potassium current, and L-type calcium current (ICa-L) of rat ventricular myocytes were recorded using a patch-clamp technique. Catestatin notably reduced ventricular arrhythmia caused by myocardial ischemia/reperfusion and electrical stimulation of rats. In ventricular myocytes, catestatin markedly shortened the action potential duration of ventricular myocytes, which was counteracted by potassium channel antagonists TEACl and 4-AP, and ICa-L current channel agonist Bay K8644. In addition, catestatin significantly increased transient outward potassium current, delayed rectifier potassium current, and inward rectifying potassium current density in a concentration-dependent manner. Catestatin accelerated the activation and decelerated the inactivation of the transient outward potassium current channel. Furthermore, catestatin decreased ICa-L current density in a concentration-dependent manner. Catestatin also accelerated the inactivation of the ICa-L channel and slowed down the recovery of ICa-L from inactivation. CONCLUSIONS: Catestatin enhances the activity of transient outward potassium current, delayed rectifier potassium current, and inward rectifying potassium current, while suppressing the ICa-L in ventricular myocytes, leading to shortened action potential duration and ultimately reducing the ventricular arrhythmia in rats.

Reduced connexin-43 expression, slow conduction and repolarisation dispersion in a model of hypertrophic cardiomyopathy.

Hypertrophic cardiomyopathy (HCM) is an inherited heart muscle disease that is characterised by left ventricular wall thickening, cardiomyocyte disarray and fibrosis, and is associated with arrhythmias, heart failure and sudden death. However, it is unclear to what extent the electrophysiological disturbances that lead to sudden death occur secondary to structural changes in the myocardium or as a result of HCM cardiomyocyte electrophysiology. In this study, we used an induced pluripotent stem cell model of the R403Q variant in myosin heavy chain 7 (MYH7) to study the electrophysiology of HCM cardiomyocytes in electrically coupled syncytia, revealing significant conduction slowing and increased spatial dispersion of repolarisation - both well-established substrates for arrhythmia. Analysis of rhythmonome protein expression in MYH7 R403Q cardiomyocytes showed reduced expression of connexin-43 (also known as GJA1), sodium channels and inward rectifier potassium channels - a three-way hit that reduces electrotonic coupling and slows cardiac conduction. Our data represent a previously unreported, biophysical basis for arrhythmia in HCM that is intrinsic to cardiomyocyte electrophysiology. Later in the progression of the disease, these proarrhythmic phenotypes may be accentuated by myocyte disarray and fibrosis to contribute to sudden death.

[Arrhythmia in sleep apnea]. / Arrhythmien bei Schlafapnoe.

BACKGROUND: Sleep apnea is a widespread and yet still underdiagnosed condition. Various studies from the past have provided evidence that there is a link between sleep apnea and various cardiovascular diseases, including arrhythmias. OBJECTIVE: The aim of this article is to provide an overview of the current study situation and to point out possible consequences relevant to everyday life. MATERIAL AND METHODS: A systematic search was carried out in various databases using the keywords sleep apnea (OSAS/SA) and arrhythmias/dysrhythmias. RESULTS: There are several pathophysiological links between sleep-related breathing disorders and cardiac arrhythmias, the most important of which appear to be intrathoracic pressure, increased adrenergic tone as well as recurrent hypoxia and hypercapnia. This results in an increased occurrence of clinically relevant arrhythmias, such as atrial fibrillation, symptomatic bradycardia, high-grade atrioventricular (AV) blocks as well as ventricular arrhythmias in patients with untreated sleep apnea. These pathologies also appear to be positively influenced by the treatment of sleep apnea. CONCLUSION: A close correlation between sleep apnea and cardiac arrhythmias is undisputed. Large randomized studies in this respect are so far rare but it is undisputed that a thorough search should be carried out for sleep apnea and consistently treated in patients with a history of cardiac disease as this can have a relevant influence on the treatment and ultimately the prognosis of the patient.

Predictors and clinical outcomes of permanent pacemaker implantation after transcatheter aortic valve implantation.

OBJECTIVE: This study aimed to identify the incidence, risk factors, and outcomes of permanent pacemaker (PPM) implantation after transcatheter aortic valve implantation (TAVI) procedures. METHODS: A retrospective analysis was conducted on 70 patients who underwent TAVI at the Department of Cardiology, Fujian Provincial Hospital, from January 2018 to March 2022. Based on whether a new PPM was implanted after TAVI, all patients were divided into two groups: NEW PPM and NO PPM. Baseline characteristics and clinical data were compared between the two groups. Univariate analysis was used to analyze different variables between the two groups. A binary logistic regression analysis was used to evaluate independent correlates for PPM implantation after TAVI. RESULTS: The mean age of the 70 patients was 73.1 ± 8.8 years. The incidence of PPM implantation was 17.1%. Patients with diabetes and chronic kidney disease were more likely to require PPM (50% vs. 20.7%, p = 0.042, 25% vs. 5.2%, p = 0.042). Our study did not identify any significant differences in the incidence of electrocardiographic conduction disturbances except for the previous right bundle branch block (RBBB) (NO PPM 6.9% vs. NEW PPM 33.3%, p < 0.05). We found that prosthesis size, implantation depth, procedural duration, and length of hospital and intensive care unit (ICU) stays were comparable between the two groups. The leading independent predictors of PPM implantation were previous RBBB (odds ratio 10.129, p = 0.034). CONCLUSION: The previous RBBB was the leading independent predictor of PPM implantation. New PPM was not associated with significantly new-onset left BBB, extended post-procedure hospitalization, ICU stay, or procedural duration.

Data Analysis of Heart Rate Variability and Arrhythmia in Patients with Paroxysmal Atrial Fibrillation.

BACKGROUND: Atrial fibrillation (AF) is the most common type of arrhythmia. Heart rate variability (HRV) may be associated with AF risk. The aim of this study was to test HRV indices and arrhythmias as predictors of paroxysmal AF based on 24-hour dynamic electrocardiogram recordings of patients. METHODS: A total of 199 patients with paroxysmal AF (AF group) and 204 elderly volunteers over 60 years old (Control group) who underwent a 24-hour dynamic electrocardiogram from August 2022 to March 2023 were included. Time-domain indices, frequency-domain indices, and arrhythmia data of the two groups were classified and measured. Binary logistic regression analysis was performed on variables with significant differences to identify independent risk factors. A nomogram prediction model was established, and the sum of individual scores of each variable was calculated. RESULTS: Gender, age, body mass index and low-density lipoprotein (LDL) did not differ significantly between AF and Control groups (p > 0.05), whereas significant group differences were found for smoking, hypertension, diabetes, and high-density lipoprotein (HDL) (p < 0.05). The standard deviation of all normal to normal (NN) R-R intervals (SDNN), standard deviation of 5-minute average NN intervals (SDANN), root mean square of successive NN interval differences (rMSSD), 50 ms from the preceding interval (pNN50), low-frequency/high-frequency (LF/HF), LF, premature atrial contractions (PACs), atrial tachycardia (AT), T-wave index, and ST-segment index differed significantly between the two groups. Logistic regression analysis identified rMSSD, PACs, and AT as independent predictors of AF. For each unit increase in rMSSD and PACs, the odds of developing AF increased by 1.0357 and 1.0005 times, respectively. For each unit increase in AT, the odds of developing AF decreased by 0.9976 times. The total score of the nomogram prediction model ranged from 0 to 110. CONCLUSION: The autonomic nervous system (ANS) plays a pivotal role in the occurrence and development of AF. The individualized nomogram prediction model of AF occurrence contributes to the early identification of high-risk patients with AF.

Cardiac Arrhythmias in Pulmonary Arterial Hypertension and Chronic Thromboembolic Pulmonary Hypertension: Mechanistic Insights, Pathophysiology, and Outcomes.

Arrhythmias are increasingly recognized as severe complications of precapillary pulmonary hypertension, encompassing pulmonary arterial hypertension (PAH) and chronic thromboembolic pulmonary hypertension (CTEPH). Despite their significant contribution to symptoms, morbidity, in-hospital mortality, and potentially sudden death in PAH/CTEPH, there remains a lack of comprehensive data on epidemiology, pathophysiology, and outcomes to inform the management of these patients. This review provides an overview of the latest evidence on this subject, spanning from the molecular mechanisms underlying arrhythmias in the hypertrophied or failing right heart to the clinical aspects of epidemiology, diagnosis, and treatment.

A novel diagnosis method combined dual-channel SE-ResNet with expert features for inter-patient heartbeat classification.

As the number of patients with cardiovascular diseases (CVDs) increases annually, a reliable and automated system for detecting electrocardiogram (ECG) abnormalities is becoming increasingly essential. Scholars have developed numerous methods of arrhythmia classification using machine learning or deep learning. However, the issue of low classification rates of individual classes in inter-patient heartbeat classification remains a challenge. This study proposes a method for inter-patient heartbeat classification by fusing dual-channel squeeze-and-excitation residual neural networks (SE-ResNet) and expert features. In the preprocessing stage, ECG heartbeats extracted from both leads of ECG signals are filtered and normalized. Additionally, nine features representing waveform morphology and heartbeat contextual information are selected to be fused with the deep neural networks. Using different filter and kernel sizes for each block, the SE-residual block-based model can effectively learn long-term features between heartbeats. The divided ECG heartbeats and extracted features are then input to the improved SE-ResNet for training and testing according to the inter-patient scheme. The focal loss is utilized to handle the heartbeat of the imbalance category. The proposed arrhythmia classification method is evaluated on three open-source databases, and it achieved an overall F1-score of 83.39 % in the MIT-BIH database. This system can be applied in the scenario of daily monitoring of ECG and plays a significant role in diagnosing arrhythmias.

Leadwise clustering multi-branch network for multi-label ECG classification.

The 12-lead electrocardiogram (ECG) is widely used for diagnosing cardiovascular diseases in clinical practice. Recently, deep learning methods have become increasingly effective for automatically classifying ECG signals. However, most current research simply combines the 12-lead ECG signals into a matrix without fully considering the intrinsic relationships between the leads and the heart's structure. To better utilize medical domain knowledge, we propose a multi-branch network for multi-label ECG classification and introduce an intuitive and effective lead grouping strategy. Correspondingly, we design multi-branch networks where each branch employs a multi-scale convolutional network structure to extract more comprehensive features, with each branch corresponding to a lead combination. To better integrate features from different leads, we propose a feature weighting fusion module. We evaluate our method on the PTB-XL dataset for classifying 4 arrhythmia types and normal rhythm, and on the China Physiological Signal Challenge 2018 (CPSC2018) database for classifying 8 arrhythmia types and normal rhythm. Experimental results on multiple multi-label datasets demonstrate that our proposed multi-branch network outperforms state-of-the-art networks in multi-label classification tasks.

In situ assembly of an injectable cardiac stimulator.

Without intervention, cardiac arrhythmias pose a risk of fatality. However, timely intervention can be challenging in environments where transporting a large, heavy defibrillator is impractical, or emergency surgery to implant cardiac stimulation devices is not feasible. Here, we introduce an injectable cardiac stimulator, a syringe loaded with a nanoparticle solution comprising a conductive polymer and a monomer that, upon injection, forms a conductive structure around the heart for cardiac stimulation. Following treatment, the electrode is cleared from the body, eliminating the need for surgical extraction. The mixture adheres to the beating heart in vivo without disrupting its normal rhythm. The electrofunctionalized injectable cardiac stimulator demonstrates a tissue-compatible Young's modulus of 21 kPa and a high conductivity of 55 S/cm. The injected electrode facilitates electrocardiogram measurements, regulates heartbeat in vivo, and rectifies arrhythmia. Conductive functionality is maintained for five consecutive days, and no toxicity is observed at the organism, organ, or cellular levels.

Cardiac arrhythmias in fish induced by natural and anthropogenic changes in environmental conditions.

A regular heartbeat is essential for maintaining the homeostasis of the vertebrate body. However, environmental pollutants, oxygen deficiency and extreme temperatures can impair heart function in fish. In this Review, we provide an integrative view of the molecular origins of cardiac arrhythmias and their functional consequences, from the level of ion channels to cardiac electrical activity in living fish. First, we describe the current knowledge of the cardiac excitation-contraction coupling of fish, as the electrical activity of the heart and intracellular Ca2+ regulation act as a platform for cardiac arrhythmias. Then, we compile findings on cardiac arrhythmias in fish. Although fish can experience several types of cardiac arrhythmia under stressful conditions, the most typical arrhythmia in fish - both under heat stress and in the presence of toxic substances - is atrioventricular block, which is the inability of the action potential to progress from the atrium to the ventricle. Early and delayed afterdepolarizations are less common in fish hearts than in the hearts of endotherms, perhaps owing to the excitation-contraction coupling properties of the fish heart. In fish hearts, Ca2+-induced Ca2+ release from the sarcoplasmic reticulum plays a smaller role than Ca2+ influx through the sarcolemma. Environmental changes and ion channel toxins can induce arrhythmias in fish and weaken their tolerance to environmental stresses. Although different from endotherm hearts in many respects, fish hearts can serve as a translational model for studying human cardiac arrhythmias, especially for human neonates.

In Vivo Approaches to Understand Arrhythmogenic Cardiomyopathy: Perspectives on Animal Models.

Arrhythmogenic cardiomyopathy (AC) is a hereditary cardiac disorder characterized by the gradual replacement of cardiomyocytes with fibrous and adipose tissue, leading to ventricular wall thinning, chamber dilation, arrhythmias, and sudden cardiac death. Despite advances in treatment, disease management remains challenging. Animal models, particularly mice and zebrafish, have become invaluable tools for understanding AC's pathophysiology and testing potential therapies. Mice models, although useful for scientific research, cannot fully replicate the complexity of the human AC. However, they have provided valuable insights into gene involvement, signalling pathways, and disease progression. Zebrafish offer a promising alternative to mammalian models, despite the phylogenetic distance, due to their economic and genetic advantages. By combining animal models with in vitro studies, researchers can comprehensively understand AC, paving the way for more effective treatments and interventions for patients and improving their quality of life and prognosis.