Different Types Of Arrhythmias Research Articles

Non-invasive detection of cardiac arrhythmias using photoplethysmography signals

Abstract Cardiac arrhythmias (CAs) are associated with critical heart-related complications such as stroke or heart failure. Because of the intermittent and asymptomatic presentation of some CAs in their early stages, the screening remains limited using traditional methods based on ECG. Recent advances in photoplethysmography (PPG) have revealed substantial potential for wearable devices to detect CAs within large populations. Although PPG has demonstrated efficiency in discriminating atrial fibrillation (AF) from normal sinus rhythm, it remains unclear whether AF detection methods remains effective in the presence of CAs other than AF. In this study, we applied a recurrent neural network on a dataset containing eight different types of CAs (Table 1). The classifier processes sequences of interbeat intervals (IBIs) as input and discriminates between normal and abnormal rhythms. The algorithm was evaluated on 64 patients (45 males and 19 females , with a mean age of 55.9 ± 16.0) undergoing a diagnostic or therapeutic electrophysiological procedure. This dataset includes simultaneous recordings of PPG signals from a wrist-bracelet and 12-lead ECG, with the latter used as the gold-standard for annotating cardiac rhythms. The classifier achieved 84% accuracy, 77% sensitivity and 88% specificity in detecting abnormal rhythms. Table 1 shows that AF (99.6%), atrial tachycardia (100%) and AVRT (96.4%) were well classified as abnormal rhythm, whereas the reliability of the detection decreased for atrial flutter (65.4%), atrial or ventricular bigeminy (72.4%) and ventricular tachycardia (80.2%). Undetected abnormal rhythms were often characterized by a rather regular rhythm (illustrated by VT in Figure 1) and CAs for which PPG heartbeat detection was initially not optimal (Bigeminy in Figure 1). In conclusion, this study shows the capability of PPG-based technology to detect various CAs extending beyond AF. It highlights the merits and limitations of IBI-based detection of abnormal rhythms, highlighting the need for a better comprehension of the peripheral hemodynamic signature of CAs.

Demographics, clinical features and genetics of inherited arrhythmias in Oman

Abstract Background Inherited arrhythmia syndromes (IAS) are a group of rare disorders that result from genetic mutations in several genes encoding ion channels causing disruption in its structure and function and predispose affected individuals to various symptoms including sudden cardiac death (SCD). They include congenital long QT syndrome, Brugada syndrome (BrS), progressive cardiac conduction disease, short QT syndrome and Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT). There have been few reports of long QT syndrome from the Arabian Gulf region but this is the first comprehensive review of multiple inherited arrhythmia syndromes in the region. Objectives Our objective are to describe the demographics of children and adults with inherited arrhythmia syndromes, report the presenting clinical features, genetic mutations and management strategies. Also, to report some unique and rare genetic findings in our population. Methods This is a descriptive retrospective study that included children and adults with inherited arrhythmia syndromes diagnosed and treated at a national heart center over 16 years, between 2006 to 2022. Data collected included patient demographics, geographical distribution, clinical features, genetic reports and management strategies. Result One hundred and six patients were included in the analysis. Sixty-six (62.3%) were males, and 68 (64.2%) were adults and 38 (35.8%) were children. Three inherited arrhythmia syndromes were found in our population that included Long QT, Brugada and catecholaminergic polymorphic ventricular tachycardia (CPVT), which accounted for 58 (54.7%), 39 (36.8%) and 9 (8.5%), respectively. Seventy-six (71.7%) of the patients were reported to be from consanguineous families. Most children, 31 (91.4%) had long QT, whereas most adults, 39(54.9%) had Brugada. The clinical features varied based on the type of arrhythmia. There were 28(26.4%) asymptomatic individuals who were diagnosed incidentally. Syncope was the most common presenting symptom in patients with Long QT, 24(41.4%) and Brugada syndrome, 20(51.3%). Palpitations, 4(44.4%) and bradycardia, 4(44.4%) were the predominant presenting features in patients with CPVT. The treatment modalities included beta blockers and antiarrhythmics, implantable cardioversion defibrillators(ICDs), pacemakers, and left sympathetic ganglionectomy(LSGs). We identified 36 individuals with pathogenic or likely pathogenic variants and 12 VOUS(variants of uncertain significance) in different genes of IAS. Conclusion This is the first comprehensive study on the inherited arrhythmia syndromes in Oman and the Arabian Peninsula. We provided insights about demographics, clinical, genetic profile and the treatment strategies of the most common IAS in the region. Tis study hat will help in early detection of the different types of arrhythmias and early intervention to prevent sudden cardiac death in patients and their relatives.

In-hospital mortality following catheter ablation procedures in Germany: insights from the VARY project

Abstract Background The number of catheter ablation (CA) procedures has increased significantly over the last decade. 102,737 CA have been performed in Germany in 2021. Although CA of different types of arrhythmias is generally considered to be safe, serious complications, including in-hospital death, may occur. There is a paucity of real-world national healthcare data in Germany. Aim To investigate the overall in-hospital mortality in patients undergoing any type of CA procedure in Germany and to identify variables associated with in-hospital mortality. Methods According to the German law German hospitals are obliged to provide a dataset including case data, diagnosis based on the ICD-10-GM, operational and procedural codes to the Institute for hospital remuneration system (InEK) every year. The VARY project aims at analyzing and monitoring these data to report the real world scenario on patients undergoing CA in Germany. As of now, 22 German centers were contacted by the Institute for Myocardial Infarction Research (IHF). Their Data between 2019 and 2023 provided from seven of those centers were used for this analysis. Datasets from ten centers are pending. Five centers were not able to provide their data. The project is supported by the German Heart Foundation. Results A total 21,028 patients underwent inpatient CA. 49 patients (69.4% males) died in-hospital following a CA procedure resulting in an overall in-hospital mortality rate of 0.23%. A percutaneous epicardial ablation approach was used in 6.1% of the patients who died (Group A) compared to 0.3% in the surviving patients (Group B). Radiofrequency ablation was used in 83.7% vs. 72.6%, cryoablation in 10.2% vs 27.1%. Patients who died were older compared to those who survived (mean age: 66.7 ± 14.6 vs. 62.0 ± 14.4 years, p <0.01) and had more comorbidities such as diabetes (19/49 (38.8.%) vs. 2274/20,979 (10.8%), p <0,01), chronic obstructive pulmonary disease (5/49 (10.2%) vs. 555/20,979 (2.6%), p < 0,01) and heart failure (39/49 (79.6%) vs. 5116/20,979 (24.4%), p <0.01). There was no difference with regards to gender between the two groups. The most common arrhythmia in both groups was atrial fibrillation with 67.3% and 75.3% (p < 0.01). Cardiac arrest was observed in 44.9% of patients in group A compared to only 0.3% in group B. The median hospital stay was longer in group A (19.0 (IQR 8.0; 42.0) vs. 2.0 (IQR 1.0; 3.0), p < 0.01). 46/49 (93.9%) patients who died were treated on an intensive care unit for a median time of 13.5 days (IQR 3.8; 33.8) compared to 1272/20,979 (6.1%) (p < 0.01) in group B. A cardiac implantable electronic device implantation or revision was performed in 18.4% patients in group A (4 defibrillator implantations, 2 pacemaker implantations, 5 revisions) compared to 2.9% in group B (survivors). Conclusion Inpatient catheter ablation procedures in Germany are associated with a very low in-hospital mortality comparable to data reported from the US.

Classification Method of ECG Signals Based on RANet.

Electrocardiograms (ECG) are an important source of information on human heart health and are widely used to detect different types of arrhythmias. With the advancement of deep learning, end-to-end ECG classification models based on neural networks have been developed. However, deeper network layers lead to gradient vanishing. Moreover, different channels and periods of an ECG signal hold varying significance for identifying different types of ECG abnormalities. To solve these two problems, an ECG classification method based on a residual attention neural network is proposed in this paper. The residual network (ResNet) is used to solve the gradient vanishing problem. Moreover, it has fewer model parameters, and its structure is simpler. An attention mechanism is added to focus on key information, integrate channel features, and improve voting methods to alleviate the problem of data imbalance. Experiments and verifications are conducted using the PhysioNet/CinC Challenge 2017 dataset. The average F1 value is 0.817, which is 0.064 higher than that for the ResNet model. Compared with the mainstream methods, the performance is excellent.

Person identification with arrhythmic ECG signals using deep convolution neural network

Over the past decade, the use of biometrics in security systems and other applications has grown in popularity. ECG signals in particular are attracting increased attention due to their characteristics, which are required for a trustworthy identification system. The majority of ECG-based person identification systems are evaluated without considering the health-state of the individuals. Few person identification systems consider person-by-person health-state annotation. This paper proposes a person identification system considering the health-state annotated ECG signals where each person’s beats overlap among variant arrhythmia classes. This overlapping between the normal class and other arrhythmia classes grants the ability to isolate normal beats in the train set from the Arrhythmic beats in the test set. Therefore, this paper investigates the effect of arrhythmic heartbeats on biometric recognition. An effective lightweight CNN based on depth-wise separable convolution (DWSC) is proposed to enhance the performance of person identification for several common arrhythmia types using the MITBIH dataset. The proposed methodology has been tested on nine arrhythmia types and presents how different types of arrhythmia affect ECG-based biometric systems differently. The experimental results show excellent recognition performance (99.28%) on normal heartbeats and (93.81%) on arrhythmic heartbeats, outperforming other models in terms of mean accuracy.

Polymorphic Variants of SCN5A Gene (rs41312433 and rs1805124) Associated with Coronary Artery Affliction in Patients with Severe Arrhythmias.

A group of 32 unrelated individuals, aged 63 ± 12 years, was included in the study. All the patients were examined, diagnosed and treated with an implantable cardioverter defibrillator at the Department of Internal Cardiology Medicine, Faculty Hospital Brno. The control group included 87 persons of similar age without afflicted coronary circulation, which was confirmed coronagraphically. Genomic DNA was extracted from samples of peripheral blood according to the standard protocol. Two SCN5A polymorphisms-IVS9-3C/A (rs41312433) and A1673G (rs1805124, H558R)-were examined in association with coronary artery stenosis in the patients. In the case-control study, no significant differences in genotype distribution/allelic frequencies were observed for IVS9-3c>a and A1673G gene polymorphisms between patients with severe arrhythmias and healthy persons. The distribution of SCN5A double genotypes was not significantly different among different types of arrhythmias according to their ejection fraction in arrhythmic patients (p = 0.396). The ventricular arrhythmias with an ejection fraction below 40% were found to be 10.67 times more frequent in patients with multiple coronary stenosis with clinically valid sensitivity, specificity and power tests. In the genotype-phenotype study, we observed a significant association of both SCN5A polymorphisms with the stenosis of coronary vessels in the patients with severe arrhythmia. The double genotype of polymorphisms IVS9-3C/A together with A1673G (CCAA) as well as their simple genotypes were associated with significant multiple stenosis of coronary arteries (MVS) with high sensitivity and specificity (p = 0.05; OR = 5 (95% CI 0.99-23.34); sensitivity 0.70; specificity 0.682; power test 0.359) Moreover, when a concrete stenotic coronary artery was associated with SCN5A genotypes, the CCAA double genotype was observed to be five times more frequent in patients with significant stenosis in the right coronary artery (RCA) compared to those without affliction of this coronary artery (p = 0.05; OR = 5 (95% CI 0.99-23.34); sensitivity 0.682; specificity 0.700; power test 0.359). The CCAA genotype was also more frequent in patients without RCA affliction with MVS (p = 0.008); in patients with ACD affliction but without MVS (p = 0.008); and in patients with both ACD affliction and MVS compared to those without ACD affliction and MVS (p = 0.005). Our study presents a highly sensitive and specific association of two polymorphisms in SCN5A with significant coronary artery stenoses in patients with potentially fatal ventricular arrhythmias. At the same time, these polymorphisms were not associated with arrhythmias themselves. Thus, SCN5A gene polymorphic variants may form a part of germ cell gene predisposition to ischemia.

The influence of cardiac arrhythmias on the detection of heartbeats in the photoplethysmogram: benchmarking open-source algorithms.

Objective.Cardiac arrhythmias are a leading cause of mortality worldwide. Wearable devices based on photoplethysmography give the opportunity to screen large populations, hence allowing for an earlier detection of pathological rhythms that might reduce the risks of complications and medical costs. While most of beat detection algorithms have been evaluated on normal sinus rhythm or atrial fibrillation recordings, the performance of these algorithms in patients with other cardiac arrhythmias, such as ventricular tachycardia or bigeminy, remain unknown to date.Approach.ThePPG-beatsopen-source framework, developed by Charlton and colleagues, evaluates the performance of the beat detectors namedQPPG,MSPTDandABDamong others. We applied thePPG-beatsframework on two newly acquired datasets, one containing seven different types of cardiac arrhythmia in hospital settings, and another dataset including two cardiac arrhythmias in ambulatory settings.Main Results.In a clinical setting, theQPPGbeat detector performed best on atrial fibrillation (with a medianF1score of 94.4%), atrial flutter (95.2%), atrial tachycardia (87.0%), sinus rhythm (97.7%), ventricular tachycardia (83.9%) and was ranked 2nd for bigeminy (75.7%) behindABDdetector (76.1%). In an ambulatory setting, theMSPTDbeat detector performed best on normal sinus rhythm (94.6%), and theQPPGdetector on atrial fibrillation (91.6%) and bigeminy (80.0%).Significance.Overall, the PPG beat detectorsQPPG,MSPTDandABDconsistently achieved higher performances than other detectors. However, the detection of beats from wrist-PPG signals is compromised in presence of bigeminy or ventricular tachycardia.

Advancements in Artificial Intelligence for ECG Signal Analysis and Arrhythmia Detection: A Review

Context: With the widespread availability of portable electrocardiogram (ECG) devices, there is an increasing interest in utilizing artificial intelligence (AI) methods for ECG signal analysis and arrhythmia detection. The potential benefits of AI-assisted arrhythmia prognosis, early screening, and improved accuracy in arrhythmia classification are discussed. Evidence Acquisition: Artificial intelligence methods are a new way to classify different types of arrhythmias. For example, deep learning (DL) algorithms, including long short-term memory (LSTM) networks, convolutional neural networks (CNN), CNN-based autoencoders (AE), and convolutional recurrent neural networks (CRNN), have been extensively utilized for ECG signal analysis and arrhythmia detection. Results: This study explores different DL techniques for classifying arrhythmias. The two-dimensional (2D) CNN model achieved an accuracy of 97.42% in classifying five different arrhythmias. After classifying five types of ECG signals, an accuracy of 99.53% was achieved by the CNN-based AE and transfer learning (TL) models. The CNN-Bi-LSTM model achieved an accuracy of 98.0% in categorizing five categories of ECG signals. The CNN+LSTM model achieved an accuracy of 98.24% in classifying five classes of arrhythmias. The CNN-support vector machine (SVM) classifier model demonstrated an accuracy of 98.64% in detecting seventeen classes of heartbeats. The results indicated that the CNN-based AE and TL models perform exceptionally well with high accuracy in detecting ECG signals. Conclusions: The present study demonstrates the growing interest in utilizing DL for ECG signal detection in medical and healthcare applications over the past decade. Deep learning models have been shown to outperform experienced cardiologists, delivering state-of-the-art and more accurate results.

Attention-assisted hybrid CNN-BILSTM-BiGRU model with SMOTE-Tomek method to detect cardiac arrhythmia based on 12-lead electrocardiogram signals.

Cardiac arrhythmia is one of the most severe cardiovascular diseases that can be fatal. Therefore, its early detection is critical. However, detecting types of arrhythmia by physicians based on visual identification is time-consuming and subjective. Deep learning can develop effective approaches to classify arrhythmias accurately and quickly. This study proposed a deep learning approach developed based on a Chapman-Shaoxing electrocardiogram (ECG) dataset signal to detect seven types of arrhythmias. Our DNN model is a hybrid CNN-BILSTM-BiGRU algorithm assisted by a multi-head self-attention mechanism regarding the challenging problem of classifying various arrhythmias of ECG signals. Additionally, the synthetic minority oversampling technique (SMOTE)-Tomek technique was utilized to address the data imbalance problem to detect and classify cardiac arrhythmias. The proposed model, trained with a single lead, was tested using a dataset containing 10,466 participants. The performance of the algorithm was evaluated using a random split validation approach. The proposed algorithm achieved an accuracy of 98.57% by lead II and 98.34% by lead aVF for the classification of arrhythmias. We conducted an analysis of single-lead ECG signals to evaluate the effectiveness of our proposed hybrid model in diagnosing and classifying different types of arrhythmias. We trained separate classification models using each individual signal lead. Additionally, we implemented the SMOTE-Tomek technique along with cross-entropy loss as a cost function to address the class imbalance problem. Furthermore, we utilized a multi-headed self-attention mechanism to adjust the network structure and classify the seven arrhythmia classes. Our model achieved high accuracy and demonstrated good generalization ability in detecting ECG arrhythmias. However, further testing of the model with diverse datasets is crucial to validate its performance.

INDUCTION OF CARDIAC ARRHYTHMIAS DURING BICYCLE ERGOMETRY IN THE DIAGNOSIS OF CORONARY HEART DISEASE

HighlightsThe frequency of arrhythmias during bicycle ergometry was compared in patients with and without coronary heart disease. Their diagnostic significance in diagnosing this disease was analyzed separately for men and women. AbstractAim. To determine the diagnostic significance of exercise-induced cardiac arrhythmias (CAs) during bicycle ergometry (BEM) in patients with suspected coronary heart disease (CHD).Methods. For the retrospective study, 384 case histories of patients discharged from the cardiology department between 2011 and 2020 were selected according to the inclusion and exclusion criteria. The data of BEM and coronary angiography were studied. The correlation between the occurrence of induction of different types of CAs during BEM and the presence of coronary artery blood flow abnormalities was investigated. Indicators of the diagnostic significance of CAs in the diagnosis of coronary artery disease, such as sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) results, were determined separately for men and women. The statistical significance of the results was assessed.Results. Statistically significant specificity and PPV for the induction of ventricular arrhythmias (VAs) without taking into account rare single monomorphic ventricular extrasystoles (VEs) in the diagnosis of CHD in men were 83.5% and 73.1%, respectively. The same indicators for VAs, excluding all single VEs, were 95.3% and 82.6% in men.Conclusion. The induction of VAs in men on BEM, with the exception of rare single monomorphic VEs, is diagnostically significant in the detection of CHD and allows us to qualify the result of BEM as “positive”. The induction of other CAs on BEM has not shown diagnostic significance in the detection of CHD.

Rare-variant collapsing and bioinformatic analyses for different types of cardiac arrhythmias in the UK Biobank reveal novel susceptibility loci and candidate amyloid-forming proteins

Cardiac arrhythmias are a common health problem. Both common and rare genetic risk factors exist for cardiac arrhythmias. Cardiac amyloidosis is a rare disease that may manifest various arrhythmias. Few large-scale whole exome sequencing studies elucidating the contribution of rare variations to arrhythmias have been published. To access gene collapsing analysis of rare variations for different types of cardiac arrhythmias in UK Biobank. Identified genes were analyzed in silico for probability to form amyloid fibrils. We used 2 published UK Biobank portals (https://azphewas.com/ and https://app.genebass.org/) to access gene collapsing analysis of rare variations for different types of cardiac arrhythmias. Diagnosis of arrhythmia was based on the International Classification of Diseases, 10th Revision (ICD-10) codes: conduction disorders (I44, I45), paroxysmal tachycardia (I47), atrial fibrillation (I48), and other arrhythmias (I49). Rare variations in 5 genes were linked to conduction disorders (SCN5A, LMNA, SMAD6, HSPB9, TMEM95). The TTN gene was associated with both paroxysmal tachycardia and other arrhythmias. Atrial fibrillation was associated with rare variations in 8 genes (TTN, RPL3L, KLF1, TET2, NME3, KDM5B, PKP2, PMVK). Two of the genes linked to heart conduction disorders were potential amyloid-forming proteins (HSPB9, TMEM95), while none of the 8 genes linked to other types of arrhythmias were potential amyloid-forming proteins. Rare variations in 13 genes were associated with arrhythmias in the UK Biobank. Two of the heart conduction disorder-linked genes are potential amyloid-forming candidates. Amyloid formation may be an underestimated cause of heart conduction disorders.

Abstract 18257: Arrhythmias in Tumor Lysis Syndrome

Background: Tumor lysis syndrome (TLS) is a common complication seen among hospitalized patients with acute promyelocytic leukemia (APL) undergoing induction therapy. Question: What is the incidence and type of arrhythmias in patients with TLS? Methods: This study utilized the National Inpatient Sample Database between 2015-2019 to identify patients hospitalized with APL and further subclassify them into those with and without TLS. ICD-10 codes were used to identify different types of arrhythmias in both groups. A multivariate logistic regression model was constructed to assess adjusted odds ratio for all-cause mortality and cardiac/renal complications. Results: A total of 9938 hospitalizations with APL were identified. Of those, 415 patients had TLS (4.2%). APL patients with TLS were older and more likely to be males. CVA, CHF, CAD, HTN and higher comorbidity scores were more frequent among patients with TLS (Table 1). On adjusted analysis, Patients with TLS had ~2.7-fold increase in all-cause mortality (aOR 2.74, 95% CI [2.12 - 3.55], p< 0.01). In addition, TLS patients had higher rates of cardiac arrhythmias (33.7% vs 21.4%, p<0.01). Atrial fibrillation was the most common arrhythmia (45%), followed by ventricular arrhythmias (11.3%), and lastly, torsade de pointes (8.2%). Finally, TLS patients were more likely to have AKI (aOR 4.13, 95% [3.31 - 5.16], and need hemodialysis (aOR 3.44, 95% CI [2.34 - 5.05], p<0.01). Conclusion: Patients with TLS are at high risk for arrhythmias, all-cause mortality and cardiac morbidity. Preventive strategies like continuous telemetry monitoring may allow for early identification of arrhythmias and may help improve outcomes.

A Context-Responsive LSTM based IoT Enabled E- Healthcare Monitoring System for Arrhythmia Detection

Detecting Arrhythmia, a life-threatening cardiac condition, in real-time is crucial for timely intervention and improved healthcare outcomes. Traditional manual methods for Arrhythmia detection using Electrocardiogram (ECG) signals are error-prone and resource-intensive. To address these limitations, this paper presents an automated system based on the Context Responsive Long Short-Term Memory (CR-LSTM) model for real-time Arrhythmia classification. The system leverages IoT technology to continuously monitor vital signs and effectively combines contextual information with temporal sensor data to accurately discern different types of Arrhythmias. The CR-LSTM model achieves an impressive accuracy of 99.72% in multiclass classification of Arrhythmias, making it a promising solution for dynamic healthcare settings and proactive personalized care.

Multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion.

Since ECG contains key characteristic information of arrhythmias, extracting this information is crucial for identifying arrhythmias. Based on this, in order to effectively extract ECG data features and realize automatic detection of arrhythmia, a multi-classification method of arrhythmia based on multi-scale residual neural network and multi-channel data fusion is proposed. First, the features of single-lead ECG signals are extracted and converted into two-dimensional images, and the feature data sets are labeled and divided according to different types of arrhythmias. The improved residual neural network is trained on the training set to obtain the classification model of the neural network. Finally, the classification model is applied to the automatic detection of arrhythmias during exercise. The accuracy of the classification model of this method is as high as 99.60%, and it has high accuracy and generalization ability. The automatic identification of arrhythmia also contributes to the research and development of future wearable devices.

Analysis of heroin effects on calcium channels in rat cardiomyocytes based on transcriptomics and metabolomics.

Heroin can cause damage to many human organs, possibly leading to different types of arrhythmias and abnormal electrophysiological function of the heart muscle and the steady state of calcium-ion channels. We explored cardiomyocytes treated with heroin and the effect on calcium-ion channels. Transcriptomics and metabolomics were used to screen for differential genes and metabolite alterations after heroin administration to jointly analyze the effect of heroin on calcium channels in cardiomyocytes. Cardiomyocytes from primary neonatal rats were cultured in vitro and were treated with different concentrations of heroin to observe the changes in morphology and spontaneous beat frequency and rhythm by a patch clamp technique. Transcriptomic studies selected a total of 1,432 differentially expressed genes, 941 upregulated and 491 downregulated genes in rat cardiomyocytes from the control and drug intervention groups. Gene Ontology functional enrichment showed that 1,432 differential genes selected by the two groups were mainly involved in the regulation of the multicellular organismal process, response to external stimulus, myofibril, inflammatory response, muscle system process, cardiac muscle contraction, etc. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that these genes were mainly concentrated in cardiac muscle contraction, osteoclast differentiation, adrenergic signaling in cardiomyocytes, dilated cardiomyopathy, hypertrophic cardiomyopathy, and other important pathways. Metabolomic testing further suggested that cardiomyocyte metabolism was severely affected after heroin intervention. After the treatment with heroin, the L-type calcium channel current I-V curve was up-shifted, the peak value was significantly lower than that of the control group, action potential duration 90 was significantly increased in the action potential, resting potential negative value was lowered, and action potential amplitude was significantly decreased in cardiomyocytes. In this study, heroin could cause morphological changes in primary cardiomyocytes of neonatal rats and electrophysiological function. Heroin can cause myocardial contraction and calcium channel abnormalities, damage the myocardium, and change the action potential and L-type calcium channel.

Recurrence quantification analysis for fine-scale characterisation of arrhythmic patterns in cardiac tissue

This paper uses recurrence quantification analysis (RQA) combined with entropy measures and organization indices to characterize arrhythmic patterns and dynamics in computer simulations of cardiac tissue. We performed different simulations of cardiac tissues of sizes comparable to the human heart atrium. In these simulations, we observed four classic arrhythmic patterns: a spiral wave anchored to a highly fibrotic region resulting in sustained re-entry, a meandering spiral wave, fibrillation, and a spiral wave anchored to a scar region that breaks up into wavelets away from the main rotor. A detailed analysis revealed that, within the same simulation, maps of RQA metrics could differentiate regions with regular AP propagation from ones with chaotic activity. In particular, the combination of two RQA metrics, the length of the longest diagonal string of recurrence points and the mean length of diagonal lines, was able to identify the location of rotor tips, which are the active elements that maintain spiral waves and fibrillation. By proposing low-dimensional models based on the mean value and spatial correlation of metrics calculated from membrane potential time series, we identify RQA-based metrics that successfully separate the four different types of cardiac arrhythmia into distinct regions of the feature space, and thus might be used for automatic classification, in particular distinguishing between fibrillation driven by self-sustaining chaos and that created by a persistent rotor and wavebreak. We also discuss the practical applicability of such an approach.

Interventional electrophysiology in bulgaria in 2022: data from the electronic registry BG-EPHY

This study presents data from the national electronic registry BG-EPHY on electrophysiologic (EP) cardiac ablations in 2022. Material and methods: This is a retrospective study of a full one-year sample of the BG-EPHY registry. Sex and age distribution of the patients, number of ablations, use of electroanatomic mapping (EAM), irrigated and cryoablations, distribution of different types of arrhythmias, acute procedural success and complications are presented. Results: In 2022 seven EP centers performed 1369 ablations in 872 men (63.7%) and 497 women (57% increase compared to previous year), incl. 15 ablations in pediatric patients (1.1%). EAM was used in 746 procedures (54.5%), irrigated-tip catheter – in 814 (59.5%), cryoballoon catheter – in 130 (9.5%), and intracardiac echocardiography – in 33 (2.4%). The most frequently performed ablation was pulmonary vein isolation (40.2%), followed by ablation for AV nodal reentrant tachycardia (18.6%) and typical atrial fl utter (16.6%). The acute success was over 98%, while intraprocedural complications were less than 2%. Conclusion: The national registry of electrophysiology collects systematically and continuously basic data on all ablations of cardiac arrhythmias performed in the country. In 2022, during a subsiding COVID-19 pandemic and with the inclusion of new centers the number of ablations increased compared to 2021. Distribution of EP procedure types was similar to previous years. Acute success was very high, while intraprocedural complications were rare.

Self-Attention LSTM-FCN model for arrhythmia classification and uncertainty assessment

This paper presents ArrhyMon, a self-attention-based LSTM-FCN model for arrhythmia classification from ECG signal inputs. ArrhyMon targets to detect and classify six different types of arrhythmia apart from normal ECG patterns. To the best of our knowledge, ArrhyMon is the first end-to-end classification model that successfully targets the classification of six detailed arrhythmia types and compared to previous work does not require additional preprocessing and/or feature extraction operations separate from the classification model. ArrhyMon’s deep learning model is designed to capture and exploit both global and local features embedded in ECG sequences by integrating fully convolutional network (FCN) layers and a self-attention-based long and short-term memory (LSTM) architecture. Moreover, to enhance its practicality, ArrhyMon incorporates a deep ensemble-based uncertainty model that generates a confidence-level measure for each classification result. We evaluate ArrhyMon’s effectiveness using three publicly available arrhythmia datasets (i.e., MIT-BIH, Physionet Cardiology Challenge 2017 and 2020/2021) to show that ArrhyMon achieves state-of-the-art classification performance (average accuracy 99.63%), and that confidence measures show close correlation with subjective diagnosis made from practitioners.

PO-03-221 BURDEN OF ARRHYTHMIAS AND CLINICAL OUTCOMES IN PATIENTS WITH DILATED CARDIOMYOPATHY ACCORDING TO LARGE DATASET

Dilated cardiomyopathy (DCM) is characterized by dilation & impaired systolic function of one or both ventricles. Although heart rhythm disorders are associated with DCM, description of their prevalence & clinical outcomes is limited in the literature. Aim of this study was to investigate the burden, basic characteristics, & clinical outcomes of arrhythmias in DCM. We queried the National Inpatient Sample between 2017-2020 for adult patients who were hospitalized with DCM. The primary outcome was inpatient mortality. The secondary outcomes were cardiogenic shock, cardiac arrest, invasive mechanical ventilation, length of stay (LOS) & total hospital cost. Multivariable logistic, linear & Poisson regression analyses were used to estimate clinical outcomes. p-value < 0.05 was significant. There were 875,625 hospitalizations with DCM, of which 402,947 (46.1%) had arrhythmia of any type. Mean age was 64.2 yrs, 57.7% were Caucasian, 66.2% were male. The most prevalent arrhythmia was ventricular tachycardia (VT) 105,810 (12.08%), followed by paroxysmal atrial fibrillation (AFib) 104,330 (11.9%), permanent AFib 77824 (8.8%), persistent AFib 30,810 (3.5%), different degree of atrioventricular block (AVB) 26,644 (3.04%) with complete AVB being the most frequent (1.5%), & sick sinus syndrome 20220 (2.3%). Subgroup with arrhythmia vs without arrhythmia had HTN 6.3% vs 8.2%; PH 19.8% vs 15.5%; HF 89.0% vs 85.9%; HLD 53.3% vs 47.2%; CKD 45.5% vs 39.7%; COPD 28.3% vs 26.9%, respectively. During hospitalization, these subgroups had following complication rates: ACS 12.5% vs 14.1%; AKI 37.6% vs 31.8%; stroke 2.8% vs 3.1%, respectively. Subgroup with arrhythmia was associated with higher ICD (6.1% vs 2.0%), PPM (2.4% vs 0.2%), and LVAD (1.4% vs 0.6%) insertion rate. Subgroup with VT showed worse primary and secondary outcomes, while populations with AFib and AVB showed significant secondary outcomes (Table 1). Significant proportion of DCM patients is affected by arrhythmia (46%), where VT, AFib, AVB, & sick sinus syndrome being the most prevalent. Subgroup with arrhythmia is associated with HF, PH, HLD, CKD, COPD & with increased insertion rates of PPM, ICD, LVAD devices. Moreover, DCM population with different types of arrhythmias showed worse clinical outcomes & had higher resource utilization. This signifies the importance of early detection & treatment of rhythm disorders in DCM population. More research aimed at pathogenesis & long-term outcomes is needed.

Automatic cardiac arrhythmias classification using CNN and attention-based RNN network.

Cardiac disease has become a severe threat to public health according to the government report. In China, there are 0.29 billion cardiac patients and early diagnosis will greatly reduce mortality and improve life quality. Electrocardiogram (ECG) signal is a priority tool in the diagnosis of heart diseases because it is non-invasive and easily available with a simple diagnostic tool of low cost. The paper proposes an automatic classification model by combing convolutional neural network (CNN) and recurrent neural network (RNN) to distinguish different types of cardiac arrhythmias. Morphology features of the raw ECG signals are extracted by CNN blocks and fed into a bidirectional gated recurrent unit (GRU) network. Attention mechanism is used to highlight specific features of the input sequence and contribute to the performance improvement of classification. The model is evaluated with two datasets considering the class imbalance problem constructed with records from MIT-BIH arrhythmia database and China Physiological Signal Challenge 2018 database. Experimental results show that this model achieves good performance with an average F1 score of 0.9110 on public dataset and 0.9082 on subject-specific dataset, which may have potential practical applications.