29 - Arrhythmias

Normal pacemaker cell and conduction system function is fundamentally important in maintaining cardiac mechanical performance. Cardiac arrhythmias consist of a group of electrical disturbances in the heart, including pacemaker cell firing and/or conduction abnormalities. The resultant fast, slow, or irregular heart rhythm can cause significant hemodynamic compromise and discomforts, and/or death. Importantly, a thorough understanding of cardiac electrophysiology is essential for management of any cardiac patient. The electrocardiogram, as recorded from electrodes placed on the surface of the chest and extremities, is a simple, yet important diagnostic technique for identification of cardiac arrhythmias. Nevertheless, it can also be useful in diagnoses of electrolyte abnormalities, medication toxicity, ischemic heart disease, and/or enlargement of cardiac chambers.

In this paper a framework for an objective and interactive grading system of disturbances in heart rhythm is presented. The objects included in the study are transgenic mice which suffer from cardiac arrhythmias and their wild-type siblings. For all these mice long-time ECG recordings are available. The RR interval length are utilised to deduce statistical features on short-time heartbeat patterns. We demonstrate that these features are biologically relevant to classify the heartbeat patterns by a K-means clustering approach. Additionally, an extension of K-means is proposed, which consider user-defined constraints. The results of constraint-based clustering methods enable significant mid- and long-time analysis studies.

Cardiac arrhythmias are common; some are life-threatening, others merely a nuisance. They are caused by abnormalities in impulse formation or conduction that lead to slow or fast, regular or irregular heart rhythms. It is not difficult to treat patients with slow rhythms: currently available pacemakers are able to adapt their function to the needs of the body. The situation is different, however, for patients with rapid rhythms. Such rhythms may originate anywhere in the heart and result from a variety of mechanisms. They may be focal, meaning that the abnormal impulse formation is confined to a small area, or they . . .

Cardiology patient page. Familial dilated cardiomyopathy.

Arrhythmia-induced cardiomyopathy is a complex condition that causes a decline in heart function as a result of irregular heart rhythms. This disorder highlights the link between irregular heart rhythm and heart failure, necessitating prompt identification and intervention. It often occurs due to ongoing fast heart rhythms like atrial fibrillation or tachycardia. Understanding the mechanisms, symptoms, and available treatments is essential for enhancing patient outcomes given the complicated nature of the condition. This article delves into various aspects of arrhythmia-induced cardiomyopathy, including pathogenesis, clinical presentation, diagnostic methods, epidemiology, typical arrhythmias associated with the condition, and management options. It assesses patients' future outlook and necessary follow-up, aiming to provide healthcare providers with a comprehensive understanding of how to handle this intricate condition. The article emphasizes the important effect an integrative approach can have on both patients' lives and the clinical consequences of diagnosing and treating this condition. This extensive understanding enhances the resources at the disposal of physicians, enabling targeted treatments that enhance cardiomyopathy by targeting arrhythmia regulation. More research and development are needed in the field of cardiomyopathy and arrhythmia relationship. The presentation urges the medical field to delve deeper into the complexities of illness by emphasizing the need for continuous research and a multifaceted treatment plan. By combining these understandings, our goal is to enhance patient outcomes and create opportunities for further studies on cardiovascular wellness.

Artificial intelligence (AI) is a powerful technology that can enhance clinical decision-making and the efficiency of global health systems. An AI-enabled electrocardiogram (ECG) is an essential tool for diagnosing heart abnormalities such as arrhythmias. The most prevalent arrhythmia globally is atrial fibrillation (AF), which is an irregular heart rhythm that originates in the atria and can lead to other heart-related complications. A trusted AI classification of AF is explored in this study. Deep learning (DL) has been used to analyze large amounts of publicly available ECG datasets in order to classify normal sinus rhythm (NSR), AF, and other types of arrhythmias. A convolutional neural network (CNN) has been proposed to extract ECG features and classify ECG signals. Based on a 10-fold cross-validation strategy, we conducted experiments involving three scenarios for AF classification: (i) a balanced set, an imbalanced set, and an extremely imbalanced set; (ii) a comparison of ECG denoising algorithms; and (iii) the classification of AF, NSR, and other arrhythmia types (15 classes). As a result, we have achieved 100% accuracy, sensitivity, specificity, precision, and F1-score for the AF, NSR, and non-AF classifications, both for balanced and imbalanced sets. In addition, for the classification of AF, NSR, and other types of arrhythmia (15 classes), the performance results achieved an accuracy of 99.77%, sensitivity of 96.48%, specificity of 99.87%, precision of 97.03%, and F1-score of 96.68%. The results can empower AI diagnosis and assist clinicians in classifying AF on routine screening ECGs.

- Cardiac arrhythmias are irregular heart rhythms that may appear suddenly and remain undetected without continuous monitoring. Conventional monitoring approaches often depend on cloud-based analysis, leading to increased latency, higher power consumption, and concerns regarding data privacy. This work presents a compact intelligent edge device capable of performing real-time arrhythmia detection directly on embedded hardware. The proposed system combines a MAX30003 ECG analog front-end for accurate biopotential acquisition with an ESP32-S3 microcontroller for on device signal processing and classification. Acquired ECG signals undergo filtering, segmentation, and normalization before being analyzed using a lightweight convolutional neural network optimized for resource-constrained environments. The model identifies five classes of arrhythmia defined by AAMI standards (N, S, V, F, Q) without requiring continuous internet connectivity. A local OLED interface provides instant visual alerts, while optional wireless communication enables data transfer when needed. The implementation demonstrates that reliable cardiac monitoring and classification can be achieved using a portable, low-power architecture, making it suitable for wearable and remote healthcare applications where real-time response, energy efficiency, and data security are critical. Key Words: ECG Monitoring, Arrhythmia Classification, Edge Computing, ESP32-S3, MAX30003, Embedded AI.

One of the most common diseases of the cardiovascular system is cardiac arrhythmias. Assessment of the heart rate is one of the obligatory methods of pediatric examination and its deviation from the age norm may indicate a change in the child's health and be one of the first manifestations of the disease. Heart rhythm disturbances accompany various diseases, namely: congenital heart defects, cardiomyopathies, rheumatic and infectious diseases, intoxication, vegetative crises, endocrine diseases, diseases of the nervous system and others. Most often, in children's practice, it is not always possible to establish the cause of heart rhythm disturbances, as well as to classify the presence of organic or functional disorders without conducting morphological studies. Purpose — to study the features of heart rhythm and the state of the autonomic nervous system (ANS) among the children of primary school age with sinus bradycardia. Materials and methods. Children of primary school age with sinus bradycardia were examined (210 children). All children underwent: clinical examination, ECG at rest and after exercise, daily monitoring of ECG and heart rate, echocardiography, cardiointervalography with spectral analysis of heart rate. Results. Assessment of the cardiovascular system among children with bradycardia revealed the following features: 80.0% of children had no complaints, 68.0% of children at the age of 6–7 years and 55.0% of children at the age of 8–10 years had moderate bradycardia, 32.0% of children at the age of 6–7 years and 45.0% of children at the age of 8–10 years had significant bradycardia. After the exercise test, 71.0% of children still had bradycardia, which is typical for children at the age of 9–10 years and 29.0% of children had an acceleration of heart rate above the age norm. Holter monitoring revealed the following cardiac arrhythmias: sinus rhythm driver migration, ectopic rhythm, sinoatrial block, 2 grade atrioventricular block, atrial extrasystole and ventricular extrasystole. Different duration of sinoatrial pauses was detected: in the range from 1300–1400 ms among the most children (78.0%) to 1700–1800 ms among 1.8% of children. Data from spectral analysis of sinus rhythm showed the predominance of parasympathetic nervous system tone among the 84.0% of children, asympathicotonic (40.5%) and normal (44.9%) autonomic reactivity. Conclusions. Examination of the children of the primary school age revealed sinus bradycardia and bradyarrhythmia among the 80.7% of children. Bradyarrhythmia is typical for children at the age of 6–7 years, bradycardia — for children at the age of 9–10 years. Spectral analysis of the ANS showed an imbalance in the regulation of sinus rhythm: the predominance of the parasympathetic link, regardless of the initial state of the ANS and the violation of adaptive mechanisms. According to Holter monitoring, the duration of pauses is longer than normal for the given age (more than 1300 ms) indicates sinus node dysfunction and requires closer monitoring due to the risk of developing sinus node weakness syndrome and other threatening conditions in later life. The research was carried out in accordance with the principles of the Helsinki declaration. The study protocol was approved by the Local ethics committee of all participating institution. The informed consent of the patient was obtained for conducting the studies. No conflict of interest was declared by the authors. Key words: children, bradycardia, bradyarrhythmia, vegetative homeostasis.

Irregular heart rhythms are a primary manifestation of cardiovascular disease, considerably contributing to global morbidity and mortality rates. Moreover, patients with cardiac arrhythmias often experience a higher prevalence of sleep disorders, anxiety, and depression owing to various factors. To investigate the epidemiological characteristics and psychological factors associated with arrhythmia in the elderly and to establish a theoretical foundation for its prevention and treatment in older adults. A retrospective analysis was performed on 169 elderly patients admitted to the Shangqiu First People's Hospital from December 2022 to December 2023. All subjects underwent 24-hour electrocardiogram monitoring to record heart rate, heart rate variability, and 24-hour ambulatory electrocardiogram data. Additionally, patients' medical records were reviewed to gather information on their general condition, including age, gender, underlying diseases, and other relevant factors. Patients were divided into four groups based on their Hamilton Anxiety (HAMA) and Hamilton Depression Rating Scale (HAMD) scores: Group A (HAMA scores ≥ 7), Group B (HAMD scores ≥ 7), Group C (both HAMA and HAMD scores ≥ 7), and Group D (HAMA and HAMD scores < 7). Psychological factors such as depression, anxiety, sleep status, and quality of life were analyzed. Pearson correlation was used to examine the relationship between scores from the Pittsburgh Sleep Quality Index (PSQI), HAMA/HAMD scales, and the Short Form 36-item Health Survey (SF-36) with the presence of arrhythmia. Among the 169 patients, 87 (51.5%) had concurrent arrhythmia. Atrial arrhythmias constituted the largest proportion at 34.8% (30 out of 87), followed by sinus tachycardia at 24.1% (21 out of 87), and ventricular arrhythmias at 9.2% (8 out of 87). Factors such as advanced age, coronary heart disease, hypertension, smoking, exposure to secondhand smoke, and residing in rural areas significantly increased the risk of developing arrhythmia. There was a statistically significant difference between the two groups regarding PSQI, HAMA-14, HAMD-17, and SF-36 scores. Pearson correlation analysis revealed that PSQI, HAMA-14, and HAMD-17 scores were positively correlated with arrhythmia in the elderly, while the SF-36 score was negatively correlated. The anxiety, depression, and combined anxiety-depression groups exhibited significantly higher PSQI, HAMA-14, and HAMD-17 scores compared to the nonanxiety and non-depression group. Arrhythmia among the elderly is primarily found in individuals with advanced age and existing health conditions. It is also linked to psychological factors such as depression, anxiety, reduced quality of life, and sleep disturbances.

Summary The first artificial pacemaker was implanted 51 years ago. Considerable progress has been made in recent decades and pacemakers have become fully automated cardiac rhythm management (CRM) systems representing a new, growing, and important set of therapies constituting a challenge for cardiologists and family practitioners. In the 25th year since the first implant of an automatic defibrillator (ICD) in Switzerland the present review puts the key CRM developments into perspective and is intended as a contribution to improved understanding and thereby management of patients under electrical therapy of the heart, a growing domain that is evolving towards a tool for full cardiac (and other) disease management. With the initial aim of preventing Adams-Stokes attacks by implanting a pacemaker with an electrode sutured on the heart, pacemakers have evolved to become devices able to automatically sense, analyse, and deliver appropriate electrical therapy against cardiac arrhythmias in their different forms. Automatic atrial and/or ventricular pacing, cardiac resynchronisation for correction of heart failure, and defibrillation for primary or secondary prevention of sudden cardiac death have demonstrably improved clinically important endpoints such as quality of life, symptom relief, hospitalisation rate and mortality. The increasing number of studies with documented endpoint reductions has resulted in corresponding treatment recommendations in the form of guidelines. The complexity of the indications and individual CRM programming possibilities, combined with the growing prevalence of patients seen to be benefiting from CRM in daily practice, has revealed the need for practising physicians to stay abreast of latest developments and be able to counsel their patient sa dequately. New ways of controlling vital signs via multiple sensors open the way to automatic remote and permanent assessment of many patient- or device-related parameters and thereby remind us of the importance of understanding the basic physiology of haemodynamics and cardiac function.

A porcine model for an acute lethal arterial subdural bleeding in man is presented. Blood from the abdominal aorta was led via an electronic drop recorder into a collapsed intracranial subdural rubber balloon. Systemic arterial pressure (SAP), two intracranial pressures and 6 other vital parameters were monitored continuously in spontaneously breathing (n = 4) and mechanically ventilated (n = 4) pigs. In both animal groups bleeding caused an immediate rise in intracranial pressures (ICP) with transtentorial pressure gradients developing. As a result the cerebral perfusion pressures (CPP) decreased progressively, leading to an isoelectric EEG. In spontaneously breathing animals, the pressure changes resulted in apnoea within 2-4 minutes, irregularities in heart rhythm and in a marked rise in SAP (the Cushing reaction). A final collapse of all pressures occurred after 222 +/- 68 sec at a mean bleeding volume of 10.3 +/- 1.9 ml. In contrast, in mechanically ventilated animals, the course of bleeding was less dramatic. No change in cardiac rhythm or rise in SAP appeared despite a larger mean bleeding volume (12.0 +/- 1.6 ml). Instead, SAP slowly fell, reaching a level of approximately 40 mm Hg within 1 hour, while CPP concomitantly decreased from 120 mm Hg to 15 mm Hg. The findings in this and in a parallel study are explained in terms of the intracranial volume tolerance concept (Zwetnow et al. 1986). The beneficial effect of assisted ventilation on the course of subdural bleeding is multifactorial, involving both metabolic and mechanical mechanisms.

Arrhythmia is a cardiovascular disease that alters the heart rate, resulting in too fast, too slow, or irregular rhythms. It is a life-threatening disease if left untreated. Traditionally, arrhythmia is diagnosed by a trained doctor, using an electrocardiogram to analyze irregular heartbeats. However, these methods are vulnerable to inadvertent misdiagnosis, especially during the early stages of the disease. In this paper, an approach for cardiac arrhythmia detection is presented, where the subjects or instances are first categorized as diseased or normal and then further graded into normal (non-diseased) or as distinct subtypes of cardiac arrhythmia. The dataset was obtained from the UCI Machine Learning Data Repository, and machine learning methods such as XGBoost, CatBoost, SVM, and Random Forest, were experimented with. Addition-ally, the mutual information-based feature selection approach, minimal redundancy maximum relevance (mRMR), is proposed to improve classification accuracy. Standard evaluation metrics such as accuracy, f1-score, precision, and recall are utilized for comparison of the obtained results. The experimental results demonstrated that accuracy of 81.48% was achieved for multi-class classification, while binary classification achieved up to 84% accuracy.

Background and Objective: Arrhythmias are the most dangerous cause of death in cardiac patients, the purpose of this study is to investigate cardiac arrhythmias in non-cardiac surgeries with a focus on the type of arrhythmia and the type of surgery in Imam Khomeini (RA) hospital in Ilam city in 2021. Materials and Methods: In a retrospective study, 150 patients were selected as the sample size. A questionnaire containing demographic information and type of underlying disease, type of arrhythmia and clinical outcome of arrhythmia, type of surgery and type of anesthesia. The studied data were analyzed by SPSS software. To analyze the data, T-test and chi-square were used. Results: The most arrhythmia caused in general anesthesia was related to AF arrhythmia and tachycardia, significantly 100% (P&lt;0.04). The results show that craniotomy had the highest (72.5%) clinical outcome of arrhythmia in the form of death, and there was a significant relationship between the type of surgery and the clinical outcome of arrhythmia. Conclusion: The results of this study generally showed that bradycardia is the most common arrhythmia and PVC arrhythmia is the least, general anesthesia has played an important role in causing cardiac arrhythmias, and the incidence of AF arrhythmia is evident at older ages and the greatest recovery is in the age range of 30-60 years and it was more in men than women, and more incidence of AF was reported in patients who underwent laparotomy. Postoperative AF is a frequent and serious complication in older patients

IntroductionAcute myocardial infarction (MI) is one of the most common causes of death in humans in highly developed countries. Among its most frequent complications affecting the patient’s prognosis are cardiac arrhythmias: ventricular tachycardia (VT) and ventricular fibrillation (VF).Material and MethodsThe study aimed to characterise arrhythmias in 19 pigs subjected to experimentally induced MI obtained by occlusion of the proximal left anterior descending (LAD) coronary artery using an angioplasty balloon. The anaesthetic protocol was modified to reduce mortality by including procedures stabilising haemodynamic disorders which develop during episodes of ischaemia and arrhythmia. During 30 min of experimentally induced ischaemia, the heart rhythm was recorded using a 12-lead ECG. The time, frequency, and type of arrhythmias were analysed.ResultsVentricular arrhythmias were found in 94.74% of the treated pigs. The most common were ventricular premature complexes, reported in 88.89% of pigs with arrhythmia. Ventricular tachycardia was recorded in 66.67% and ventricular fibrillation in 50% of pigs with arrhythmias.ConclusionMyocardial infarction due to proximal LAD occlusion is characterised by a high incidence of ventricular arrhythmias, especially VT and VF. Because of the high survival rate, this MI porcine model may serve as a model for research on acute ischaemic ventricular arrhythmias in humans. Additionally, it reduces the total number of animals required for testing while yielding meaningful results, which is in line with the 3R principle.

Introduction: Arrhythmias in pregnancy may cause concern for the wellbeing of mothers and fetuses. Understanding the physiologic changes and types of arrhythmias that occur during pregnancy can help in identifying cases requiring intervention. This study aimed to determine the prevalence and types of electrocardiography changes and cardiac arrhythmias in normal pregnancy. Materials and Methods: This observational study was conducted at a tertiary care hospital. Four hundred and fifty women aged 20–35 years with singleton pregnancy in early gestation, irrespective of parity, comprised the study group. A baseline ECG of the first trimester was reviewed with repeat ECG at 22–24 weeks and 32 weeks along with 24-h Holter studies during 20–24 weeks. Results: The most common rhythm disturbance was ventricular premature contractions (1.8%), followed by atrial fibrillation (0.7%). A significant association was seen between rhythm disturbances with advancing age and pregnancy-induced hypertension. No difference was observed between P-axis wave and PR interval. QRS axis and duration decreased significantly from the time of enrollment to 22–24 weeks to 32 weeks, whereas QTc interval increased during the course of pregnancy. No ST/T-wave changes were seen in any of the cases at enrollment, whereas at 22–24 and at 32 weeks, new T-wave inversion was observed in 8.2%. Conclusion: ECG changes such as reduction in QRS axis, T-wave inversion in lead III and V1–2, and prolonged QTc were observed in pregnant women. The prevalence of rhythm disturbances was low, with ventricular premature complex being the most common pathology. Minor electrocardiographic changes may be considered normal in pregnancy unless associated with significant symptoms.