Irregular Heart Rhythm Research Articles

Abstract 4135239: Pausing Under Pressure: Ocular Hypertension as an Atypical Cause of Heart Block

A 67-year-old male with a history of hypertension presented to the emergency department with abnormal cardiac monitor results. Prior to this, he was diagnosed with primary open-angle glaucoma by ophthalmology and underwent left-sided aqueous tube shunt placement, which was complicated by hypotony requiring revision and holding of all topical glaucoma medications. Concurrently, he was noted to have an irregular heart rhythm during a routine physical. In office ECG showed normal sinus rhythm, but subsequent 14-day continuous ECG monitor showed sinus bradycardia with nadir heart rate of 21 beats-per-minute and 114 ventricular pauses (longest 4.8 seconds) primarily due to second-degree atrioventricular (AV) block, Mobitz type 1, but also periods of high-grade AV block. He was sent to the emergency department for expedited pacemaker evaluation. On further review, heart block and ventricular pauses occurred exclusively over a 12-hour period that temporally coincided with a hospitalization for acute ocular hypertension following shunt revision. His intraocular pressure peaked at 56 mmHg (normal range 10-21 mmHg), then normalized to 21 mmHg after acetazolamide and anterior chamber paracentesis. Bradycardia resolved following ophthalmic intervention. Bradyarrhythmias were attributed to excess vagal tone from acute ocular hypertension via an atypical but recognized cause of the oculocardiac reflex, and pacemaker placement was avoided. The oculocardiac reflex is a reflex bradycardia involving the trigeminal and vagus nerves. Cardiac effects are consistent with other vagally-mediated bradyarrhythmias, including sinus bradycardia, AV block, and even asystole. This reflex occurs commonly during traction on extraocular muscles during strabismus surgery, but can be seen in other orbital and ocular stimuli. To our knowledge, this is the first documented case of ocular hypertension leading to atrioventricular block through this mechanism. This case illustrates the importance of (1) clinical context of abnormal events during extended cardiac monitoring, (2) understanding the oculocardiac reflex as a cause of bradyarrhythmias, and (3) ruling out reversible medical causes of dysrhythmias prior to permanent device implantation.

Deep learning hybrid model ECG classification using AlexNet and parallel dual branch fusion network model.

Cardiovascular diseases are a cause of death making it crucial to accurately diagnose them. Electrocardiography plays a role in detecting heart issues such as heart attacks, bundle branch blocks and irregular heart rhythms. Manual analysis of ECGs is prone to mistakes and time consuming, underscoring the importance of automated methods. This study uses AI models like AlexNet and a dual branch model for categorizing ECG signals from the PTB Diagnostic ECG Database. AlexNet achieved a validation accuracy of 98.64% and a test set accuracy of 99% while the dual branch fusion network model achieved a test set accuracy of 99%. Data preprocessing involved standardizing, balancing and reshaping ECG signals. These models exhibited precision, sensitivity and specificity. In comparison to state of the arts' models such as Hybrid AlexNet SVM and DCNN LSTM our proposed models displayed performance. The high accuracy rates of 99% underscore their potential for ECG classification. These results validate the advantages of incorporating learning models into setups for automated ECG analysis providing adaptable solutions for various healthcare settings including rural areas.

Assessment of the Sokolow Lyons Criteria of Left Ventricular Hypertrophy in Comparison with Echocardiography in Hypertensive Patients

Around 65.4% of people over the age of 60 have hypertension, and it's the underlying cause of 6% of all fatalities globally. The left ventricle enlarges, or hypertrophies, in people with hypertension. If the left ventricle of the heart thickens its walls or the chamber of the left ventricle expands, or if both happen at once, the condition is called left ventricular hypertrophy (LVH) [1]. This condition impacts approximately 15 -20% of the general population and has similar rates in men and women. Serious health complications such as cardiovascular diseases, stroke, congestive heart failure, irregular heart rhythms (ventricular arrhythmias), and sudden cardiac death are linked to it.Rationale of the study: In people with slightly hypertensive conditions, the prevalence of LVH is 20%; in those with severe or complex hypertension, it approaches 100%. LVH is also a result of valve disorders, which cause pressure and/or volume overload. Furthermore, "physiological" LVH—which occurs in well-trained athletes in reaction to job overload—can arise. About 0.2% of the general population has hypertrophic cardiomyopathy, a hereditary condition caused by mutations in the sarcomere protein genes. It is characterized by severe cardiac tissue disarray, fibrotic scarring, and aberrant internal coronary arteries.Background and statement of the problem: Hypertension is a leading cause of many cardiovascular problems and cerebrovascular accidents Aim: Assessment of the Sokolowlyon’s Criteria of Left Ventricular Hypertrophy in comparison with Echocardiography Inhypertensives PatientsObjective: To determine whether the ECHO of left ventricular hypertrophy significantly coincides with the Sokolow Lyon criterion of the ECG.Methodology: This cross-sectional study was carried out at the Shree Balaji Medical College and Hospital's general medicine department in Chromepet, Tamil Nadu, India. The study ran from June 2022 to December 2023, a duration of eighteen months.Results: The study focused on assessing the correlation between Sokolow-Lyon criteria on (ECG) and (LVH) as determined by echocardiography (ECHO). The results revealed that among the 125 participants, 88 cases (70.40%) were identified as having LVH based on Sokolow-Lyon criteria, while 37 cases (29.60%) showed no signs of LVH. These findings provide a quantitative breakdown of the prevalence of LVH according to ECG criteria within the study population.Conclusion: Our investigation's findings showed that the ECG LVH Sokolow Lyon criteria had a satisfactory level of specificity and sensitivity in the diagnosis of left ventricular hypertrophy (LVH). The most sensitive diagnostic criteria turned out to be the ECG LVH Sokolow Lyon criteria, especially when it came to patients with co-morbidities. We found that the Sokolow Lyon criteria had a 36% sensitivity in the current investigation. As such, we suggest applying these criteria to assist in verifying the diagnosis of LVH, particularly in low-complexity healthcare environments without image-based diagnostic techniques like MRI and ECHO.

Evaluating the Impact of Windowing Techniques on Fourier Transform-Preprocessed Signals for Deep Learning-Based ECG Classification

(1) Background: Arrhythmias, or irregular heart rhythms, are a prevalent cardiovascular condition and are diagnosed using electrocardiogram (ECG) signals. Advances in deep learning have enabled automated analysis of these signals. However, the effectiveness of deep learning models depends greatly on the quality of signal preprocessing. This study evaluated the impact of different windowing techniques applied to Fourier transform-preprocessed ECG signals on the classification accuracy of deep learning models. (2) Methods: We applied three windowing techniques—Hamming, Hann, and Blackman—to transform ECG signals into the frequency domain. A one-dimensional convolutional neural network was employed to classify the ECG signals into five arrhythmia categories based on features extracted from each windowed signal. (3) Results: The Blackman window yielded the highest classification accuracy, with improved signal-to-noise ratio and reduced spectral leakage compared to the Hamming and Hann windows. (4) Conclusions: The choice of windowing technique significantly influences the effectiveness of deep learning models in ECG classification. Future studies should explore additional preprocessing methods and their clinical applications.

Fulminant myocarditis associated with human rhinovirus A66 infection: a case report

BackgroundHuman rhinoviruses (HRVs) are among the most common pathogens of upper respiratory infections, and they are responsible for the common cold. An increasing number of studies have shown that HRV is associated with more severe illness. However, HRV-associated fulminant myocarditis has rarely been reported.Patient presentationA previously healthy 8-year-old boy developed fever, fatigue, and vomiting for 3 days, with a subsequent exacerbation accompanied by confusion lasting for 9 h. The day before admission, the patient presented with oliguria, confusion, and hypotension, and he was suspected of having myocarditis. The patient was transferred to our hospital for further diagnosis and treatment. On admission, rough and moist rales were detected, and the heart sounds were muffled, accompanied by an irregular heart rhythm and a gallop. An electrocardiogram (EKG) revealed a wide QRS complex, ST-segment depression, premature ventricular contractions, and complete right bundle branch block. Laboratory tests revealed that brain natriuretic peptide (BNP), N-terminal pro BNP (NT-pro BNP), and cardiac biomarkers, such as troponin I, creatinine kinase (CK), and creatinine kinase-MB (CK-MB) were elevated. Additionally, echocardiography revealed an ejection fraction of approximately 28%. The child developed severe cardiac dysfunction and tissue hypoperfusion, and the cardiogenic shock could not be corrected despite active drug therapy. He had indications for ECMO implantation. A rarely reported rhinovirus, namely, A66, was detected in his bronchoalveolar lavage fluid and oropharyngeal swabs via metagenomic next-generation sequencing and a PCR assay. Bacterial culture of all the samples yielded negative results.ConclusionsThis case presents a patient with severe human rhinovirus A66 infection, which is likely responsible for fulminant myocarditis. This report facilitates prompt diagnosis and treatment of fulminant myocarditis. Clinicians should consider rhinovirus as a possible pathogen of fulminant myocarditis, especially when patients present with symptoms or signs of heart involvement.

9398 Early Identification of Impending Thyroid Storm Leading to Improved Thyrotoxic Induce Cardiomyopathy

Abstract Disclosure: D.H. Sacoto: None. D. Patel: None. A. De Rosairo: None. K. Madani: None. B. Singh: None. A. Bonilla Campos: None. R. Belokovskaya: None. F. Alberto A.: None. Introduction: Heart failure can be the leading presentation in 6% of thyroid storm cases and is the leading cause of death, particularly in elderly patients. However, its importance lies in symptom control and heart function reversibility once the euthyroid state is achieved. We present a caseof thyroid storm-induced atrial fibrillation (Afib) leading to severe systolic dysfunction. Case: A 61-year-old female with a history of Graves’ disease (GD) presented to the ED with two months of palpitations and one week of shortness of breath. There were bibasilar lung crackles, irregular heart rhythm, pedal edema, and thyromegaly at the examination. EKG was consistent with Afib with rapid ventricular response (123-135 beats per minute); She was tachypneic (respiratory rate: 40 per minute) and hypoxemic (87% O2 saturation), which required bilevel positive airway pressure. Otherwise, hemodynamically stable (BP: 127/106 mmHg). Laboratories showed negative troponin T (<0.010 ng/mL, n <0.01 ng/mL). However, elevation of Pro-B-type natriuretic peptide (6000 pg/mL, n <125 pg/mL), an x-ray displaying bibasilar pleural effusions and an echocardiogram demonstrating a severely reduced ejection fraction (EF) (21%, n 50-70%), myocardial perfusion scan was normal. Thyroid evaluation showed a suppressed TSH (<0.01 uIU/mL, n 0.27-4.20 uIU/mL) with elevated free T4 (5.8 ng/dL, n 0.9-1.8 ng/dL), free T3 (202 ng/dL, n 2.0-4.4 ng/dL), and TSI (8.25 IU/L, n 0-0.55 IU/L). A thyroid ultrasound revealed increased vascular flow on the left thyroid lobe. A Burh-Schakowsky score of 45 points was consistent with impending thyroid storm. She was started on methimazole, cholestyramine, and propranolol. Three months follow-up, she was asymptomatic, TSH normalized (4.12 uIU/mL), and mild improvement of cardiac EF 25%. Discussion: A thyroid storm in the setting of GD often presents after precipitating factors such as surgery, infection, or, as in our case, anti-thyroid medication non-compliance. Increased thyroid hormone levels upregulate cardiac β receptors, enhancing sensitivity to sympathetic innervation. These can shorten the refractory period of cardiomyocytes, leading to tachyarrhythmias, with Afib occurring in 20% of patients. Chronic tachycardia finally impairs left ventricle function, with heart failure as an outcome. A high index of suspicion and the ability of early diagnosis of impending thyroid storm is critical since the first laboratory confirmation is often delayed, and second, either anti-thyroid medication, radioactive iodine ablation or thyroidectomy, in addition to non-selective beta-blockers, can reverse cardiac dysfunction and lead to clinical improvement as early as 12 -Twenty-four hours of medical treatment. In our case, the early diagnosis and treatment of thyroid storm, in addition to the rate control prevented a deadly outcome and contributed to the improvement of heart failure. Presentation: 6/1/2024

Abstract Or105: Constitutive Deletion of the Obscurin Ig58/59 Domains Induces Structural and Electrophysiological Remodeling in Atria

Introduction: Obscurin is a giant cytoskeletal protein that supports muscle development, tethers intracellular compartments and regulates contraction. In the ObscnΔIg58/59 mouse model, expressing obscurin lacking Immunoglobulin (Ig) domains 58 and 59, aging males exhibit irregular heart rhythm with prominent atrial fibrillation, atrial enlargement, and progressive remodeling of the ventricles. A mechanistic basis for the emergence of arrhythmia early on could not be identified in ObscnΔIg58/59 ventricles, suggesting that the atria are preferentially impacted by deletion of the obscurin Ig58/59 module prior to the ventricles. Hypothesis: We hypothesize that Ig58/59 deletion elicits unique structural, electrical, and functional consequences in the atria preceding ventricular maladaptation. Aims: I. Examine the structural organization of ObscnΔIg58/59 atria II. Assess Ca 2+ handling in isolated atrial cardiomyocytes and determine molecular alterations. Methods: Electron microscopy and super resolution microscopy were utilized to visualize sarcomeric ultrastructure and the transverse-axial tubule (TAT) network, respectively. Ca 2+ handling activity and synchrony were also resolved, while Phos-Tag gels were used to quantify T-cap phospho-species. Results: ObscnΔIg58/59 atria exhibited misalignment of Z-disks. Spontaneous and stimulated Ca 2+ cycling behavior were [AB1] differentially disrupted in atrial cardiomyocytes in 6- and 12-month ObscnΔIg58/59 males. Relatedly, atrial cells showed an age-dependent deterioration in TAT architecture. Finally, as a function of aging, ObscnΔIg58/59 atria displayed alterations in the expression and phosphorylation of T-cap, a Z-disk protein implicated in the integration of t-tubules with the sarcomeric cytoskeleton. Conclusions: The structural and electrical alterations that arise in ObscnΔIg58/59 atria precede ventricular dysfunction and coincide with the emergence of atrial fibrillation. Collectively, our work indicates that the atria are principally affected by Ig58/59 elimination, instigating arrhythmias and morphological alterations with age. The ObscnΔIg58/59 mouse model has thus emerged as a proxy for atrial cardiomyopathy.

Long QT Syndrome With Drugs Used in the Management of Arrhythmias: A Systematic Review.

Long QT syndrome (LQTS) is a severe cardiac disorder characterized by an abnormally prolonged QTc interval on an electrocardiogram (ECG), which can result in life-threatening irregular heart rhythms. The use of certain medications, particularly anti-arrhythmic drugs such as quinidine, sotalol, and amiodarone, can lead to acquired LQTS by prolonging the QT interval through the inhibition of specific ion channels responsible for heart repolarization, which may present symptoms like fainting, seizures, and sudden cardiac arrest. This systematic review, conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, focused on analyzing the association between Long QT syndrome and drugs utilized for managing arrhythmias, involving a thorough examination of six selected studies from an initial pool of 68 articles. It was found that antiarrhythmic drugs such as amiodarone, sotalol, dofetilide, procainamide, quinidine, and flecainide have the potential to cause QT prolongation as a side effect, which is often influenced by factors including dosage, coexisting medical conditions, electrolyte imbalances, and other risk factors. Prolonged QT interval significantly elevates the risk of a life-threatening arrhythmia called torsade de pointes. The management of this side effect typically involves reducing the medication dosage or discontinuing it altogether and, in some cases, employing selective beta blockers. However, further research is essential to improve the understanding and implementation of strategies to prevent and manage QT prolongation caused by antiarrhythmic drugs. Additional clinical studies are warranted to enhance knowledge and provide comprehensive guidelines to healthcare practitioners regarding the appropriate use of these medications. Close monitoring of the QT interval is recommended for patients receiving anti-arrhythmic therapy, and consideration should be given to patient-specific risk factors for LQTS, including age, sex, and electrolyte imbalances.

Understanding Arrhythmia-Induced Cardiomyopathy: Symptoms and Treatments.

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.

A comparative study of heterogeneous machine learning algorithms for arrhythmia classification using feature selection technique and multi-dimensional datasets

Arrhythmia, a common cardiovascular disorder, refers to the abnormal electrical activity within the heart, leading to irregular heart rhythms. This condition affects millions of people worldwide, with severe implications on cardiac function and overall health. Arrhythmias can strike anyone at any age which is a significant cause of morbidity and mortality on a global scale. About 80% of deaths related to heart disease are caused by ventricular arrhythmias. This research investigated the application of an optimized multi-objectives supervised Machine Learning (ML) models for early arrhythmia diagnosis. The authors evaluated the model’s performance on the arrhythmia dataset from the UCI ML repository with varying train-test splits (70:30, 80:20, and 90:10). Standard preprocessing techniques such as handling missing values, formatting, balancing, and directory analysis were applied along with Pearson correlation for feature selection, all aimed at enhancing model performance. The proposed optimized RF model achieved impressive performance metrics, including accuracy (95.24%), precision (100%), sensitivity (89.47%), and specificity (100%). Furthermore, the study compared the proposed approach to existing models, demonstrating significant improvements across various performance measures.

Arrhythmia Classification from ECG Signals Using Transformers and Data Balancing Techniques

While many arrhythmias pose minimal threat, certain heart rhythm irregularities elevate the potential for stroke or heart failure. The complexity arises particularly with the supraventricular premature heartbeat which has a resemblance to a normal beat and occurs infrequently. Consequently, this research proposes a data balancing and classification technique that enhances the accuracy of identifying mentioned hard-to-classify heartbeats while maintaining robust metrics for other classes. The study introduces a deep learning framework combined with a multi-head attention transformer, for balancing – under-sampling and synthetic minority oversampling are used. To evaluate the proposed model, various experiments based on real data were conducted. The results were compared with an existing model used in chest belt heartbeat monitoring, and the results show that the transformer model achieved better performance for supraventricular premature heartbeats, at the same time reaching high overall and per-class metrics.

Cardiac Manifestations of Wegener's Granulomatosis: A Case Report and Literature Review

Wegener's Granulomatosis is a rare systemic vasculitis. Lesions commonly occur in the upper airways, lungs, kidneys, and can also affect the skin, eyes, joints, peripheral nervous system, and the heart. Cardiac involvement is rare and can affect the endocardium, myocardium, or pericardium. Pulmonary arterial hypertension (PAH) has been rarely reported. We report the case of D.R., a 17-year-old without significant personal medical history, referred for the evaluation of new-onset exertional dyspnea. The clinical examination was normal except for an irregular heart rhythm with a pronounced pulmonary B2 sound. The ECG revealed atrial fibrillation (AF) with incomplete right bundle branch block (iRBBB). A chest X-ray showed cardiomegaly primarily affecting the atria without signs of pulmonary congestion. Precapillary PAH was suggested by echocardiography and confirmed through right heart catheterization. Routine blood tests were normal. Contrast-enhanced chest CT showed no evidence of pulmonary embolism or signs of pulmonary parenchymal involvement. Given the patient's young age, a specific vasculitis work-up was conducted, leading to a diagnosis of Wegener's Granulomatosis. Through this case report, we will present the cardiac and extracardiac manifestations of this vasculitis, as well as therapeutic management strategies.

ECG of the month

In this issue's ECG of the month, Charles Bloe presents the case of a 19-year-old man with a family history of cardiomyopathy who noticed an irregular heart rhythm on his smartwatch.

Fetal premature atrial contractions: natural course, risk factors and adverse outcome.

Fetal premature atrial contractions (PAC) are usually benign but may be associated with congenital heart defect (CHD) and tachyarrhythmia, which in turn carry a risk of cardiac failure and fetal death. We aimed to explore the frequency of adverse outcome and to identify risk factors for tachyarrhythmia in pregnancies complicated by fetal PAC. This was a retrospective cohort study of fetuses diagnosed with PAC at two academic centers in Amsterdam, The Netherlands, between 2007 and 2022. Cases with a congenital anomaly and those with a prior diagnosis of CHD or other arrhythmias were excluded. M-mode and Doppler tracings were reanalyzed and the frequency of PAC was recorded. The incidence of the following adverse outcomes was examined: underlying CHD not identified at the 20-week fetal anomaly scan, tachyarrhythmia, other arrhythmia, administration of antiarrhythmic therapy and death. Risk factors for tachyarrhythmia were analyzed using odds ratios (OR). In 24.1% of 1439 referred cases, PAC resolved before confirmation at the fetal medicine unit (FMU). Of the 939 included cases with confirmed PAC, the total incidence of adverse outcome was 6.8% (64/939). CHD was diagnosed in 14 (1.5% (95% CI, 0.9-2.5%)) cases, of which eight were diagnosed prenatally and six postnatally. Compared with baseline, the incidence of CHD was higher in the presence of fetal PAC (OR, 1.8 (95% CI, 1.0-3.3); P = 0.034). Tachyarrhythmia occurred prenatally and/or postnatally in 32 (3.4%) cases, of which eight (25.0%) showed signs of cardiac failure, and in 23 (71.9%) cases, antiarrhythmic therapy was required. No cases of tachyarrhythmia led to fetal or infant death. Risk factors for tachyarrhythmia were PAC with short runs of supraventricular tachycardia (OR, 98.7), blocked PAC (OR, 30.3), PAC in bigeminy (OR, 21.8), frequent PAC (one per 5-10 beats) (OR, 6.9), signs of cardiac failure (OR, 14.2) and the presence of a foramen ovale aneurysm (OR, 5.0). PAC are generally benign and often resolve spontaneously. However, fetuses with an irregular heart rhythm should be referred for advanced ultrasonography, which should focus on classifying the type of PAC and risk of adverse outcome. When risk factors for tachyarrhythmia are identified, weekly fetal heart-rate monitoring is advised until resolution of the PAC. In the absence of risk factors, standard obstetric care may be sufficient, with additional instructions to report reduced fetal movements. Should tachyarrhythmia or cardiac failure develop, referral back to the FMU is indicated. © 2023 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

Circulating neuropeptide Y as a biomarker in postoperative atrial fibrillation cases administered off-pump coronary bypass Graft surgery

Background and aimsPostoperative atrial fibrillation (POAF) is considered the most prevalent irregular heart rhythm after heart surgery. The cardiac autonomic nervous system significantly affects POAF, and neuropeptide Y (NPY), an abundant neuropeptide in the cardiovascular system, is involved in this autonomic regulation. The current work aimed to examine the potential association of NPY with POAF in individuals administered isolated off-pump coronary artery bypass grafting. MethodsFrom January 1 to May 31, 2020, we examined consecutive cases administered successful isolated off-pump coronary artery bypass grafting with no previously diagnosed atrial fibrillation (AF). Clinical characteristics and plasma samples were collected before surgery. NPY was quantified by enzyme-linked immunosorbent assay (ELISA) in peripheral blood, and POAF cases were identified through a 7-day Holter monitoring. ResultsAmong 120 cases with no previously diagnosed AF, 33 (27.5 %) developed POAF during hospitalization. Median NPY levels were markedly elevated in the POAF group in comparison with the sinus rhythm group (31.72 vs. 27.95, P = 0.014). Multivariable logistic regression analysis revealed age (OR = 1.135, 95%CI 1.054–1.223; P = 0.001), left atrial size (OR = 1.136, 95%CI 1.004–1.285; P = 0.043), and NPY levels in peripheral blood (OR = 1.055, 95%CI 1.002–1.111; p = 0.041) independently predicted POAF. Additionally, NPY levels were positively correlated with high-frequency (HF) (r = 0.2774, P = 0.0022) and low-frequency (LF) (r = 0.2095, P = 0.0217) components of heart rate variability. ConclusionIn summary, this study demonstrates an association between elevated NPY levels in peripheral blood before surgery and POAF occurrence.

Acquisition and Analysis of Cardio Graphic Signal Using Various Transforms

In the modern days, cardiac problems among various age group people have increased by a large ratio. Electro cardiograph is widely used to monitor the electrical activities of the heart. With the help of ECG, we can able to easily detect and classify the irregular activities of the heart. Hence in this study, we have acquired ECG signals from ten different subjects of various age groups using the BIOPAC system. All the acquired ECG signals are pre-processed using the infinite impulse response bandpass filter. Then the most vital R wave is detected by using the peak detection method present in-built in the BIOPAC system. This R wave detection plays a very vital role in diagnosing heart rhythm irregularities, calculating the heart rate variability, recognizing the heart rate variability, and measuring the heart rate. The pre-processed ECG signal is processed using highly advanced windowing techniques to extract the power spectrum and frequency-based values of the ECG signal. The results of the Fourier transform and spectral density of the processed ECG signals are displayed in this study. The features extracted from ECG signals acquired from ten different subjects have been tabulated in this study.

Ventricular bigeminy associated with myocardial ischemia in a dog with a colonic torsion: a case report

BackgroundVentricular bigeminy due to myocardial ischemia has been reported in humans as well as in canine patients with obstructive gastrointestinal diseases. This is the first case report of ventricular bigeminy in a dog with a colonic torsion that resolved after fluid resuscitation and restoration of myocardial perfusion.Case presentationAn 11-year-old, male neutered mixed breed dog presented with a one day history of vomiting, tenesmus, and lethargy. Physical examination identified an irregular heart rhythm and intermittent pulse deficits. A ventricular arrhythmia represented by ventricular premature complexes (VPCs) organized in bigeminy, was appreciated on a 3-lead electrocardiogram (ECG) with a single lead (II) view. Abdominal radiographs confirmed a colonic torsion. Prior to anesthetic induction, ventricular bigeminy was non responsive to fentanyl or lidocaine. The patient was anesthetized and intravascular volume deficit was identified by dampened plethysmographic wave amplitude (plethysomographic variability), audible softening of the Doppler sound, and more pronounced pulse deficits. Fluid resuscitation was achieved with a combination of intravenous crystalloid and colloid fluid therapy comprising 7.2% hypertonic saline and 6% hetastarch. The patient’s cardiac rhythm converted to normal sinus after fluid resuscitation. The colonic torsion was surgically corrected. The patient recovered well from anesthesia and was ultimately discharged from the hospital 5 days later.ConclusionsThe present case report highlights that myocardial ischemia can lead to ventricular arrythmias, such as ventricular bigeminy. This is the first documented case of ventricular bigeminy in the canine patient with a colonic torsion. Assessment of patient volume status and appropriate fluid resuscitation along with continuous electrocardiogram (ECG) monitoring are vital to patient stability under general anesthesia.

Advanced Neural Network Architecture for Enhanced Multi-Lead ECG Arrhythmia Detection through Optimized Feature Extraction

Cardiovascular diseases are a pervasive global health concern, contributing significantly to morbidity and mortality rates worldwide. Among these conditions, arrhythmia, characterized by irregular heart rhythms, presents formidable diagnostic challenges. This study introduces an innovative approach utilizing deep learning techniques, specifically Convolutional Neural Networks (CNNs), to address the complexities of arrhythmia classification. Leveraging multi-lead Electrocardiogram (ECG) data, our CNN model, comprising six layers with a residual block, demonstrates promising outcomes in identifying five distinct heartbeat types: Left Bundle Branch Block (LBBB), Right Bundle Branch Block (RBBB), Atrial Premature Contraction (APC), Premature Ventricular Contraction (PVC), and Normal Beat. Through rigorous experimentation, we highlight the transformative potential of our methodology in enhancing diagnostic accuracy for cardiovascular arrhythmias. Arrhythmia diagnosis remains a critical challenge in cardiovascular care, often relying on manual interpretation of ECG signals, which can be time-consuming and prone to subjectivity. To address these limitations, we propose a novel approach that leverages deep learning algorithms to automate arrhythmia classification. By employing advanced CNN architectures and multi-lead ECG data, our methodology offers a robust solution for precise and efficient arrhythmia detection. Through comprehensive evaluation, we demonstrate the effectiveness of our approach in facilitating more accurate clinical decision-making, thereby improving patient outcomes in managing cardiovascular arrhythmias.

An improved method to detect arrhythmia using ensemble learning-based model in multi lead electrocardiogram (ECG).

Arrhythmia is a life-threatening cardiac condition characterized by irregular heart rhythm. Early and accurate detection is crucial for effective treatment. However, single-lead electrocardiogram (ECG) methods have limited sensitivity and specificity. This study propose an improved ensemble learning approach for arrhythmia detection using multi-lead ECG data. Proposed method, based on a boosting algorithm, namely Fine Tuned Boosting (FTBO) model detects multiple arrhythmia classes. For the feature extraction, introduce a new technique that utilizes a sliding window with a window size of 5 R-peaks. This study compared it with other models, including bagging and stacking, and assessed the impact of parameter tuning. Rigorous experiments on the MIT-BIH arrhythmia database focused on Premature Ventricular Contraction (PVC), Atrial Premature Contraction (PAC), and Atrial Fibrillation (AF) have been performed. The results showed that the proposed method achieved high sensitivity, specificity, and accuracy for all three classes of arrhythmia. It accurately detected Atrial Fibrillation (AF) with 100% sensitivity and specificity. For Premature Ventricular Contraction (PVC) detection, it achieved 99% sensitivity and specificity in both leads. Similarly, for Atrial Premature Contraction (PAC) detection, proposed method achieved almost 96% sensitivity and specificity in both leads. The proposed method shows great potential for early arrhythmia detection using multi-lead ECG data.

Predicting acute myocardial infarction from haematological markers utilizing machine learning and explainable artificial intelligence

Myocardial infarction (MI) is the leading cause of human death globally. It occurs when a blockage in an artery prevents blood and oxygen from reaching the heart muscle, causing tissues in the heart muscle to die. This leads to a necessity to develop a method to diagnose MI’s early, preventing further complications such as irregular heart rhythm, heart failure or even cardiac arrest. This research aims to develop a more accurate machine learning (ML) model to help predict acute myocardial infarction (AMI) with a greater degree of accuracy without invasive procedures using additional explainable artificial intelligence (XAI) techniques which will help medical practitioners to better diagnose AMI more precisely. According to the results, the random forest classifier model gave the highest accuracy of 86%. XAI techniques were used to visualize the data and results, and determined white blood cell (WBC) count to be the most crucial feature in classification, followed by neutrophil (NEU) count, neutrophil-lymphocyte (NEU/LY) ratio, platelet width of distribution (PDW) values and basophil (BA) counts. The developed model can help medical practitioners make a more accurate early diagnosis of AMI using readily available hematological parameters, enabling practitioners to provide superior care to a diverse range of individuals.