Electrical Activity Of Heart Research Articles

Why Portable Handheld Cardiac Ultrasound Devices Ought to Incorporate 3-lead Electrocardiographs: A Mini-narrative Review

Introduction: Transthoracic echocardiography is a non-invasive diagnostic test used to assess the structure and function of the heart in real-time. An examination of the heart's electrical activity is done non-invasively through electrocardiography (ECG). The ECG is primarily used during an echocardiography procedure to assess the heart's rhythm and to synchronise the cardiac cycles with echocardiographic image acquisition; this enables the echocardiographer to acquire images at specific phases of the cardiac cycle. A handheld echocardiograph is not connected to the electrocardiogram, which is a clinical limitation for the point-of-care ultrasound procedure. The objectives of this review are to highlight the reasons why 3-lead electrocardiography should be incorporated into pocket-sized handheld echocardiographs to support clinicians in conducting and reporting complete cardiac investigations. Methods: An in-depth search was made on the PubMed database to find experimental and review studies that assessed the importance of 3-lead ECG in the acquisition of hand-held echocardiographic data. Four related studies were selected for this review. Results: The study found that integrating cardiac point-of-care ultrasound into primary healthcare practices such as family medicine or general practitioners’ practices and training clinicians to accurately perform the procedure can expedite cardiovascular disease diagnosis and treatment in critical and non-critical scenarios. Secondly, recent hand-held cardiac ultrasound examination outcomes are as accurate and precise as standard transthoracic echocardiography and cardiac magnetic resonance imaging. Conclusion: Furthermore, medical device companies should consider integrating and producing new hand-held echocardiographs with configured 3-lead electrocardiographs that are artificial intelligence-enhanced to assist clinicians in precise point-of-care cardiac assessments.

The Effects of Phenanthrene on Electrical Activity in Ventricular Cardiomyocytes of Atlantic Cod (<i>Gadus morhua</i>)

The production of oil on the Arctic shelf and its transport along the Northern Sea Route increase risks of pollution of the ecosystems in the Arctic seas with oil and oil products. Today, polyaromatic hydrocarbons are known as the most toxic oil components, and phenanthrene is predominant in terms of its concentration in oil and physiological effects. Phenanthrene affects the electrical activity of fish heart, but its effects are species-specific. At the same time, the effects of phenanthrene on cardiac function in Arctic fishes, including economically important commercial species, are studied not enough. This study examines the effects of phenanthrene on electrical activity and ionic currents in ventricular myocardium of Atlantic cod (Gadus morhua). The major ionic currents in cod myocardium were IKr, IK1, INa and ICa. Phenanthrene (1 μM) did not affect the duration of action potentials (APs) recorded in isolated cod ventricular cardiomyocytes using patch clamp method. Meanwhile, phenanthrene suppressed rapid delayed rectifier current IKr by 61.33 ± 3.94%, decreasing the repolarization reserve of the myocardium. Phenanthrene did not affect nor the level of resting membrane potential, not background inward rectifier current IK1. Also, application of phenanthrene decreased AP upstroke velocity in cod myocytes, which was due to the suppression of fast sodium current INa. Finally, phenanthrene slightly reduced the amplitude of calcium current ICa and accelerated its inactivation, which overall led to the decrease in ICa charge transfer. Thus, the effects of phenanthrene on cod myocardium at cellular level can be described as potentially proarrhythmic, which makes the populations of cod in Arctic seas vulnerable to pollution of the aquatic environment by oil components after oil spills due to technological disasters.

Abstract 4134619: A Novel Missense Mutation in TNNT2 Gene in a Lebanese Pedigree With Ebstein Anomaly And Wolf-Parkinson-White Syndrome: A Case Report

Background: Ebstein anomaly (EA) is a rare congenital heart defect occurring in 1.2 to 5 in 100,000 live births, characterized by a downward displacement of the tricuspid valve, thin-walled right ventricle, and tricuspid valve regurgitation. It can present variably from asymptomatic cases to severe symptoms like arrhythmias and right-sided heart failure. EA is often associated with other anomalies such as interatrial communication and mitral valve prolapse. The condition can lead to accessory atrioventricular pathways, frequently resulting in Wolff-Parkinson-White syndrome (WPW), which involves abnormal heart electrical activity and increases the risk of sudden cardiac death. While the genetic basis of EA is not fully understood, it appears to involve multiple genes like FLNA and NKX2-5, MYH6, MYH7 suggesting a complex polygenic inheritance pattern. TNNT2 is known to be associated with Cardiomyopathy but has not been previously associated with EA and WPW. Case: In this report, we present findings from a lebanese family with EA, comprising 2 affected individuals. Whole exome sequencing in the affected individuals identified a pathogenic variant in the TNNT2 gene at coding strain position 260 (a missense mutation: C to T), resulting in the substitution of Proline with Leucine at position 87 in affected individuals (Figure 1). No variants were detected in any other candidate gene examined. Individuals I:1 and II:2 were found to be normal, with no EA findings on echocardiography. Methods: Whole exome sequencing in the patients involved collecting blood samples after obtaining consent. These samples were sent to Centogene lab, where genomic DNA is enzymatically fragmented. The regions of interest are then enriched using DNA capture probes, facilitating detailed genetic analysis. Results: The whole exome sequencing identified a heterozygous missense mutation in both the father and his daughter. This nonsynonymous variant is located on chromosome 1 (GRCh37) in the TNNT2 gene region. The specific variant, NM_001276345.1:c.260C>T p.(Pro87Leu), was found in both individuals. Conclusion: In conclusion, the genetic basis of EA is rather complex and remains poorly understood. It is established that mutations in the TNNT2 gene are linked to several cardiomyopathies, none of them overlap with the described phenotype of our patients. In this report, we therefore confirm the potential role of TNNT2 gene mutation in the genetic basis of familial EA with WPW.

Pathological Alterations in Heart Mitochondria in a Rat Model of Isoprenaline-Induced Myocardial Injury and Their Correction with Water-Soluble Taxifolin.

Mitochondrial damage and associated oxidative stress are considered to be major contributory factors in cardiac pathology. One of the most potent naturally occurring antioxidants is taxifolin, especially in its water-soluble form. Herein, the effect of a 14-day course of the peroral application of the water-soluble taxifolin (aqTAX, 15 mg/kg of body weight) on the progression of ultrastructural and functional disorders in mitochondria and the heart's electrical activity in a rat model of myocardial injury induced with isoprenaline (ISO, 150 mg/kg/day for two consecutive days, subcut) was studied. The delayed ISO-induced myocardial damage was accompanied by an increase in the duration of RR and QT intervals, and long-term application of aqTAX partially restored the disturbed intraventricular conduction. It was shown that the injections of ISO lead to profound ultrastructural alterations of myofibrils and mitochondria in cardiomyocytes in the left ventricle myocardium, including the impairment of the ordered arrangement of mitochondria between myofibrils as well as a decrease in the size and the number of these organelles per unit area. In addition, a reduction in the protein level of the subunits of the respiratory chain complexes I-V and the activity of the antioxidant enzymes catalase, glutathione peroxidase, and Mn-SOD in mitochondria was observed. The application of aqTAX caused an increase in the efficiency of oxidation phosphorylation and a partial restoration of the morphometric parameters of mitochondria in the heart tissue of animals with the experimental pathology. These beneficial effects of aqTAX are associated with the inhibition of lipid peroxidation and the normalization of the enzymatic activities of glutathione peroxidase and Mn-SOD in rat cardiac mitochondria, which may reduce the oxidative damage to the organelles. Taken together, these data allow one to consider this compound as a promising cardioprotector in the complex therapy of heart failure.

A multi-adaptive neuro-fuzzy inference system with variable thresholds for heartbeat classification

Various heart disorders are non-invasively diagnosed using electrocardiograms (ECGs). An ECG records a variety of waveforms, including P, QRS, and T waves, which represent the electrical activity of the human heart. Cardiovascular diseases are diagnosed by examining the length, form, and spacing of these waveforms. This research develops a multi-adaptive, neuro-fuzzy inference system (MANFIS), enhanced by a variable threshold approach, to enhance heartbeat classification accuracy. The MIT-BIH arrhythmia database was utilized, and seven features were extracted from each record. A subtractive clustering method was employed to prepare the inputs for the MANFIS, enabling heartbeat classification. By applying a variable threshold to the MANFIS outputs, classification accuracy was further enhanced. The proposed method, termed variable-threshold MANFIS, can separately detect normal sinus rhythm, left bundle branch block, right bundle branch block, premature ventricular contraction, atrial premature condition, and paced beat. This is achieved using six different ANFIS classifiers, each with its own threshold. The system was evaluated, achieving an accuracy of 98.33%, a sensitivity of 93.12%, a specificity of 99.66%, a precision of 98.33, and an F1-score of 95.44. A distinct feature of this machine-learning-based model is its controllable threshold, which delivers promising results across all training, testing, and validation datasets. The proposed diagnostic system is applicable in new automated medical instrumentation and serves as a valuable tool in cardiology.

Adaptive control of cardiac rhythms

Cardiac rhythms are related to heart electrical activity, being the essential aspect of the cardiovascular physiology. Usually, these rhythms are represented by electrocardiograms (ECGs) that are useful to detect cardiac pathologies. Essentially, the heart activity starts in the sinoatrial node (SA) node, the natural pacemaker, propagating to the atrioventricular node (AV), and finally reaching the His-Purkinje complex (HP). This paper investigates the control of cardiac rhythms in order to induce normal rhythms from pathological responses. A mathematical model that presents close agreement with experimental measurements is employed to represent the heart functioning. The adopted model comprises a network of three nonlinear oscillators that represent each one of the cardiac nodes, connected by delayed couplings. The pathological behavior is induced by an external stimulus in the SA node. An adaptive controller is proposed acting in the SA node considering an strategy based on the signal obtained by the natural pacemaker and its regularization. The incorporation of adaptive compensation in a Lyapunov-based control scheme allows the compensation for the unknown dynamics. The controller ability to deal with interpatient variability is evaluated by assuming that the heart model is not available to the controller design, being used only in the simulator to assess the control performance. Results show that the adaptive term can reduce the control effort by around 3% while reducing the tracking error by 20%, when compared to the conventional feedback approach. Additionally, the controller can avoid abnormal rhythms, turning the ECG closer to the expected normal behavior and preventing critical cardiac responses. Therefore, this work demonstrates that an adaptive controller can be used to regulate the ECG signal without prior information about the system and disregarding inter- and intrapatient variability.

Design a Simple Portable Electrocardiograph Based on the IOT Concept

Abstract There are many cases of heart disease with death associated. The symptoms are not always clearly visible and require examination using an electrocardiogram (ECG) device to be detected. Recording human electrical activity can be done in various organs of the human body, one of which is in the heart. An ECG is a medical test to detect electrical activity signals produced by the heart by exchanging the signals on a monitor or graph on paper. This study aimed to design, build, test, and analyze a portable ECG device that is compact and easy to use. This portable electrocardiogram helps monitor human heart rate to check heart rate periodically. Tool design is done by making tools equipped with a monitoring system. The proposed system uses the AD8232 sensor to detect a person’s cardiac activity. Arduino Nano is a microcontroller connected to ESP32 with a WiFi feature. Heart activity monitors can be done anywhere by applying the Internet of Things concept, namely with the Ubidots application. A portable tool using Li-Ion batteries as a tool voltage source and BMS 3S as a charger, this tool is also equipped with a 3.2-inch TFT screen as a graph viewer of the results of monitoring heart activity. The results showed that the system could monitor the user’s heart for 5 minutes when sitting, walking, and running. The system created has been able to send heart signal data to be processed by Arduino Nano and ESP32 as a data transfer tool so that the data generated in the form of electrocardiogram graphs can be displayed on the Ubidots application. TFT LCDs can display electrocardiogram graphs generated by the electrical activity of the human heart and beats per minute values. The AD8232 sensor can measure beat per minute (bpm) values with an average increase in bpm values for walking conditions of 17% and running conditions of 19.17%. The AD8232 Sensor Performance can detect the user’s heart rate with a percentage of data difference between walking and running conditions of 2.88% with an accuracy rate of 97.12%.

Importance of Zinc Homeostasis for Normal Cardiac Rhythm

<p>Current arrhythmia therapies such as ion channel blockers, catheter ablation, or implantable cardioverter defibrillators have limitations and side effects, and given the proarrhythmic risk associated with conventional, ion channel-targeted anti-arrhythmic drug therapies, a new approach to arrhythmias may be warranted. Measuring and adjusting the level of particular ions that impact heart rhythm can be a simple and low-complication strategy for preventing or treating specific arrhythmias. In addition, new medicines targeting these ions may effectively treat arrhythmias. Numerous studies have shown that intracellular and extracellular zinc concentrations impact the heart's electrical activity. Zinc has been observed to affect cardiac rhythm through a range of mechanisms. These mechanisms encompass the modulation of sodium, calcium, and potassium ion channels, as well as the influence on beta-adrenergic receptors and the enzyme adenylate cyclase. </p><p> Moreover, zinc can either counteract or induce oxidative stress, hinder calmodulin or the enzyme Ca (2+)/calmodulin-dependent protein kinase II (CaMKII), regulate cellular ATP levels, affect the processes of aging and autophagy, influence calcium ryanodine receptors, and control cellular inflammation. Additionally, zinc has been implicated in the modulation of circadian rhythm. Additionally, zinc has been implicated in the modulation of circadian rhythm. In all the above cases, the effect of zinc largely depends on the normal or increased cellular level of zinc, which shows the importance of maintaining the serum and intracellular levels of zinc within the normal range.</p>

Fentanyl and Sudden Death-A Postmortem Perspective for Diagnosing and Predicting Risk.

Sudden, unexpected deaths are extremely difficult for families, especially when the victim is a child. Most sudden deaths occur due to cardiovascular issues, and a smaller number (approximately one-quarter) are attributed to other causes, such as epilepsy. The medicinal and non-medicinal use of the synthetic opioid fentanyl, which can cause breathing problems, is frequently involved in these deaths. It is also being found more often in autopsies of sudden death cases, and the number of overdose deaths from illicit drugs containing fentanyl is increasing. There are cases in which it is mixed with other drugs. A gene known as the KCNH2 gene or human ether-a-go-go-related gene (hERG), involved in the heart's electrical activity, can be related to abnormal heart rhythms. This gene, along with others, may play a role in sudden deaths related to fentanyl use. In response, we have examined the scientific literature on genetic variations in the KCNH2 gene that can cause sudden death, the impact of fentanyl on this process, and the potential benefits of genetic testing for the victims to offer genetic counseling for their family members.

3DECG-Net: ECG fusion network for multi-label cardiac arrhythmia detection

Cardiovascular diseases represent the leading global cause of death, typically diagnosed and addressed through electrocardiograms (ECG), which record the heart's electrical activity. In recent years, there has been a notable surge in ECG recordings, driven by the widespread use of wearable devices. However, the limited availability of medical experts to analyze these recordings underscores the necessity for automated ECG analysis using computer-aided methods. In this study, we introduced 3DECG-Net, a deep learning model designed to detect and classify seven distinct heart states through the analysis of data fusion from 12-lead ECG in a multi-label framework. Our model leverages a residual architecture with a multi-head attention mechanism, undergoing training within a five-fold cross-validation scheme. By transforming 12-lead ECG signals into 3D data with the help of Recurrent Plot technique, 3DECG-Net achieves a noteworthy micro F1-score of 80.3 %, surpassing the performance of other state-of-the-art deep learning models developed for this specific task. Also, we present an ECG preprocessing framework to generate compact, high-quality ECG signals for potential application in future studies within this domain. We conduct an explainable AI experiment using Local Interpretable Model-agnostic Explanations (LIME) to elucidate the significance of each lead in accurately diagnosing specific arrhythmias, ensuring the logical processing of ECG data by 3DECG-Net. The findings of this study suggest that the proposed model is trustworthy and has the potential to be used as an effective diagnostic toolset for identifying heart arrhythmias. Its effectiveness can improve the diagnostic process, facilitate early treatment, and enhance overall efficiency in medical settings.

Detection and classification of electrocardiography using hybrid deep learning models

ObjectiveElectrocardiography (ECGs) has been a vital tool for cardiovascular disease (CVD) diagnosis, which visually depicts the heart's electrical activity. To enhance automatic classification between normal and diseased ECG, it is essential to extract consistent and qualitative features. MethodsPrecision of ECG classification through a hybrid Deep Learning (DL) approach leverages both Convolutional Neural Network (CNN) architecture and Variational Autoencoder (VAE) techniques. By combining these methods, we aim to achieve more accurate and robust ECG interpretation. The method is trained and tested over the PTB-XL dataset, which contains 21,799 with 12-lead ECGs from 18,869 patients, each spanning 10 s. The classification evaluation of five super-classes and 23 sub-classes of CVD, with the proposed CNN-VAE model is compared. ResultsThe classification of various CVDs resulted in the highest accuracy of 98.51%, specificity of 98.12%, sensitivity of 97.9%, and F1-score of 97.95%. We have also achieved the minimum false positive and false negative rates of 2.07 and 1.87, respectively, during validation. The results are validated upon the annotations given by individual cardiologists, who assigned potentially multiple ECG statements to each record. ConclusionWhen compared to other deep learning methods, our suggested CNN-VAE model performs significantly better in the testing phase. This study proposes a new architecture of combining CNN-VAE for CVD classification from ECG data, this can help clinicians to identify the disease earlier and carry out further treatment. The CNN-VAE model can better characterize input signals due to its hybrid architecture.

AirECG: Contactless Electrocardiogram for Cardiac Disease Monitoring via mmWave Sensing and Cross-domain Diffusion Model

The electrocardiogram (ECG) has always served as a crucial biomedical examination for cardiac diseases monitoring and diagnosing. Typical ECG measurement requires attaching electrodes to the body, which is inconvenient for long-term monitoring. Recent wireless sensing maps wireless signals reflected from human chest into electrical activities of heart so as to reconstruct ECG contactlessly. While making great progress, we find existing works are effective only for healthy populations with normal ECG, but fall short when confronted with the most desired usage scenario: reconstructing ECG accurately for people with cardiac diseases such as atrial fibrillation, premature ventricular beat. To bridge the gap, we propose AirECG, which moves forward to reconstruct ECG for both healthy people and even cardiac patients with morbid ECG, i.e., irregular rhythm and anomalous ECG waveform, via contactless millimeter-wave sensing. To realize AirECG, we first custom-design a cross-domain diffusion model that can perform multiple iteration denoising inference, in contrast with the single-step generative models widely used in previous works. In this way, AirECG is able to identify and eliminate the distortion due to the unstable and irregular cardiac activities, so as to synthesize ECG even during abnormal beats. Furthermore, we enhance the determinacy of AirECG, i.e., to generate high-fidelity ECG, by designing a calibration guidance mechanism to combat the inherent randomness issue of the probabilistic diffusion model. Empirical evaluation demonstrates AirECG's ability of ECG synthesis with Pearson correlation coefficient (PCC) of 0.955 for normal beats. Especially for abnormal beats, the PCC still exhibits a strong correlation of 0.860, with 15.0%~21.1% improvement compared with state-of-the-art approaches.

Monitoring the health of patients using a nodal potential map

Purpose: The article presents the developed device and methods for monitoring the heart's condition using a nodal potential map. The proposal enables monitoring of the patient's health condition. Methods: Testing the heart's electrical activity in our device's case uses a "nodal potential map" BSPM (Body Surface Potential Mapping) technique. This is an extended version of the standard ECG measurement, which, instead of 12 leads, uses several dozen or even several hundred measurement electrodes placed in appropriate locations on the front and back of the chest. Results: The developed solution enables monitoring of heart function in a noninvasive way and is more precise than a standard ECG test. The collected measurements allowed us to determine the health and condition of the patient's heart—no anomalies were detected during its operation. Discussion: The next stage of work will be the development of machine learning algorithms trained on the measurements obtained from the vest. The results obtained so far indicate the potential of models classifying heart diseases.

Abstract Mo074: Overexpression of IPP2 Impairs Intracellular Calcium Handling, Leading to Arrhythmogenic Events

Cardiac arrhythmias, which are disruptions in the heart's electrical activities, critically affect the ventricular conduction system, impairing efficient blood circulation. These disturbances result in irregular heartbeats, contributing to patient mortality or causing harm to distant organs via embolic events. Given the severe implications, a profound understanding of the molecular mechanisms driving arrhythmia is crucial for devising specific treatments. This study unveils a pioneering link between the overexpression of IPP2 (Protein Phosphatase 1 Regulatory Inhibitor Subunit 2), driven by the Myh6 promoter (termed TgIPP2), and the onset of age-dependent cardiac arrhythmias. The electrical abnormalities observed in TgIPP2 overexpressing mice include a spectrum of dysfunctions such as axis deviations, elongated QRS and PR intervals, absence of P waves, AV dissociation, ventricular tachyarrhythmia, and heightened T waves. Investigating the pathophysiology behind these electrical anomalies, we subjected young adult mice to catecholamine challenges at low doses and conducted surface electrocardiogram (EKG) monitoring. TgIPP2 mice demonstrated a notable increase in arrhythmogenic episode frequency and duration following 0.2 mg/kg isoproterenol injection compared to their control counterparts. Through a protein phosphorylation analysis, we identified a key regulator of the ryanodine receptor, which modulates calcium release from the sarcoplasmic reticulum, correlating with the extended PR intervals and the delayed peaking of T waves. Therapeutic trials employing RyR blockers, specifically dantrolene, successfully normalized the erratic cardiac rhythms in aged TgIPP2 mice. However, beta blockers or L-type calcium channel blockers induced sudden cardiac death in this model. The study reveals that IPP2-induced aberrant phosphorylation disrupts calcium homeostasis, leading to arrhythmias. In conclusion, our research marks a significant advancement in understanding arrhythmogenic mechanisms, demonstrating that IPP2 overexpression directly triggers arrhythmia, particularly affecting the atrioventricular (AV) node, atria, and ventricles. The discovery that aberrant phosphorylation, especially of the RyR regulator, plays a pivotal role in initiating cardiac arrhythmias opens new pathways for targeted anti-arrhythmic therapies.

Study of some components of the influence and formation of blood flow in patients with "slow flow".

"Slow flow" is one very important concept in modern fundamental and clinical biomedicine. Slow coronary flow is indicative of delayed filling of the terminal coronary artery vessels, occurring in the absence of significant coronary stenosis. This group patient of patients exhibits a high incidence of disability and represents a significant financial and material burden for the state and the healthcare system in general. The primary objective of our study was to examine patients with slow coronary flow. We studied the standard parameters recommended by the international health care system (electrocardiography (by Medica QRS-12, Germany), through the electrical activity of a patient's heart by the electrical impulses (beating) of the heart; HC1(Germany); coagulogramma by Coatron M1 (Germany), troponin by AQT 90 (Germany); general blood test we used automatic human counting device HC1(Germany). Also, we investigate the original parameters (non-standard parameters, which we use in this pilot study) that we were first studied for this diagnosis and non-standard parameters. A general blood test showed that patients with slow flow had a higher blood leukocyte count than the control group, but the amount of hemoglobin was normal, the hematocrit was much higher than in the control group, and the platelet count was close to the lower limit of clinical standards.We obtained details of blood flow by coagulation situation, such as prothrombin time, prothrombin index, international normalized ratio, activated partial thromboplastin time, thrombin time, fibrinogen, and rheological properties such as index of erythrocyte aggregability, index of erythrocyte deformability, plasma viscosity, in silico blood rheological index. Blood flow can be considered as a superposition of vortices with similar frequencies and wave vectors that change after bifurcations or other obstacles in the vascular network. These factors together determine the conditions for structuring the flow of moving blood. Disruption or alteration of these factors results in slow flow. It has been found that the speed of blood flow in the coronary arteries depends on changes in the number and function of red blood cells. Slow flow is directly influenced by the aggregation and deformation of red blood cells, their number, and plasma viscosity. Consequently, the rheological status plays a crucial role in determining blood flow and its velocity.

Variant patterns of electrical activation and recovery in normal human hearts revealed by noninvasive electrocardiographic imaging.

Although electrical activity of the normal human heart is well characterized by the electrocardiogram, detailed insights into within-subject and between-subject variations of ventricular activation and recovery by noninvasive electroanatomic mapping are lacking. We characterized human epicardial activation and recovery within and between normal subjects using non-invasive electrocardiographic imaging (ECGI) as a basis to better understand pathology. Epicardial activation and recovery were assessed by ECGI in 22 normal subjects, 4 subjects with bundle branch block (BBB) and 4 with long-QT syndrome (LQTS). We compared characteristics between the ventricles [left ventricle (LV) and right ventricle (RV)], sexes, and age groups (<50/≥50years). Pearson's correlation coefficient (CC) was used for within-subject and between-subject comparisons. Age of normal subjects averaged 49 ± 14 years, 6/22 were male, and no structural/electrical heart disease was present. The average activation time was longer in LV than in RV, but not different by sex or age. Electrical recovery was similar for the ventricles, but started earlier and was on average shorter in males. Median CCs of between-subject comparisons of the ECG signals, activation, and recovery patterns were 0.61, 0.32, and 0.19, respectively. Within-subject beat-to-beat comparisons yielded higher CCs (0.98, 0.89, and 0.82, respectively). Activation and/or recovery patterns of patients with BBB or LQTS contrasted significantly with those found in the normal population. Activation and recovery patterns vary profoundly between normal subjects, but are stable individually beat to beat, with a male preponderance to shorter recovery. Individual characterization by ECGI at baseline serves as reference to better understand the emergence, progression, and treatment of electrical heart disease.

The magnetocardiogram.

The magnetic field produced by the heart's electrical activity is called the magnetocardiogram (MCG). The first 20 years of MCG research established most of the concepts, instrumentation, and computational algorithms in the field. Additional insights into fundamental mechanisms of biomagnetism were gained by studying isolated hearts or even isolated pieces of cardiac tissue. Much effort has gone into calculating the MCG using computer models, including solving the inverse problem of deducing the bioelectric sources from biomagnetic measurements. Recently, most magnetocardiographic research has focused on clinical applications, driven in part by new technologies to measure weak biomagnetic fields.

Electrical Activity of Heart During ECG For EMF And Non– EMF Trails.

Background: The ubiquitous use of mobile phones has raised concerns about potential health impacts, particularly regarding the effects of electromagnetic fields (EMF) on the human brain and heart. Heart rate variability (HRV) is a critical measure of heart health, influenced by brain function and susceptible to disruption by mobile phone radiation. Objective: This study aims to investigate the influence of mobile phone EMF on HRV, utilizing higher-order spectral analysis of ECG data to determine the functional relationship between the heart and brain under EMF exposure. Methods: Twenty healthy subjects from the University of Sindh, Jamshoro, participated in this experiment. The subjects, with an average age of 27 years, underwent ECG and EEG recording under varying conditions of EMF exposure. Each session involved comparisons of EMF effects over different durations and positions relative to the heart. Results: Statistically significant differences were observed in bicoherence values for chest positions V1 and V2, with non-EMF trials showing higher values compared to EMF trials. Coherence between ECG and EEG indicated significant changes in the 16-30 Hz frequency band during EMF exposure. Extended exposure of 40 minutes correlated with notable variations in HRV. Conclusion: The findings suggest that mobile phone EMF can significantly affect HRV parameters, particularly with prolonged exposure and when the EMF source is close to the heart. These results underscore the importance of developing guidelines for safe mobile phone usage.

Detection of Cardiovascular Diseases from ECG Images Using Machine Learning and Deep Learning Methods

Abstract: Cardiovascular diseases (CVDs) are the leading cause of death globally, emphasizing the importance of early prediction and classification for saving lives. Electrocardiogram (ECG) is a widely used, cost-effective, and noninvasive tool for measuring heart electrical activity, crucial for detecting CVDs. This study leverages deep learning techniques to predict four major cardiac abnormalities—abnormal heartbeat, myocardial infarction, history of myocardial infarction, and normal individuals—using a public dataset of ECG images from cardiac patients. The research explores transfer learning with pretrained deep neural networks such as SqueezeNet and AlexNet, as well as introduces a novel convolutional neural network (CNN) architecture for cardiac abnormality prediction. Additionally, pretrained models and the new CNN architecture are utilized as feature extraction tools for traditional machine learning algorithms including support vector machine, K-nearest neighbors, decision tree, random forest, and Naïve Bayes. Experimental results showcase the superiority of the proposed CNN model over existing works, achieving 98.23% accuracy, 98.22% recall, 98.31% precision, and 98.21% F1 score. Furthermore, when the proposed CNN model is utilized for feature extraction, it achieves the highest score of 99.79% using the Naïve Bayes algorithm.

Lifestyle Modification and Medical Treatment of Long QT Syndrome

Long QT Syndrome (LQTS) is a genetic or acquired cardiac disorder characterized by a prolonged QT interval on an electrocardiogram, predisposing individuals to life-threatening arrhythmias and sudden cardiac death. Management of LQTS involves both lifestyle modifications and pharmacological treatments to mitigate risks and enhance patient outcomes. Lifestyle interventions include avoiding strenuous physical activities, especially swimming, and managing emotional stress to prevent arrhythmic triggers. Dietary modifications, such as maintaining adequate levels of potassium and magnesium, are essential to stabilize the heart's electrical activity. On the pharmacological front, betablockers like propranolol and nadolol are the mainstay treatments, effectively reducing heart rate and adrenergic stimulation, thus minimizing arrhythmic events. Sparings where beta-blockers are insufficient, additional medications like mexiletine, and potassium-sparing diuretics are used to manage the condition. High-risk patients benefit from implantable cardioverter-defibrillators (ICDs), which provide immediate intervention during severe arrhythmic episodes. Combining lifestyle changes with pharmacological treatments offers a comprehensive approach to LQTS management, significantly lowering the incidence of arrhythmias and improving overall patient prognosis. Patient education and regular follow-up are crucial to ensure adherence to treatment plans and timely adjustments. Advances in genetic testing facilitate personalized medicine, allowing for tailored treatment strategies based on individual genetic profiles. This integrated approach enhances the effectiveness of LQTS management, providing a holistic solution to a complex cardiac condition.