Atrial Fibrillation Arrhythmias Research Articles

High-Density and Resolution Epicardial Mapping of the Atria: Translational Research with Clinical Impact

The electrical arrhythmogenic substrate underlying the most common cardiac arrhythmia atrial fibrillation (AF) may consist of conduction disorders, low-voltage areas, or fractionated potentials. High-density and resolution epicardial mapping (HDREM) approaches have been introduced to quantify and visualize electrophysiological properties of the atria. These approaches are essential for obtaining innovative insights into arrhythmogenic substrates and identifying novel targets for therapy. The aim of this review is to summarize and discuss the (1) contribution of HDREM studies to the knowledge on atrial arrhythmogenesis and (2) future applications of HDREM of atria in daily clinical practice.

Analysis of Thyroid Disorders in the Occurrence of Paroxysmal Atrial Fibrillation

Thyroid disorders can directly affect the cardiovascular system, especially atrial fibrillation arrhythmia. Atrial fibrillation can be triggered by hormonal changes in the human body system. This can be dangerous and can increase the risk of stroke in sufferers. For this reason, researchers conducted this study to be able to analyze the risk of atrial fibrillation due to thyroid disorders. This study used a retrospective analytical design, with a chi square test to determine the risk of increased atrial fibrillation due to thyroid disorders. This study was conducted at Budhi Asih Hospital with Holter monitoring patient samples. The results of this study showed that in patients with thyroid disorders, the results of Holter monitoring showed paroxysmal atrial fibrillation as much as 9.9%, with a p-value: 0.000; OR: 39.346. The conclusion is that there are significant results between thyroid disorders and the incidence of paroxysmal atrial fibrillation

Prediction of ECG fiducial parameters from PPG signals for the analysis of cardiovascular diseases: A novel Gaussian process regression-based approach

PurposeCardiologists use 12-lead electrocardiograph (ECG) and prolonged continuous Holter monitoring to detect abnormalities in heart rhythm, which could diagnose or hint at different cardiac diseases. However, the availability of a costly ECG machine and the expertise to interpret ECG is suboptimal in our country. The current state of the art proposes mitigating the cost and easy diagnosis by physicians with the use of low cost photoplethysmograph (PPG), which will deliver ECG information in terms of ECG fiducial parameters. MethodsThe MIMIC-III matched paired datasets of PPG/Lead-II ECG signals with the six classes (right bundle branch block (RBBB), hypertension (HYPER), arrhythmia-atrial fibrillation (ARYTH-ATRFAB), dilated cardiomyopathy (DCM), complete heart block (CHB) and NORMAL) associated cardiovascular diseases (CVDs) syndromes of 30 patients with 5 minutes duration each were taken from Physionet. The proposed work tested by predicting ECG fiducial parameters (P, Q, R, S and T) through Gaussian process regression(GPR)-based predictor by using the PPG fiducial parameters with a training and testing ratio of 70:30. The performance was analyzed by different performance evaluation parameters e.g., RMSE, rRMSE, etc. ResultsAlthough the shape of the ECG may differ in various ways from that of the normal ECG, the methodology can predict the ECG fiducials from PPG for a variety of CVD patients with a performance accuracy of 95–99% by evaluating through seven performance metrics. ConclusionThe ECG fiducial parameters can be used to analyze CVDs captured from a low-cost, handy, skin-compatible continuous and non-invasive optical recording device well known as a PPG sensor.

Perioperative outcomes of hypertrophic cardiomyopathy: An insight from the National Readmission Database

BackgroundData regarding hypertrophic obstructive cardiomyopathy (HOCM) patients undergoing noncardiac surgery is lacking. We sought to examine the perioperative outcomes of HOCM patients undergoing noncardiac surgery using a national database. MethodsWe used the National readmission database from 2016 to 2019. We identified HOCM, heart undergoing noncardiac surgery using ICD 10 codes. We examined hospital outcomes as well as 90 days readmission outcomes. ResultsWe identified 16,098 HOCM patients and 21,895,699 non-HOCM patients undergoing noncardiac surgery. The HOCM group had more comorbidities at baseline. After adjustment for major clinical predictors, the HOCM group experienced more in-hospital death, odds ratio (OR) 1.33 (1.216–1.47), P < 0.001, acute myocardial infarction (AMI), OR 1.18 (1.077–1.292), P < 0.001, acute heart failure odds ratio OR 1.3 to (1.220–1.431), P < 0.001, 90 days readmission OR 1.237 (1.069–1.432), P < 0.01, cardiogenic shock OR 2.094 (1.855–2.363), P < 0.001. Cardiac arrhythmia was the most common cause of readmission, out of the arrhythmias atrial fibrillation was the most prevalent. Acute heart failure was the most common complication of readmission. There was no difference in major adverse cardiovascular events (MACE), and AMI between both groups and readmission. ConclusionHOCM patients undergoing noncardiac surgery may be at increased risk of in-hospital and readmission events. Acute heart failure was the most common complication during index admission, while cardiac arrhythmias were the most common complication during readmission. More research is needed to address this patient population further.

Atrial fibrillation detection on reconstructed photoplethysmography signals collected from a smartwatch using a denoising autoencoder

Photoplethysmography (PPG) signals collected by wearables have been shown to be effective in accurate detection of atrial fibrillation (AF), provided that the data are devoid of motion and noise artifacts (MNA). Many studies have been previously conducted to detect AF arrhythmia using PPG data; however, the subjects were mostly in clinics or controlled settings with data collection lasting several minutes to at most several hours with minimal MNA. Our study, Pulsewatch, differs from previous AF studies in that PPG data from smartwatches prescribed to stroke survivors were continuously collected for two weeks in real-life conditions, which invariably included a significant amount of MNA. Our aim is to provide a framework for a novel use of a denoising autoencoder to reconstruct motion-artifact-removed PPG signals so that we can improve the AF detection performance and to increase the amount of analyzable data.We used more than 30,000 25-sec PPG segments from 129 subjects randomly selected from Pulsewatch and Stanford University’s datasets. The training and testing datasets from these two databases came from smartwatches from different vendors with varying sampling frequencies and time duration of recordings in diverse and realistic settings. In this study, the highly corrupted PPG data were automatically detected and discarded, but those segments contaminated with low-to-moderate motion and noise artifacts (MNA) were subjected to a convolutional denoising autoencoder (CDA). To reconstruct the artifact-removed PPG segments, we proposed to employ two distinct CDA models for AF and non-AF data groups initially classified as AF or non-AF. Using the proposed approach, we significantly improved the performance of detecting occult AF. We achieved classification accuracy, sensitivity, and specificity of 91.02%, 91.54%, and 90.85%, respectively, for out-of-sample test data from both databases. By sanitizing data from low-to-moderate MNA, we were able to increase the usable data coverage by 21%.

Classification of Electrocardiogram Signals using Deep Learning based on Genetic Algorithm Feature Extraction.

Arrhythmias using electrocardiogram (ECG) signal is important in medical and computer research due to the timely diagnosis of dangerous cardiac conditions. The current study used the ECG to classify cardiac signals into normal heartbeats, congestive heart failure, ventricular arrhythmias, atrial fibrillation arrhythmias, atrial flutter, malignant ventricular arrhythmias, and premature atrial fibrillation. A deep learning algorithm was used to identify and diagnose cardiac arrhythmias. We proposed a new ECG signal classification method to increase signal classification sensitivity. We smoothed the ECG signal with noise removal filters. A discrete wavelet transform based on an arrhythmic database was applied to extract ECG features. Feature vectors were obtained based on wavelet decomposition energy properties and calculated values of PQRS morphological features. We used the genetic algorithm to reduce the feature vector and determine the input layer weights of the artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). Proposed methods for classifying ECG signals were in different classes of rhythm to diagnose heart rhythm diseases. Training data was with 80% of the data set and test data was with 20% for the whole data set. The learning accuracy for the results of training and test data in the ANN classifier was calculated as 99.9% and 88.92% and in ANFIS as 99.8% and 88.83% respectively. Based on these results, good accuracy was observed.

Beta-blockers in cardiac arrhythmias-Clinical pharmacologist's point of view.

β-blockers is a vast group of antiarrhythmic drugs which differ in their pharmacokinetic and chemical properties. Some of them block β-adrenergic receptors selectively while the others work non-selectively. Consequently, they reduce the influence of the sympathetic nervous system on the heart, acting negatively inotropic, chronotropic, bathmotropic and dromotropic. Although they have been present in medicine since the beginning of the 1960s, they still play a crucial role in the treatment of cardiac arrhythmias. They are also first-line group of drugs used to control the ventricular rate in patients with the most common arrhythmia-atrial fibrillation. Previous reports indicate that infection with SARS-CoV-2 virus may constitute an additional risk factor for arrhythmia. Due to the aging of the population in developed countries and the increase in the number of patients with cardiac burden, the number of people suffering from cardiac arrhythmias will increase in the upcoming years. As a result the role of above-mentioned beta-blockers will remain significant. Particularly noteworthy is propranolol-the oldest beta adrenergic antagonist, which in recent years has found additional applications due to its unique properties. In this article, we reviewed the accessible literature and summarized the current guidelines on the use of beta-blockers in the treatment of cardiac arrhythmias.

Effect of Magnesium Sulfate in Prevention of Postoperative Atrial Fibrillation Arrhythmia among Candidates of CABG Surgery with Abnormal P-wave Duration

Background: Cardiac arrhythmias, especially atrial fibrillation, are among the most common early complications of open-heart surgery. Hypomagnesemia is one of the clinical mechanisms associated with the pathogenesis of postoperative atrial fibrillation. This study aimed to evaluate the effect of magnesium sulfate on the prevention of atrial fibrillation in patients with abnormal P-wave duration who underwent coronary artery bypass graft (CABG)surgery. Methods: In this clinical trial, 150 patients participated who had undergone CABG surgery at Imam Khomeini hospital in Ahvaz in 2014. According to the inclusion criteria, the intervention and control groups were randomly matched. The intervention group, consisting of 57 patients, received 2.4 g of magnesium sulfate daily for three days and 75 patients of the control group received a placebo. Prevention of postoperative atrial fibrillation by magnesium sulfate was evaluated in patients and data were analyzed using SPSS software. Results: The two groups did not have a significant difference in terms of gender, diabetes, hyperlipidemia, and hypertension. Also, mean P-wave duration, length of intensive care unit stay, the total length of hospitalization, left ventricular ejection fraction (LVEF), distribution of creatinine, atrial fibrillation, and aortic clamp time in intervention and control groups were not statistically significant. Only, the total pump duration difference was reported to be statistically significant between groups. Conclusion: The results of our study showed that the administration of magnesium sulfate alone cannot be helpful in the prevention of atrial fibrillation among patients with long P-wave duration. Trial Registration: https://www.irct.ir/, Identifier: IRCT2015092814190N9

Automatic Atrial Fibrillation Arrhythmia Detection Using Univariate and Multivariate Data

Atrial fibrillation (AF) is still a major cause of disease morbidity and mortality, making its early diagnosis desirable and urging researchers to develop efficient methods devoted to automatic AF detection. Till now, the analysis of Holter-ECG recordings remains the gold-standard technique to screen AF. This is usually achieved by studying either RR interval time series analysis, P-wave detection or combinations of both morphological characteristics. After extraction and selection of meaningful features, each of the AF detection methods might be conducted through univariate and multivariate data analysis. Many of these automatic techniques have been proposed over the last years. This work presents an overview of research studies of AF detection based on RR interval time series. The aim of this paper is to provide the scientific community and newcomers to the field of AF screening with a resource that presents introductory concepts, clinical features, and a literature review that describes the techniques that are mostly followed when RR interval time series are used for accurate detection of AF.

Feasibility of an automatic ultrasonographic image acquisition system associated with an artificial intelligence algorithm for real-time monitoring of cardiac motion during cardiac radio-ablation

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): This publication is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 954783. Background Stereotactic arrhythmia radio-ablation (STAR) is a promising strategy for the non-invasive treatment of ventricular arrhythmias (VA). The management of the cardio-respiratory motion of the target and the reduction of the uncertainties related to patients’ positioning are two of the main challenges that STAR has to overcome. A prototype of a system was developed that can automatically acquire and interpret echocardiographic images using an artificial intelligence (AI) algorithm to calculate cardiac displacement in real-time. Purpose To evaluate the feasibility of this automatic acquisition system in patients with a history of VA. Methods We conducted a single center, feasibility study enrolling consecutive patients with a history of VA. Echocardiographic images were automatically acquired from the parasternal and apical views with a dedicated probe. The system was designed to hold the probe fixed to the chest in the supine position during both free-breathing and short expiratory breath-hold sequences, to simulate STAR treatment. The primary endpoint was the percentage of patients reaching a score ≥2 in a multi-parametric assessment evaluating the quality of automatically acquired images in terms of: A - allowing a proper identification of the cardiac cycle phase using the AI algorithm, B - allowing a correct measurement of the heart displacement by the AI algorithm, C - ability to visually distinguish typical cardiac structures, and D - stability of the image throughout the respiration cycle. Moreover, we investigated the potential impact of clinical and demographic characteristics on achieving the primary outcome. Results From May to September 2021, we enrolled 24 patients (63±14 years, 21% females). All of them had a history of VA and 21 (88%) had an ICD. Eight patients (33%) had coronary artery disease, 12 (50%) had non-ischemic cardiomyopathy, and 3 had idiopathic VA. Parasternal as well as apical images were obtained from all patients except from one, in whom parasternal view could not be collected due to the patient’s inability to maintain the supine position. The primary outcome was achieved in 23 patients (96%) for the apical view, in 20 patients (87%) for the parasternal view, and in all patients in at least one of the two views. The images quality was maximal (i.e. score=4) in at least one of the two windows in 19 patients (79%). Atrial fibrillation arrhythmia was the only clinical characteristics associated with a poor score outcome in both imaging windows (apical p=0,022, parasternal p=0,014). Conclusion An automatic ultrasonographic image acquisition system associated with an AI algorithm is feasible for real-time monitoring of cardiac motion in patients with a history of VA. The possibility of real-time, non-invasive monitoring of cardiac position would lead to a significant improvement in the quality and safety of STAR treatment, particularly in case of treatment with heavy particles such as protons and carbon ions.

P289 EYESHOT OF CARDIAC AMYLOIDOSIS CASES PRESENTED TO A CARDIOLOGY UO IN CENTRAL ITALY IN 2021

Abstract Introduction ATTR–related cardiac amyloidosis, caused by the accumulation of insoluble fibrillar proteins derived from transthyretin misfolding, is an often underdiagnosed pathology, especially when neurological involvement is absent or minor. Transthoracic echocardiography represents the first fundamental step in the diagnostic process. Abstract During the year 2021, a total of 9 patients with an echocardiographic feature of hypertrophic heart disease suggestive of infiltrative pathology were referred to our Cardiology unit. Of these patients, 89% were male (8 out of 9), aged between 60 and 88 years (average age of 72 years). The clinic presentation was heart failure in 5 cases (55% of the total), arrhythmic storm in 1 case, history of amyloid polyneuropathy in 1 case and it was an occasional finding in 2 patients who required a cardiological evaluation for other reasons. The 12–lead ECG documented atrial fibrillation arrhythmia in 4 out of 9 cases (44%) and sinus rhythm in the remaining 5 cases (56%). Transthoracic echocardiography revealed left ventricular hypertrophy with granular sparkling aspect of the SIV in all patients, with indexes of global systolic function preserved in 4 out of 9 cases (44%), moderately reduced in 3 out of 9 cases (33%) and severely reduced in the remaining 2 cases (22%). Diastolic dysfunction was present in all patients, with a restrictive pattern in 4 of 9 cases (44%). Myocardial scintigraphy with DPD was performed in 7 patients, which confirmed the suspicion of aTTR amyloidosis in 6 cases (Perugini Score = 2), in the remaining 2 patients the diagnostic procedure was not performed due to severe comorbidities. At present, diagnostic confirmation of aTTR amyloidosis has been confirmed by biopsy in 2 patients; a patient‘s biopsy is currently under analysis; 1 patient is on Patisiran therapy. Conclusions Cardiac amyloidosis represents a frequent clinical condition especially in the elderly population. In the light of the most recent therapeutic strategies, a correct and early etiopathogenetic diagnosis is of fundamental importance.

Feasibility of an Automatic Ultrasonographic Image Acquisition System Associated With an Artificial Intelligence Algorithm for Real-Time Monitoring of Cardiac Motion During Cardiac Radio-Ablation.

BackgroundThe management of the cardio-respiratory motion of the target and the reduction of the uncertainties related to patient's positioning are two of the main challenges that stereotactic arrhythmia radio-ablation (STAR) has to overcome. A prototype of a system was developed that can automatically acquire and interpret echocardiographic images using an artificial intelligence (AI) algorithm to calculate cardiac displacement in real-time.MethodsWe conducted a single center study enrolling consecutive patients with a history of ventricular arrhythmias (VA) in order to evaluate the feasibility of this automatic acquisition system. Echocardiographic images were automatically acquired from the parasternal and apical views with a dedicated probe. The system was designed to hold the probe fixed to the chest in the supine position during both free-breathing and short expiratory breath-hold sequences, to simulate STAR treatment. The primary endpoint was the percentage of patients reaching a score ≥2 in a multi-parametric assessment evaluating the quality of automatically acquired images. Moreover, we investigated the potential impact of clinical and demographic characteristics on achieving the primary endpoint.ResultsWe enrolled 24 patients (63 ± 14 years, 21% females). All of them had a history of VA and 21 (88%) had an ICD. Eight patients (33%) had coronary artery disease, 12 (50%) had non-ischemic cardiomyopathy, and 3 had idiopathic VA. Parasternal, as well as apical images were obtained from all patients except from one, in whom parasternal view could not be collected due to the patient's inability to maintain the supine position. The primary endpoint was achieved in 23 patients (96%) for the apical view, in 20 patients (87%) for the parasternal view, and in all patients in at least one of the two views. The images' quality was maximal (i.e., score = 4) in at least one of the two windows in 19 patients (79%). Atrial fibrillation arrhythmia was the only clinical characteristics associated with a poor score outcome in both imaging windows (apical p = 0.022, parasternal p = 0.014).ConclusionsThese results provide the proof-of-concept for the feasibility of an automatic ultrasonographic image acquisition system associated with an AI algorithm for real-time monitoring of cardiac motion in patients with a history of VA.

Pathogenetic aspects of cardiovascular diseases: at the reception of a patient with atrial fibrillation. Pilot study data

A major problem in arrhythmology is supraventricular arrhythmias atrial fibrillation. Remodeling of the myocardium and the formation of fibrosis zones are the cause of cardiac arrhythmias. Violations of the extracellular framework of myocardiocytes with the formation of zones of sclerotically altered areas lead to violations of the pulse from the sinoatrial node. The inflammatory reaction, which results in fibrosis, has been repeatedly confirmed by the results of histological examination of lung vein and myocardial tissue samples in patients with rhythm disorders atrial fibrillation. The paper considers the possibility of determining in the blood of patients with atrial fibrillation a highly sensitive C-reactive protein, widely used in clinical practice, as an indicator of the activity of sluggish inflammation in the myocardium with simultaneous determination of the level of vitamin D (25-OH), known, among other things, for its anti-inflammatory effects. The data of the pilot study are presented.

A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia

Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper, we utilized the MIT-BIH AF Database (AFDB), which is composed of data from normal people and patients with AF and onset characteristics, and the AFPDB database (i.e., PAF Prediction Challenge Database), which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF), and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction, we regarded diagnosis and prediction as two classification problems, adopted the traditional support vector machine (SVM) algorithm, and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process, the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases, the sensitivity, specificity, and accuracy measures were 99.2% and 99.2%, 99.2% and 93.3%, and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases, respectively. Moreover, the sensitivity, specificity, and accuracy were 94.2%, 79.7%, and 87.0%, respectively, when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels.

Real-Time Heart Arrhythmia Detection Using Apache Spark Structured Streaming.

One of the major causes of death in the world is cardiac arrhythmias. In the field of healthcare, physicians use the patient's electrocardiogram (ECG) records to detect arrhythmias, which indicate the electrical activity of the patient's heart. The problem is that the symptoms do not always appear and the physician may be mistaken in the diagnosis. Therefore, patients need continuous monitoring through real-time ECG analysis to detect arrhythmias in a timely manner and prevent an eventual incident that threatens the patient's life. In this research, we used the Structured Streaming module built top on the open-source Apache Spark platform for the first time to implement a machine learning pipeline for real-time cardiac arrhythmias detection and evaluate the impact of using this new module on classification performance metrics and the rate of delay in arrhythmia detection. The ECG data collected from the MIT/BIH database for the detection of three class labels: normal beats, RBBB, and atrial fibrillation arrhythmias. We also developed three decision trees, random forest, and logistic regression multiclass classifiers for data classification where the random forest classifier showed better performance in classification than the other two classifiers. The results show previous results in performance metrics of the classification model and a significant decrease in pipeline runtime by using more class labels compared to previous studies.

Discrete cosine transform and multi class support vector machines for classification cardiac atrial arrhythmia and cardiac normal

The electrocardiogram signal is the most important analysis to detect cardiac arrhythmia. Machine learning classification is used as a first step to detect someone’s arrhythmia or normal heart. This paper discusses one method for detecting arrhythmia by using digital images of cardiac signals and R-R intervals. The process electrocardiogram digital image is divided into two, first the process of calculating the R-R intervals and second the process of extraction feature using Discrete Cosine Transform, followed by calculating the Euclidean Distance or Cityblock Distance with normal electrocardiogram signal reference. Euclidean Distance results or Cityblock Distance and R-R distance of electrocardiogram signals are then classified using Multiclass Support Vector Machine. The results of accuracy the classification four classes that are cardiac normal, atrial premature beat arrhythmia, atrial flutter arrhythmia, and atrial fibrillation arrhythmia, are 81.9%. The originality is used image to detect cardiac normal or cardiac arrhythmia by combined Discrete Cosine Transform, Euclidean distance or City block distance and Multiclass Support Vector Machine.

Sociodemographic, Clinico-Laboratory Characteristics with Short Outcome among Acute Heart Failure Patients Admitted for First Time in a Tertiary Care Hospital: a Real Life Experience

Acute heart failure is one of the common causes of morbidity &amp; mortality among the patients admitted in hospital. This prospective observational study was done to characterize sociodemographic, clinical &amp; laboratory characteristics with short clinical outcome. Total 110 patients of acute heart failure were included in this study where mean age was 53.88 (± 14.6) years. Majority of the patients were aged between 51-60 years 35 (32%). 79(72%) of them smoker and 12(11%) were alcoholic. Major comorbidities were hypertension 59 (53.63%), ischemic heart disease 42 (38.18%), DM 39(35.45%), chronic kidney disease 25(22.72%). Most common symptoms at presentation were shortness of breath110(100%) along with palpitation 96(87.27%), orthopnea 85(77.27%), paroxysmal nocturnal dyspnea 59(53.63%), ankle edema 71(64.54%), raised JVP 75(68.18%), bilateral basal crepitation on auscultation 80(72.72%), anemia 19 (17.27%) &amp; oliguria 23(20.90%) where mean duration of symptoms was 11 ± 2 days. Lab investigations revealed decreased Hemoglobin (10.71 ± 1.49) g/dl, proteinuria in 31 (28.18%), ketone body 5 (4.5%), raised creatinine in 23(20.93%), hyponatremia 38 (34.54%), hypernatremia 6 (5.45%), hypokalemia 6 (5.45%), hyperkalemia 3 (2.72%). Regarding cardiac events 37(33.63%) encounter myocardial infarction, cardiogenic shock 29(26.36%), Cardiac arrhythmia 27 (24.5%), cardiac arrest 6 (5.45%), non-cardiac events like stroke 6(5.45%), hyperosmolar hyperglycemic state 2(1.8%), diabetic ketoacidosis 5(5.45%), acute kidney injury 23(20.90%). However, among cardiac arrhythmia atrial fibrillation found 13(11.81%) followed by multiple ventricular ectopic 12 (10.90%), ventricular tachycardia 7 (6.36%), Complete heart block 6 (5.45%), variable heart block 4 (3.63%), long QT 2(1.8%). Regarding short clinical outcome 52(47.27%) were improved, 40 (36.36%) had persistent symptoms &amp; rest 18 (16.36%) expired. Most common causes of those deceased person were cardiogenic shock 9(50%) followed by cardiac arrest 6 (6.33%). In conclusion we can say clinical characteristics are more or less similar but those who have reduced ejection fraction with comorbidities are very prone to develop cardiac arrhythmia causing sudden cardiac death in heart failure.&#x0D; J Dhaka Medical College, Vol. 28, No.2, October, 2019, Page 192-198

Management of Anticoagulant Therapy Monitored by an Implantable Device With Telecardiology in Patients With Acute Coronary Syndrome Associated With de Novo Atrial Fibrillation Arrhythmia

Management of Anticoagulant Therapy Monitored by an Implantable Device With Telecardiology in Patients With Acute Coronary Syndrome Associated With de Novo Atrial Fibrillation Arrhythmia

Enhancement in the Detection of Atrial Fibrillation Arrhythmia For Health Monitoring System

In day to day life, continuous monitoring of medical data is essential for many individuals including patients with severe health risks and elderly people. In this paper, one such system is developed for continuous monitoring of Atrial Fibrillation abnormality. Electrocardiographic (ECG) signal monitoring plays a vital role in the management of patients with atrial fibrillation (AF). Atrial Fibrillation (AF) is a type of abnormality in heart, it causes during the AF electrical discharges in the atrium are rapid and results in an abnormal heartbeat. In this paper, ECG signals taken from the MIT-BIH arrhythmia database. After the signal is acquired, the hybrid filtering technique is used to remove the artifacts. Naturally, the ECG signal gets distorted by different types of artifacts which must be removed from the signal otherwise it will convey incorrect information regarding the patient’s heart condition. Efficient LMS and Normalized LMS adaptive filters are computationally used for cancellation of noise. Analyzing functions of the filtered signal is Peak Signal to Noise Ratio (PSNR), Mean Square Normalized Error Performance (MSE), Maximum Square Error (MAXERR), the ratio of Squared Norms (L2RAT). The continuous health statistics will be given to individuals and caretaker in the remote location so that they can take necessary action to prevent from health issues. The paper will provide primitive solutions in the field of telemedicine using continuous health monitoring and medical data analysis of a particular individual.In future work, the ECG sensor will acquire the ECG signal and the acquired signal processed through the classification algorithm for classifying the signal into different categories. The biosensor is a combination of the biological element with the physiochemical transducer to produce an electronic signal which can be further converted, processed and transmitted for data analytics, processing, validation, visualization, interpretation, and data logging.

Comparison Effect of Tranexamic Acid (TA) and Tranexamic Acid Combined with Vitamin C (TXC) on Drainage Volume and Atrial Fibrillation Arrhythmia in Patients Undergoing Cardiac Bypass Surgery: Randomized Clinical Trial.

BackgroundTranexamic acid and vitamin C are potent antifibrinolytic and oxidative stress agents that reduce blood loss and transfusion blood in cardiopulmonary bypass (CPB).ObjectivesThe aim of this study was to evaluate the efficacy of tranexamic acid (TA) and tranexamic acid combined with vitamin C (TXC) on drainage volume (blood loss) and atrial fibrillation (AF) in patients undergoing cardiac bypass surgery in Gorgan, Shafa hospital, Iran.MethodsThis study is a double-blind randomized clinical trial. A sample size of 120 candidates of cardiac bypass surgery were included in this prospective study. Patients were randomly assigned to treatments in two groups. In both groups, 50 mg/kg tranexamic acid was administered intravenously directly before sternotomy: group A (N = 58) patients received tranexamic acid (TA) only and group B (N = 62) tranexamic acid with vitamin C (TXC) half an hour before surgery and 2 g vitamin C with 100 mL 0.9% saline were injected. Subsequently, during 4 days after surgery, 1000 mg of vitamin C and 100 cc 0.9% saline was infused every day. Intraoperative and postoperative blood loss (volume of blood in the drain) and atrial fibrillation complications were recorded for 24 hours after the operation.ResultsThe patients who received vitamin C had less bleeding during operation and in the early hours post-operation. Patients in (TA) group had mean drainage of 34.41 milliliter more than patients in (TXC) group (P < 0.001). Chi-square test showed that arrhythmia (AF) condition was the same in the two groups during 14 times of study (four times during operation and ten times up to 24 hours after the operation), and AF arrhythmia in the two groups was less than 5%.ConclusionsIn this study tranexamic acid with vitamin C have a positive effect on the amount of drainage (blood loss) and there was no significant difference in the incidence of AF between two groups.