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Background & Aims: Amiodarone is a purposive medicine useful in restoring sinus rhythm (SR) after cardiac surgery. The aim of the study was to evaluate the effect of prophylactic intraoperative single-dose intravenous amiodarone and to convert atrial fibrillation (AF) into normal sinus rhythm (NSR) in the patients undergoing valve replacement surgery. Materials & Methods: In this prospective and interventional study, 180 patients of ASA III (American Society of Anesthesiologist) classification between the ages of 18-60 years, posted for Rheumatic Mitral Valve Replacement were allocated randomly to two equal groups (Group-1 and Group-2). Their hemodynamics parameters, Pre and post pulse rate, ECG findings and incidence of AF and VT/VF and ICU, Hospital stay were noted. Data was analysed with SPSS v26 using chi-square test. A two-tailed P value of 0.05 or less was regarded as statistically significant. Results: In our study, we found that there was suggestive significance between both groups as regard to mean pulse rate changes after 5 and 10 minutes of induction (P value >0.05). At the end of surgery, there was developing atrial fibrillation in few patients and sinus rhythm in more (P value >0.05). Postoperative arrhythmias in the first 24 hrs, AF was seen in 9(10%) patients in the Group 1 compare to Group 2 49 (54.4%) (P

Photoplethysmography (PPG) signal is potentially suitable in atrial fibrillation (AF) detection for its convenience in use and similarity in physiological origin to electrocardiogram (ECG). There are a few preceding studies that have shown the possibility of using the peak-to-peak interval of the PPG signal (PPIp) in AF detection. However, as a generalized model, the accuracy of an AF detector should be pursued on the one hand; on the other hand, its generalizability should be paid attention to in view of the individual differences in PPG manifestation of even the same arrhythmia and the existence of sub-types. Moreover, a binary classifier for atrial fibrillation and normal sinus rhythm is not convincing enough for the similarity between AF and ectopic beats. In this study, we project the atrial fibrillation detection as a multiple-class classification and try to propose a training pipeline that is advantageous both to the accuracy and generalizability of the classifier by designing and determining the configurable options of the pipeline, in terms of input format, deep learning model (with hyperparameter optimization), and scheme of transfer learning. With a rigorous comparison of the possible combinations of the configurable components in the pipeline, we confirmed that first-order difference of heartbeat sequence as the input format, a 2-layer CNN-1-layer Transformer hybridR model as the learning model and the whole model fine-tuning as the implementing scheme of transfer learning is the best combination for the pipeline (F1 value: 0.80, overall accuracy: 0.87)R.

Introduction: Annual healthcare expenditures associated with atrial fibrillation (AF) in the United States (US) continue to grow as more symptomatic patients present to emergency departments (ED). Predictors of spontaneous conversion to normal sinus rhythm (ScNSR) remain poorly understood, as well as the timeline of ScNSR remains unclear. We sought to 1) to assess the association of key demographics, anthropometric, and clinical factors to ScNSR and 2) to evaluate the timeline of ScNSR, and 3) determine clinical predictors of ScNSR. Methods: This single center, retrospective cohort study analyzed patients aged ≥18 years with symptomatic AF as diagnosed and evaluated through the ED of a rural tertiary care center in West Virginia from September 2015 to December 2018. Results: Our cohort consisted of 375 AF patients (mean age 65 years, 54% male). A total of 177 patients attained ScNSR either in the ED or after hospital admission with a mean conversion time of 14.7 h (±12). Onset of symptoms

This study proposes a wavelet leaders method with multiscale entropy measures to analyze multiscale complexities in electrocardiogram (ECG) signals to characterize arrhythmia (ARR), congestive heart failure (CHF), and normal sinus rhythm (NSR). The statistical results show evidence of multiscale fractal and multiscale entropy in all health conditions. In addition, ECG signals under NSR conditions display the largest complexity compared to ARR and CHF. Further, statistical tests confirm the presence of differences in terms of multifractals between health conditions in ECG signals. Finally, multiscale entropy increases with scale. The results from statistical analyses indicate that healthy ECG signals are more complex than abnormal ones. Hence, abnormality alters and reduces complexity in arrhythmia and congestive heart failure signals.

Congestive heart failure (CHF) and arrhythmia (ARR) are common heart diseases that affect a growing population of patients worldwide. In this work, we employ multi-scale analysis (MSA) to estimate generalized Hurst exponent (GHE) from electrocardiogram (ECG) records under CHF, ARR, and normal sinus rhythm (NSR). As a result, fractal correlations in short and long fluctuations of CHF, ARR, and NSR are measured. Then, a set of six statistical tests are applied to GHE estimates to check how they are different at each time scale between two different ECG conditions. Particularly, the goal is verify if two different ECG conditions can be statistically differentiated by short or by long fluctuations. The battery of statistical tests includes Kolmogorov–Smirnov, Kruskal–Wallis, Wilcoxon rank sum, Student t-test, Ansari–Bradley, and F-test. The results from MSA show evidence that CHF, ARR, and NSR all exhibit multi-fractal properties. Besides, the results from statistical tests revealed that long fluctuations statistically differentiate CHF and ARR, ARR and NSR, and CHF and NSR. Therefore, long fluctuations account most for the characterization of CHF, ARR, and NSR. Our findings are helpful to better understand the mechanics of heart disease and normal heart beats, and also promising to eventually designing computer-aided diagnosis systems for CHF and ARR classification.

Heart disease is a major health issue, and accurate diagnosis of irregular heartbeats and heart failure is crucial. Current diagnostic processes can be time-consuming, requiring significant effort from clinicians. An effective classifier, ADCGNet: Attention-based Dual Channel Gabor Network is proposed to address this challenge by accurately classifying anomalies. ADCGNet involves pre-processing every ECG beat into two-dimensional images using Analytical Morlet transform and then applying thirty-two Gabor filters and Sobel edge detection to enhance features. ADCGNet comprises three blocks, with the first block using dual channels to extract essential features in the images efficiently. The second block includes a multi-head attention mechanism to focus on relevant features, and the third block uses a SoftMax activation function to perform classification tasks. Extensive experiments with public datasets from PhysioNet, and comparison with several state-of-the-art classifiers indicate ADCGNet is superior. Specifically, ADCGNet achieved an accuracy of 99.17%, 98.98% in precision, a recall of 98.87%, an F1-score of 98.82% and AUC, 98.75% with optimal hyperparameters. Further, a GRAD-CAM visualization of activated areas on the test samples gives graphical insight into the performance of ADCGNet. The proposed ADCGNet classifier has promising potential for enhancing the diagnosis of heart disease, and we believe it will be of much interest to the medical community.

Background and aimsIt is difficult to document atrial fibrillation (AF) on ECG in patients with non-persistent atrial fibrillation (non-PeAF). There is limited understanding of whether an AI prediction algorithm could predict the occurrence of non-PeAF from the information of normal sinus rhythm (SR) of a 12-lead ECG. This study aimed to derive a precise predictive AI model for screening non-PeAF using SR ECG within 4 weeks.MethodsThis retrospective cohort study included patients aged 18 to 99 with SR ECG on 12-lead standard ECG (10 seconds) in Ewha Womans University Medical Center for 3 years. Data were preprocessed into three window periods (which are defined with the duration from SR to non-PeAF detection) – 1 week, 2 weeks, and 4 weeks from the AF detection prospectively. For experiments, we adopted a Residual Neural Network model based on 1D-CNN proposed in a previous study. We used 7,595 SR ECGs (extracted from 215,875 ECGs) with window periods of 1 week, 2 weeks, and 4 weeks for analysis.ResultsThe prediction algorithm showed an AUC of 0.862 and an F1-score of 0.84 in the 1:4 matched group of a 1-week window period. For the 1:4 matched group of a 2-week window period, it showed an AUC of 0.864 and an F1-score of 0.85. Finally, for the 1:4 matched group of a 4-week window period, it showed an AUC of 0.842 and an F1-score of 0.83.ConclusionThe AI prediction algorithm showed the possibility of risk stratification for early detection of non-PeAF. Moreover, this study showed that a short window period is also sufficient to detect non-PeAF.

Photoplethysmography (PPG) signal is potentially suitable in atrial fibrillation (AF) detection for its convenience in use and similarity in physiological origin to electrocardiogram (ECG). There are a few preceding studies that have shown the possibility of using the peak-to-peak interval of the PPG signal (PPIp) in AF detection. However, as a generalized model, the accuracy of an AF detector should be pursued on the one hand; on the other hand, its generalizability should be paid attention to in view of the individual differences in PPG manifestation of even the same arrhythmia and the existence of sub-types. Moreover, a binary classifier for atrial fibrillation and normal sinus rhythm is not convincing enough for the similarity between AF and ectopic beats. In this study, we project the atrial fibrillation detection as a multiple-class classification and try to propose a training pipeline that is advantageous both to the accuracy and generalizability of the classifier by designing and determining the configurable options of the pipeline, in terms of input format, deep learning model (with hyperparameter optimization), and scheme of transfer learning. With a rigorous comparison of the possible combinations of the configurable components in the pipeline, we confirmed that first-order difference of heartbeat sequence as the input format, a 2-layer CNN–1-layer Transformer hybridR model as the learning model and the whole model fine-tuning as the implementing scheme of transfer learning is the best combination for the pipeline (F1 value: 0.80, overall accuracy: 0.87)R. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AF) is a common cardiac arrhythmia in both human and equine populations. It is associated with adverse outcomes in humans and decreased athletic performance in both populations. Paroxysmal atrial fibrillation (PAF) presents with intermittent, self-terminating AF episodes, and is difficult to diagnose once sinus rhythm resumes. Objective: We aimed to detect PAF subjects from normal sinus rhythm equine electrocardiograms (ECGs) using the Symmetric Projection Attractor Reconstruction (SPAR) method to encapsulate the waveform morphology and variability as the basis of a machine learning classification. Methods: We obtained ECG signals from 139 active equine athletes (120 control, 19 with a PAF diagnosis). The SPAR method was applied to 9 short (20-second) ECG strips for each subject. An optimal SPAR feature set was determined by forward feature selection for input to a machine learning model ensemble of 3 different classifiers (k-nearest neighbors, linear support vector machine, and radial basis function kernel support vector machine). Imbalanced data were handled by upsampling the minority (PAF) class. A final subject classification was made by taking a majority vote over results from the 9 ECG strips. Results: Our final cross-validated classification for a subject gave an accuracy of 89.0%, sensitivity of 94.8%, specificity of 87.1%, and receiver operating characteristic area under the curve of 0.98, taking PAF as the positive class. Conclusion: The SPAR method and machine learning generated a final model with high sensitivity, suggesting that PAF can be discriminated from short equine ECG strips. This preliminary study indicated that SPAR analysis of human ECG could support patient monitoring, risk stratification, and clinical decision-making.

Electrocardiogram (ECG) is a vital diagnosis approach for the rapid explication and detection of various heart diseases, especially cardiac arrest, sinus rhythms, and heart failure. For this purpose, in this study, a different perspective based on downsampling one-dimensional-local binary pattern (1D-DS-LBP) and long short-term memory (LSTM) is presented for the categorization of Electrocardiogram (ECG) signals. A transformation method named 1D-DS-LBP has been presented for Electrocardiogram signals. The 1D-DS-LBP method processes the bigness smallness relationship between neighbors. According to the proposed method, by downsampling the signal, the histograms of 1D local binary patterns (1D-LBP) calculated from the obtained signal groups are collected and included as a reference to the long short-term memory structure. The long short-term memory structure has been applied to 1D-DS-LBP conversion applied ECG signals with both unidirectional and bidirectional. To test the proposed approach, ECG signals of three (3) different states of congestive heart failure (CHF), arrhythmia (ARR), and normal sinus rhythm (NSR) consisting of 972 signals were used. Signals were taken from the MIT-BIH and BIDMC databases. Experiments were carried out in various scenarios. We observed that the success rate of the proposed approach obtained very high classification accuracies compared to other studies in the literature. The obtained ECG diagnostic performance values varied between 96.80% and 99.79%. Based on this, this approach has a high potential to have a wide field of study in medical applications. [ABSTRACT FROM AUTHOR]

Postoperative atrial fibrillation is one of the most recognised complications of cardiac surgery. Although its exact pathophysiology remains unknown, evidence suggests that it is multifactorial and that it directly affects patient outcomes post cardiac surgery. It is associated with an increased risk of heart failure, renal failure, stroke and mortality. Pharmacological agents such as beta blockers and antiarrhythmic drugs are well established and extensively used for both the prevention and treatment of postoperative atrial fibrillation. This article will explore the pharmacological treatment of postoperative atrial fibrillation with specific reference to metoprolol and amiodarone, along with the pharmacokinetic and pharmacodynamic effects of both drugs. It will briefly discuss the evidence reviewed regarding the effectiveness of these drugs for postoperative atrial fibrillation and the recommendations from the European Society of Cardiology and the European Association for Cardio-Thoracic Surgery on how postoperative atrial fibrillation should be treated. [ABSTRACT FROM AUTHOR]

Electrocardiogram (ECG) is widely used as a diagnostic method to identify various heart diseases such as heart failure, cardiac and sinus rhythms. The ECG signal analyzes the electrical activity of the heart and shows waveforms that help detect heart irregularities. A new approach is suggested for automatic identification of congestive heart failure (CHF) and arrhythmia (ARR). In this study, long short-term memory neural networks (LSTM) were used to classify ECG signals by combining LSTM and angle transform (AT) methods. The AT uses the angular information of the neighbor signals on both sides of the target signal to classify ECG signals. The new signals obtained as a result of AT conversion vary between 0 and 359. Histogram of new signals determines the inputs to the LSTM method. LSTM uses histograms to distinguish between three different conditions: ARR, CHF, and normal sinus rhythm (NSR). The proposed approach is tested on ECG signals received from MIT-BIH and BIDMC databases. The experimental results have shown that the proposed method, AT + LSTM, has achieved high success rate of classifying ECG signals. The success rate in classifying CHF, ARR, and NSR ECG signals for 70–30% training sets was observed as 98.97%. Further experiments were conducted for varying training–testing dataset ratio to demonstrate the robustness of the proposed approach, and success rates are observed between 98.56 and 100%. Another experiment regarding different values of the dR and dL distance parameters of the AT model has shown that the performance of the proposed method increases while increasing the distance value. The success rates from increasing the distance value were obtained between 98.97 and 100%. To show the effect of segment lengths of ARR, NSR, and CHF signals on classification success, these signals were divided into segments of 10,000, 5000, and 1000 lengths. Achieved success rates ranged from 97.75 to 98.97%. Considering the results, high results were observed with the AT + LSTM approach, which is generally recommended in all scenarios. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) and congestive heart failure (CHF) are the most prevalent types of cardiovascular disorders as the leading cause of death due to delayed diagnosis. Early diagnosis of these cardiac conditions is possible by manually analyzing electrocardiogram (ECG) signals. However, manual diagnosis is complex, owing to the various characteristics of ECG signals. An accurate classification system for AF and CHF has the potential to save patient lives. Therefore, this study proposed an ECG signal classification system for AF and CHF using a one-dimensional convolutional neural network (1-D CNN) to provide a robust classification system performance. This study used ECG signal recording of AF, CHF, and NSR, which can be accessed on the Physionet website. A total of 5600 ECG signal segments were obtained from 56 subjects, divided into train sets from 42 subjects (N = 4200 ECG segments), and test sets from 14 subjects (N = 1400). We applied for leave-one-out cross-validation in training to select the best model. The proposed 1-D CNN algorithm successfully classified raw data of ECG signals into normal sinus rhythm (NSR), AF, and CHF by providing the highest classification accuracy of 99.643%, f1-score, recall, and precision of 0.996, respectively, with an AUC score of 0.999. The results showed that the proposed method extracted the ECG signal information directly without needing several preprocessing steps and feature extraction methods that potentially reduce the information contained in the ECG signals. Furthermore, the proposed method outperformed previous studies in classifying AF, CHF, and NSR. Therefore, this approach can be considered as an adjunct for medical personnel to diagnose AF, CHF, and NSR. [ABSTRACT FROM AUTHOR]

The incidence of congestive heart failure (CHF) is approximately 10 per 1000 for Americans over the age of 65 years. The dynamics of CHF are highly complex, nonlinear, and temporal dynamics. Based on these characteristics, we extracted multimodal features from congestive heart failure (CHF) and normal sinus rhythm (NSR) signals. We performed the synthetic minority over-sampling technique (SMOTE) to increase the number of CHF subjects to balance our train data. The classification between these subjects with original data and SMOTE data was performed using machine learning classifiers such as classification and regression tree (CART), support vector machine linear (SVM-L), Naïve Bayes, neural network, and ensemble classifiers such as random forest (RF), XG boost, averaged neural network (AVNNET). With the original data, the highest performance was obtained using SVM-L with accuracy (94.28%), sensitivity (84.61%), specificity (100%), p-value (0.0002), AUC (0.9605) with 95% CI: 0.9006-1.00. By applying the SMOET, the highest performance was obtained with SVM-L with accuracy (97.14%), sensitivity (92.30%), specificity (100%), p-value (7.99e-06), AUC (0.9650) with 95% CI: 0.8945–1.00. The results reveal that proposed approach with SMOTE improved the detection performance which can be very effective and computationally efficient tool for automatic detection of congestive heart failure patients. [ABSTRACT FROM AUTHOR]

Intelligent and automated systems for diagnosing heart disease play a key role in treatment of heart disease and hence mitigating the possibility of heart disease, heart failure or sudden death. Thus, a Computer-Aided Design CAD system can provide a doctors with a clue about the category of patient heart disease, which might be Normal Sinus Rhythm, Abnormal Arrhythmia (ARR), and Congestive Heart Failure (CHF) electrocardiogram (ECG) signal. In this work a novel Slantlet transform (SLT) statistical features have been extracted and selected for 900 ECG segments taken from MIT-BIH ARR Database equally from three classes mentioned above for heart dieses classification through ECG signals. Based on the superiority of SLT in time localization as compared to the traditional wavelet transform, 12 out of 14 statistical features have been successfully passed the ANOVA test with P-value of 10−3. Then after, the relevant features are provided to three well-known classifiers (Support Vector Machine [SVM], K-nearest neighbor, and Naive Bayes). The performance tests show that Attaining 99.254% classification average AUC it can be achieved using SLT transform based features along with SVM classifier, which is a set of related supervised machine learning algorithm used for regression and classification with high generalization ability. It performs classification on two group problems. SVM classifier determines the best hyperplane which distinguishes between each positive and negative training sample.

In this study, we ranked the Multimodal Features extracted from Congestive Heart Failure (CHF) and Normal Sinus Rhythm (NSR) subjects. We categorized the ranked features into 1 to 5 categories based on Empirical Receiver Operating Characteristics (EROC) values. Instead of using all multimodal features, we use high ranking features for detection of CHF and normal subjects. We employed powerful machine learning techniques such as Decision Tree (DT), Naïve Bayes (NB), SVM Gaussian, SVM RBF and SVM Polynomial. The performance was measured in terms of Sensitivity, Specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), Accuracy, False Positive Rate (FPR), and area under the Receiver Operating characteristic Curve (AUC). The highest detection performance in terms of accuracy and AUC was obtained with all multimodal features using SVM Gaussian with Sensitivity (93.06%), Specificity (81.82%), Accuracy (88.79%) and AUC (0.95). Using the top five ranked features, the highest performance was obtained with SVM Gaussian yields accuracy (84.48%), AUC (0.86); top nine ranked features using Decision Tree and Naïve Bayes got accuracy (84.48%), AUC (0.88); last thirteen ranked features using SVM polynomial obtained accuracy (80.17%), AUC (0.84). The findings indicate that proposed approach with feature ranking can be very useful for automatic detection of congestive heart failure patients and can be very helpful for further decision making by the clinicians and physicians in order to decrease the mortality rate.

BackgroundThe most-commonly used multi-slice Simpson's method employed with routine two-dimensional segmented cine images makes it difficult to evaluate left ventricular (LV) volume and function due to endocardial border blurring and beat-to-beat variation during atrial fibrillation (AF) status.ObjectivesTo assess the feasibility of compressed sensing real-time (CSRT) cine imaging combined with an area-length method for quantification of LV systolic function in normal sinus rhythm (NSR) and AF.MethodsThe CSRT cine sequence and routine segmented balanced Steady-State-Free-Precession cine sequence were performed in 71 patients with NSR (n = 36) or AF (n = 35). Image quality and edge sharpness for both sequences were assessed. The LV functional measurements in patients with NSR included end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), ejection fraction (EF), cardiac output (CO), cardiac index (CI), and LV mass (LVM); all were assessed using segmented cine with Simpson's rule in short axis (SegSA_Simpson, as a reference standard) and area-length (AL) method in the two chamber (Seg2CH_AL) or four chamber (Seg4CH_AL) and CSRT cine with AL method in the two chamber (CSRT2CH_AL) or four chamber (CSRT4CH_AL). Finally, the mean, maximum, and minimum values of each LV functional parameter [EDV/ESV/SV/EF/CO/CI/LVM/heart rate (HR)] from 4~5 consecutive heartbeats were measured using CSRT2CH_AL in patients with AF.ResultsIn patients with NSR, measurements of EDV (p > 0.05), ESV (p > 0.05), SV (p > 0.05), EF (p > 0.05), and LVM (p > 0.05) assessed with CSRT2CH_AL did not differ significantly from those obtained with SegSA_Simpson. In patients with AF, CSRT image quality score (p < 0.001) and edge sharpness (p < 0.001) both were significantly higher than those obtained from segmented cine. The CSRT2CH_AL provided significantly different results among mean, maximum, and minimum values of each LV parameter from 4~5 consecutive heartbeats (all p < 0.001) with strong inter- and intra-observer agreement in AF.ConclusionsThe CSRT cine sequence combined with two chamber area-length analysis accurately assessed LV systolic function in NSR. This approach is expected to permit the assessment of multiple parameters in consecutive heartbeats with good inter- and intra-observer reproducibility for beat-to-beat analysis of LV function in AF.

Atrial fibrillation (AF) is associated with significant risk of heart failure and consequent death. Its episodic appearance, the wide variety of arrhythmias exhibiting irregular AF-like RR intervals and noises accompanying the ECG acquisition, impede the reliable AF detection. Therefore, the Computing in Cardiology Challenge 2017 organizers encourage the development of methods for classification of short, single-lead ECG as AF, normal sinus rhythm (NSR), other rhythm (OR), or noisy signal (NOISE). This study presents a set of 118 time and frequency domain feature including descriptors of the RR and PP intervals; QRS and P-wave amplitudes; ECG behavior within the TQ intervals, deviation of the TQ and PQRST segments from their first principle component analysis vector; dominant frequency; regularity index, width and area of the power spectrum estimated for the ECG signal with eliminated QRS complexes. Three classification techniques have been applied over the 118 ECG features – linear discriminant analysis (LDA), classification tree (CT) and neural network (NN) approach. The scores over a test subset are: (i) FNSR = 0.81; FAF = 0.61; FOR = 0.53, F1 = 0.65 for CT, which is the most simple model; (ii) FNSR = 0.82; FAF = 0.62; FOR = 0.53, F1 = 0.66 for LDA, which is the model with the most reproducible accuracy results; (iii) FNSR = 0.86; FAF = 0.74; FOR = 0.57, F1 = 0.72 for NN, which is the most accurate model.

Objective: To determine the value of left atrial appendage mean diastolic emptying velocity at which there is restoration of normal sinus rhythm from atrial fibrillation either by electrical cardioversion or pharmacological cardioversion, development of clot in left atrial appendage or left atrium and long-term (at one month) sinus rhythm maintenance and to measure the value of left atrial appendage late diastolic emptying velocity after restoration of sinus rhythm at one month duration. Study Design: Cross-sectional prospective study. Place and Duration of Study: Department of Cardiac Electrophysiology, AFIC/NIHD Rawalpindi, from Oct 2020 to Apr 2021. Methodology: Transthoracic echocardiography was used to gather standard echocardiographic data from individuals with atrial fibrillation, and trans-esophageal echocardiography was used to determine mean diastolic emptying velocities in the left atrial appendage. All patients fulfilling inclusion criteria underwent direct current cardioversion and the late diastolic emptying velocity of left atrial appendage was measured after restoring sinus rhythm at one month of follow up by again performing trans-esophageal echocardiography. The diastolic emptying velocities of successful cardioverted patients and those still in atrial fibrillation (unsuccessful direct current cardioversion were compared and analyzed. Results: Out of 61 patients, normal sinus rhythm was restored by successful cardioversion in 56 (91.8%) patients and all same (91.8%) patients had maintained normal sinus rhythm at 1 month interval checked on follow up. Patients who had their normal sinus rhythm restored by cardioversion had greater left atrial appendage mean diastolic emptying velocities, with a mean of 24.043.06 cm/s, than patients who had failed cardioversions, with a mean of 19.601. Out of 51cm/s (p=0.002). Patients who were remained in normal sinus rhythm at 1 month had lower left atrial volumes indexed to body surface area with a mean value of 27.023. Of 33 ml/m2 (p=0.001) and greater left atrial appendage late diastolic emptying velocities with a mean value of 45.394. Out of 70cm/s (p=0.001) compared to the patients who were still in atrial fibrillation. Conclusion: As a marker of mechanical remodeling, left atrial appendage diastolic emptying velocity can anticipate successful cardioversion, short-term and long-term sinus rhythm maintenance after electrical cardioversion in AF patients and even presence of clot in left atrial appendage when having lower values. [ABSTRACT FROM AUTHOR]

Intelligent and automated systems for diagnosing heart disease play a key role in treatment of heart disease and hence mitigating the possibility of heart disease, heart failure or sudden death. Thus, a Computer-Aided Design CAD system can provide a doctors with a clue about the category of patient heart disease, which might be Normal Sinus Rhythm, Abnormal Arrhythmia (ARR), and Congestive Heart Failure (CHF) electrocardiogram (ECG) signal. In this work a novel Slantlet transform (SLT) statistical features have been extracted and selected for 900 ECG segments taken from MIT-BIH ARR Database equally from three classes mentioned above for heart dieses classification through ECG signals. Based on the superiority of SLT in time localization as compared to the traditional wavelet transform, 12 out of 14 statistical features have been successfully passed the ANOVA test with P-value of 10-3. Then after, the relevant features are provided to three well-known classifiers (Support Vector Machine [SVM], K-nearest neighbor, and Naive Bayes). The performance tests show that Attaining 99.254% classification average AUC it can be achieved using SLT transform based features along with SVM classifier, which is a set of related supervised machine learning algorithm used for regression and classification with high generalization ability. It performs classification on two group problems. SVM classifier determines the best hyperplane which distinguishes between each positive and negative training sample. [ABSTRACT FROM AUTHOR]

Effective monitoring of heart patients according to heart signals can save a huge amount of life. In the last decade, the classification and prediction of heart diseases according to ECG signals has gained great importance for patients and doctors. In this paper, the deep learning architecture with high accuracy and popularity has been proposed in recent years for the classification of Normal Sinus Rhythm, (NSR) Abnormal Arrhythmia (ARR) and Congestive Heart Failure (CHF) ECG signals. The proposed architecture is based on Hybrid Alexnet-SVM (Support Vector Machine). 96 Arrhythmia, 30 CHF, 36 NSR signals are available in a total of 192 ECG signals. In order to demonstrate the classification performance of deep learning architectures, ARR, CHR and NSR signals are firstly classified by SVM, KNN algorithm, achieving 68.75% and 65.63% accuracy. The signals are then classified in their raw form with LSTM (Long Short Time Memory) with 90.67% accuracy. By obtaining the spectrograms of the signals, Hybrid Alexnet-SVM algorithm is applied to the images and 96.77% accuracy is obtained. The results show that with the proposed deep learning architecture, it classifies ECG signals with higher accuracy than conventional machine learning classifiers. [ABSTRACT FROM AUTHOR]

Congestive heart failure (CHF) is characterized by the heart's inability to pump blood adequately throughout the body without increased intracardiac pressure. Diverse approaches are used to treat CHF. These approaches, which include physical examination, echocardiography, and laboratory testing, require a high degree of competence to interpret findings and make diagnoses. Moreover, existing methods do not account for the relationships between variables and thus provide limited performance. Electrocardiogram (ECG), as a non-invasive test, may be used for CHF early diagnosis, which would require further examination to be referred. A previous study revealed a significant correlation between heart failure (HF) and ECG features. However, the method was only performed on small, balanced data; then, the features must be derived from trial and error. The current paper proposes deep-learning techniques—recurrent neural networks (RNNs) with long short-term memory (LSTM) architectures—to create a diagnostic algorithm that achieves high accuracy with limited information and automated feature extraction. The ECG signals used in this study were obtained from the public PhysioNet databases. We fine-tuned the hyperparameters of 24 LSTM models to obtain the best model. Moreover, ECG signal segmentation was compared among the first five and fifteen minutes as features. Out of the 24 LSTM models, the model with the first fifteen minutes of ECG signals (model 1) obtained the highest accuracy, sensitivity, specificity, precision, and F1-score (99.86%, 99.85%, 99.85%, 99.87%, and 99.86%, respectively). The first fifteen minutes of ECG signals performed well because the LSTM model learned an increasing number of features. In conclusion, the proposed LSTM model could give a clinician a preliminary CHF diagnosis for further medical attention. Deep learning can be a useful predictive method for increasing the number of identified CHF patients.

Atrial fibrillation (AF) is associated with significant risk of heart failure and consequent death. Its episodic appearance, the wide variety of arrhythmias exhibiting irregular AF-like RR intervals and noises accompanying the ECG acquisition, impede the reliable AF detection. Therefore, the Computing in Cardiology Challenge 2017 organizers encourage the development of methods for classification of short, single-lead ECG as AF, normal sinus rhythm (NSR), other rhythm (OR), or noisy signal (NOISE). This study presents a set of 118 time and frequency domain feature including descriptors of the RR and PP intervals; QRS and P-wave amplitudes; ECG behavior within the TQ intervals, deviation of the TQ and PQRST segments from their first principle component analysis vector; dominant frequency; regularity index, width and area of the power spectrum estimated for the ECG signal with eliminated QRS complexes. Three classification techniques have been applied over the 118 ECG features – linear discriminant analysis (LDA), classification tree (CT) and neural network (NN) approach. The scores over a test subset are: (i) FNSR = 0.81; FAF = 0.61; FOR = 0.53, F1 = 0.65 for CT, which is the most simple model; (ii) FNSR = 0.82; FAF = 0.62; FOR = 0.53, F1 = 0.66 for LDA, which is the model with the most reproducible accuracy results; (iii) FNSR = 0.86; FAF = 0.74; FOR = 0.57, F1 = 0.72 for NN, which is the most accurate model. [ABSTRACT FROM AUTHOR]

Prevalence of atrial fibrillation (AF) is high in heart failure patients supported by a continuous flow left ventricular assist device (CF-LVAD); however, the long term effects remain unclear. In this study, a computational model simulating effects of AF on cardiac function and blood flow for heart failure and CF-LVAD support is presented. The computational model describes left and right heart, systemic and pulmonary circulations and cerebral circulation, and utilises patient-derived RR interval series for normal sinus rhythm (SR). Moreover, AF was simulated using patient-derived unimodal and bimodal distributed RR interval series and patient specific left ventricular systolic functions. The cardiovascular system model simulated clinically-observed haemodynamic outcomes under CF-LVAD support during AF, such as reduced right ventricular ejection fraction and elevated systolic pulmonary arterial pressure. Moreover, relatively high aortic peak pressures and middle arterial peak flow rates during AF with bimodal RR interval distribution, reduced to similar levels as during normal SR and AF with unimodal RR interval distribution under CF-LVAD support. The simulation results suggest that factors such as distribution of RR intervals and systolic left ventricular function may influence haemodynamic outcome of CF-LVAD support during AF. [ABSTRACT FROM AUTHOR]

Heart disease is a major health issue, and accurate diagnosis of irregular heartbeats and heart failure is crucial. Current diagnostic processes can be time-consuming, requiring significant effort from clinicians. An effective classifier, ADCGNet: Attention-based Dual Channel Gabor Network is proposed to address this challenge by accurately classifying anomalies. ADCGNet involves pre-processing every ECG beat into two-dimensional images using Analytical Morlet transform and then applying thirty-two Gabor filters and Sobel edge detection to enhance features. ADCGNet comprises three blocks, with the first block using dual channels to extract essential features in the images efficiently. The second block includes a multi-head attention mechanism to focus on relevant features, and the third block uses a SoftMax activation function to perform classification tasks. Extensive experiments with public datasets from PhysioNet, and comparison with several state-of-the-art classifiers indicate ADCGNet is superior. Specifically, ADCGNet achieved an accuracy of 99.17%, 98.98% in precision, a recall of 98.87%, an F1-score of 98.82% and AUC, 98.75% with optimal hyperparameters. Further, a GRAD-CAM visualization of activated areas on the test samples gives graphical insight into the performance of ADCGNet. The proposed ADCGNet classifier has promising potential for enhancing the diagnosis of heart disease, and we believe it will be of much interest to the medical community. [ABSTRACT FROM AUTHOR]

Introduction: Annual healthcare expenditures associated with atrial fibrillation (AF) in the United States (US) continue to grow as more symptomatic patients present to emergency departments (ED). Predictors of spontaneous conversion to normal sinus rhythm (ScNSR) remain poorly understood, as well as the timeline of ScNSR remains unclear. We sought to 1) to assess the association of key demographics, anthropometric, and clinical factors to ScNSR and 2) to evaluate the timeline of ScNSR, and 3) determine clinical predictors of ScNSR., Methods: This single center, retrospective cohort study analyzed patients aged ≥18 years with symptomatic AF as diagnosed and evaluated through the ED of a rural tertiary care center in West Virginia from September 2015 to December 2018., Results: Our cohort consisted of 375 AF patients (mean age 65 years, 54% male). A total of 177 patients attained ScNSR either in the ED or after hospital admission with a mean conversion time of 14.7 h (±12). Onset of symptoms <24 hrs has strong positive association to ScNSR 3.97 (95% CI: 2.24-7.05; p < 0.0001). Male gender 0.55 (95% CI: 0.35-0.85; p = 0.007) and hypertension 0.48 (95% CI: 0.31-0.76; p = 0.002), showed a strong negative association to ScNSR. Of the patients that converted spontaneously (177), the majority, 136 (76.8%) achieved ScNSR within 24 h of ED triage without use of electrical or chemical cardioversion., Conclusion: Most patients with AF in the ED converted spontaneously to sinus rhythm within the first 24 h which underscores the importance of earlier watchful waiting over interventions to achieve normal sinus rhythm (NSR)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Indian Heart Rhythm Society. Published by Elsevier B.V. All rights reserved.)

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Prevalence of atrial fibrillation (AF) is high in heart failure patients supported by a continuous flow left ventricular assist device (CF-LVAD); however, the long term effects remain unclear. In this study, a computational model simulating effects of AF on cardiac function and blood flow for heart failure and CF-LVAD support is presented. The computational model describes left and right heart, systemic and pulmonary circulations and cerebral circulation, and utilises patient-derived RR interval series for normal sinus rhythm (SR). Moreover, AF was simulated using patient-derived unimodal and bimodal distributed RR interval series and patient specific left ventricular systolic functions. The cardiovascular system model simulated clinically-observed haemodynamic outcomes under CF-LVAD support during AF, such as reduced right ventricular ejection fraction and elevated systolic pulmonary arterial pressure. Moreover, relatively high aortic peak pressures and middle arterial peak flow rates during AF with bimodal RR interval distribution, reduced to similar levels as during normal SR and AF with unimodal RR interval distribution under CF-LVAD support. The simulation results suggest that factors such as distribution of RR intervals and systolic left ventricular function may influence haemodynamic outcome of CF-LVAD support during AF.

Background and Aims: It is difficult to document atrial fibrillation (AF) on ECG in patients with non-persistent atrial fibrillation (non-PeAF). There is limited understanding of whether an AI prediction algorithm could predict the occurrence of non-PeAF from the information of normal sinus rhythm (SR) of a 12-lead ECG. This study aimed to derive a precise predictive AI model for screening non-PeAF using SR ECG within 4 weeks., Methods: This retrospective cohort study included patients aged 18 to 99 with SR ECG on 12-lead standard ECG (10 seconds) in Ewha Womans University Medical Center for 3 years. Data were preprocessed into three window periods (which are defined with the duration from SR to non-PeAF detection) - 1 week, 2 weeks, and 4 weeks from the AF detection prospectively. For experiments, we adopted a Residual Neural Network model based on 1D-CNN proposed in a previous study. We used 7,595 SR ECGs (extracted from 215,875 ECGs) with window periods of 1 week, 2 weeks, and 4 weeks for analysis., Results: The prediction algorithm showed an AUC of 0.862 and an F1-score of 0.84 in the 1:4 matched group of a 1-week window period. For the 1:4 matched group of a 2-week window period, it showed an AUC of 0.864 and an F1-score of 0.85. Finally, for the 1:4 matched group of a 4-week window period, it showed an AUC of 0.842 and an F1-score of 0.83., Conclusion: The AI prediction algorithm showed the possibility of risk stratification for early detection of non-PeAF. Moreover, this study showed that a short window period is also sufficient to detect non-PeAF., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2023 Kim, Joo, Jeon, Kim, Shin, Im and Park.)

Background: We sought to investigate and compare the safety and efficacy of two commonly used antiarrhythmic drugs, Dofetilide (DF) and Sotalol (SL), during inpatient drug initiation in patients with symptomatic atrial fibrillation (AF). Methods: We performed a single center retrospective study of consecutive patients, admitted for initiation of either DF or SL, for AF between 2012 and 2015. Rates of successful cardioversion, QT interval prolongation, adverse events and drug discontinuations were calculated and compared. A two-tailed p value less than 0.05 was considered statistically significant. Results: Of 378 patients, 298 (78.8%) received DF and 80 (21.2%) SL, mean age was 64 ± 11 years, 90% were Caucasians and 66% were males. Among the patients who remained in AF upon admission (DF: 215/298 (72%) vs. SL: 48/80 (60%)), no significant differences were noted in pharmacological cardioversion rates (DF: 125/215(58%) vs. SL: 30/48 (62.5%); p = 0.58). Baseline QTc was similar between the groups, with higher dose dependent QTc prolongation with DF (472.25± 31.3 vs. 458± 27.03; p = 0.008). There were no significant differences in the rates of adverse events such as bradycardia (7.4% vs. 11.3%; p = 0.26), Torsades de pointes (1.3% vs. 1.2%; p = 1.00), and drug discontinuation (9.0% vs. 5.0%; p = 0.47) between the two groups. Conclusions: In our large, single center experience, we found that the use of Dofetilide resulted in significantly higher QTc without differences in the rates of successful cardioversion, adverse events, and drug intolerance when compared to Sotalol in AF patients. [ABSTRACT FROM AUTHOR]